Laser Spectrometry for Stable Isotope

Analysis of Water

Biomedical and Paleoclimatological Applications

Radboud van Trigt

Cover design Henk van TrigtPhotographs French Institue for Polar Research and Technology (IFRTP)

Maurine DietzRadboud van Trigt

A Considerable part of this work has been funded by the 'Stichting voor Fundamenteel Onderzoek derMaterie (FOM)', which is financially supported by the 'Nederlandse Organisatie voor WetenschappelijkOnderzoek (NWO)'.

RIJKSUNIVERSITEIT GRONINGEN

Laser Spectrometry for Stable Isotope Analysis of Water

Biomedical and Paleoclimatological Applications

Proefschrift

ter verkrijging van het doctoraat in deWiskunde en Natuurwetenschappenaan de Rijksuniversiteit Groningen

op gezag van deRector Magnificus, dr. D.F.J. Bosscher,

in het openbaar te verdedigen opvrijdag 11 januari 2002

om 16.00 uur

door

Radboud van Trigt

geboren op 1 juni 1972te Delft

Promotor: Prof. dr. H.A.J. MeijerReferenten: Dr. ir. E.R.Th. Kerstel

Dr. G.H. Visser

Beoordelingscommissie: Prof. dr. S. DaanProf. dr. S.J. JohnsenProf. dr. R.W.H. Morgenstern

ISBN: 90-77017-36-4

Table of contents

Table of contentsPreface 1

1. General introduction 31.1 Introduction 51.2 Isotopes 5

1.2.1 Definitions and notation 61.2.2 Fractionation 71.2.3 Relations between fractionation constants 101.2.4 Natural variations in isotope abundance ratios 101.2.5 Calibration materials and normalization 121.2.6 Accuracy and precision 141.2.7 Some applications 15

1.3 Techniques 151.3.1 Overview of methods for isotope ratio measurements on H2O samples until 1993 161.3.2 New developments since 1993 181.3.3 Spectroscopic techniques 20

1.4 Summary 22

2. Laser spectrometry: Technique and apparatus 232.1 Measurement principle 25

2.1.1 Infrared spectrum of water 252.1.2 Spectrometry 28

2.2 System description 312.2.1 Laser system 312.2.2 Scanning of the FCL 342.2.3 Optical lay-out and set-up 352.2.4 Operation 372.2.5 Measurement procedures 38

2.3 Calculations 392.3.1 Raw isotope ratio calculations 392.3.2 Pressure dependence correction 412.3.3 Filtering and calculation of means 442.3.4 Zero point adjustment 442.3.5 Calibration and normalization 45

2.4 Precision and accuracy of laser spectrometry 472.4.1 Measurements in the natural abundance range 472.4.2 Measurements in the enriched range as applied in the DLW method 53

2.5 Current status 642.5.1 Apparatus related 642.5.2 Fractionation related 652.5.3 Cell offsets 662.5.4 Memory effect 672.5.5 Interference with other species 72

2.6 Numerical simulations 732.6.1 Spectral overlap 732.6.2 Differential pressure effect 74

Table of contents

2.6.3 Realistic base-line and noise 752.6.4 Round up 75

2.7 Other attempts to improve precision and accuracy 762.8 Conclusions 78

Appendix: Specifications present set-up 79

3. Biomedical application 813.1 Introduction of the doubly labelled water method 83

3.1.1 History 833.1.2 Calculations 843.1.3 Validation studies 893.1.4 Analytical errors 903.1.5 Conversion from CO2 production to energy expenditure 913.1.6 Extension with another label: The triply labelled water method 913.1.7 Exploring the possibilities of the TLW method with 17O 92

3.2 Problems with standards, calibration 943.3 First test measurements: Seal blood and infant urine 963.4 Validation of the DLW method in Japanese Quail at different water fluxes 983.4.1 Abstract 98

3.4.2 Introduction 983.4.3 Methods 1003.4.4 Results 1033.4.5 Discussion 106

3.5 Conclusion 109

4. Glaciological application 1114.1 Introduction 113

4.1.1 Equilibrium and kinetic fractionation 1134.1.2 The Rayleigh process 1134.1.3 Meteoric water line 1154.1.4 Climate signal 1154.1.5 Paleotemperatures (climate) 1184.1.6 Deuterium excess 1204.1.7 Traditional ice core isotope measurements 123

4.2 Groningen ice core measurements 1244.2.1 Abstract 1244.2.2 Introduction 1254.2.3 Methods 1294.2.4 Results and discussion 1324.2.5 Conclusions 137

5. Certification of an unusual water sample 1395.1 Analysis of 17O content in Ontario Hydro heavy water 141

5.1.1 Introduction 1415.1.2 Constants and definition of symbols 1425.1.3 Procedure 1425.1.4 Concluding remarks 148

Table of contents

6. Future prospects 1496.1 Further development of LS 1516.2 Future possible applications 153

6.2.1 Stratospheric water 1536.2.2 Other molecules 154

7. References 157

Abbreviations 169

Summary 171Samenvatting 175

Dankwoord 179

List of publications 181

Curriculum vitae 183

Preface

1

PrefaceThis thesis is one of the results of a research project at the Centrum voor IsotopenOnderzoek

(CIO) of the University of Groningen. Dr. Harro Meijer started the project in 1993 and it was set going

with some preliminary measurements at the University of Nijmegen, in co-operation with dr. ir. Nico

Dam and prof. dr. Jörg Reuss. When a proposal was granted by the stichting Fundamenteel Onderzoek

der Materie (FOM), a color center laser and other equipment was purchased. Then dr. ir. Erik Kerstel

joined the project and Jaap van der Ploeg, an electro-technicien, was put on the work as well. In 1997 I

joined the team. Erik received a prestigeous grant as a Research Fellow from the Koninklijke

Nederlandse Academie van Wetenschappen (KNAW) and, after that ended, he received a permanent

position within the CIO, thus ensuring the continuation of the project.

The project aimed to develop a new method for measuring the relative stable isotope ratios of18O/16O, 17O/16O and 2H/1H in water. During my contract, the research group was supposed to develop

thr method up to a level where it could be employed to real-world applications. My work was scheduled

to end after the application of the method to some interesting fields, namely biomedicine and

paleoclimatology. The present thesis reports on our collective results which were achieved during my

presence at the CIO, but could never have been completed without the work already done in the period

before my arrival.

Chapter 1 of this thesis provides some general information on the field of isotope physics as

studied within the CIO. Chapter 2 gives detailed information on the current measurement set-up and the

underlying principles. In Chapter 3 an overview is given of the results of the measurements on

biomedical (enriched) samples, while Chapter 4 shows the results of the measurements on a deep

Greenland ice core. Chapter 5 describes a more exotic application of the technique. In Chapter 6, finally,

an outlook of further expected developments is given.

Radboud van Trigt, September 2001

1General introduction

Introduction

5

1.1 Introduction

In this first chapter, some background information is provided on isotopes, their applicability in

different fields of science, and the methods that are in use for measuring isotopes. The reader should

not expect to find a complete overview of methodologies and applications here, since for that purpose

better sources are available. A much more in depth description of isotopes and their use in hydrology

can, for example, be found in a series of books published by the IAEA and UNESCO (Mook 2001).

At the Centrum voor Isotopenonderzoek (CIO; http://www.cio.phys.rug.nl) of the University of

Groningen, isotope abundance ratios of some light elements from many different sources are routinely

measured. Equipment and trained personnel are available for measuring the relative 2H/1H, 13C/12C,15N/14N and 18O/16O stable isotope abundance ratios at natural and enriched levels in, amongst others,

water and solutions of different kinds, organic materials and air. Further, infrastructure is present for

measuring the isotope abundances of radioactive 3H and 14C in different materials. Next to performing

these routine measurements, the CIO has a long history in improving existing measurement methods

and techniques and in advancing our understanding of the methodologies and the underlying processes

(see, e.g., the CIO Scientific report 1995-1997). It should be seen in this light that the CIO decided to

start the development of a new method based on laser spectrometry for measuring the relative

abundance ratios of the stable isotopes in water. This thesis deals with this development and the first

measurements in the fields of paleoclimatology and biomedicine.

1.2 Isotopes

Most of the elements exist in more than one form. The number of protons Z in the nucleus of an

element X equals the number of electrons in the neutral form of the atom. This number characterises

the element. The nucleus further contains a number of neutrons (N). The mass number A of the element

is defined as the sum of the number of protons (Z) and the number of neutrons (N). The notation used

for a specific nucleus is ZA

NX . Note that the atomic number Z is characteristic for the element and N is

easily calculated from A and Z, so the nucleus is fully defined by A X . Nuclei of the same element

containing a different number of neutrons are referred to as each other’s isotopes. For the light elements

as studied within the CIO, the less abundant isotopes have higher mass numbers (and thus higher

masses).Some of the isotopes are referred to as being radioactive to indicate that their nuclei decay in

time. On the other hand, the constant formation of new nuclei leads to a natural steady-state abundance

Chapter 1

6

of the radioactive isotopes that is fairly constant in time. Other isotopes are referred to as stable,

indicating that their overall abundance in a certain material is not changing in time. However, due to

differences in the stability of intermediate products in the process of nucleosynthesis (“more stable” and

“less stable”), the different stable nuclei have different natural abundances.

For oxygen, for example, the atom number Z equals 8. In its most abundant form its mass

number A equals 16 and it thus has 8 neutrons. Further, oxygen with mass numbers 17 and 18 exist in

abundances of 0.038% and 0.20% in nature, respectively. All three forms are stable. For carbon, next to

the most abundant form (A = 12, Z = 6), isotopes with mass number 13 (1.1%) and 14 (<10-10%) are

found. The heaviest one is unstable and has a half-life time of 5730 years, the other ones are stable.

For all of the lighter elements, the lightest stable isotope is (much) more abundant than the

heavier isotopes. The heavy isotopes can be either stable, or radioactive. The isotopes that are most

frequently measured at the CIO are listed in Table 1.1.

Table 1.1: Isotopes that are most frequently studied at the CIO with their approximate natural

abundances and half-life time.

Isotope 1H 2H 3H 12C 13C 14C 14N 15N 16O 17O 18O

Concentration (%) 99.985 0.015 <10-15 98.9 1.1 <10-10 99.63 0.37 99.75 0.038 0.20

Half-life time (y) stable stable 12.32 stable stable 5730 stable stable stable stable stable

Small changes in the isotope abundances of these (and other) isotopes are used in many fields

of science as tracers or proxies. Later in this chapter, it will be explained why these isotopes behave as

almost ideal tracers or proxies for many different phenomena. The best known application of isotopes is,

without doubt, the dating of organic materials by measuring the remaining 14C content. Its use in

archaeology has become known as “the C-14 method” to the general public. However, many more

applications of isotope measurements exist: They can be found, for example, in hydrology,

oceanography, geology, biology, (bio)medicine, paleoclimatology, soil science, atmospheric research and

food authenticity research.

Isotope abundance ratio measurements are usually performed with dedicated isotope ratio mass

spectrometers (IRMS). In Paragraph 1.3 these are described in more detail.

1.2.1 Definitions and Notation

The isotope abundance ratio, AR, of a stable isotope is defined as:

Introduction

7

AA

A nRXX

= −[ ]

[ ](1.1)

where A is the mass number of the (rare) heavier isotope, X the chemical symbol representing the

element, and n the difference between the mass numbers of the rare and the most abundant isotope

(usually 1 or 2). Table 1.1 lists the approximate natural abundances on earth of some common isotopes.

However, the isotope ratios can differ slightly between different materials as the result of chemical and

physical processes (see Paragraph 1.2.2). The resulting differences in AR are unmanageably small, and it

is hard to measure these ratios in absolute terms. Therefore, the isotope abundance ratios are usually

expressed relative to the same ratio of a calibration material (“standard”). For water, the internationally

accepted calibration material is Vienna Standard Mean Ocean Water (VSMOW). The deviation, δ, relative

to this calibration material is defined as:

δ( )AA

sampleA

VSMOW

XR

R= − 1 (1.2)

and usually expressed in per mil, since δ values are small. For example, for local tapwater in Groningen

on average δ2H = -0.041 = -41‰ is measured, indicating that the abundance ratio of 2H, 2R, equals

0.00014939, compared to an assumed value of 0.00015577 for VSMOW.

It should be noted that the δ-values so-defined now refer to atomic, rather than molecular

isotope ratios, while the latter will be shown to be the result of the measurements using the new laser

spectroscopic technique. In the literature it is more common to use the former. Although in general the

molecular quantity is not exactly equal to its atomic counterpart (e.g., δ2H16OH ≠ δ2H), the difference is

much smaller than the measurement precision, principally owing to the very low abundances of the rare

isotopes. One can therefore neglect this principle difference in nearly all cases.

1.2.2 Fractionation

In the previous paragraph it is already explained that the abundances of the isotopes, as listed

in Table 1.1, are not rigidly conserved quantities in nature. In reality, due to fractionation processes,

variations occur as has first been demonstrated by Urey (1933, 1935, 1947). Isotopic fractionation is

defined as the change in isotope abundance ratios caused by a physical, chemical or biological process.

Most chemical processes depend on the electron structure (and thus the atomic number) of the

atoms or, more precise, the electron structure of the molecules involved in a reaction. Reaction rates are

Chapter 1

8

therefore essentially insensitive to atomic masses or isotopic substitution. Still, for many processes,

chemical, physical and biological, a remaining mass-dependent effect exists, leading to depletion or

enrichment of the isotope concentration in the reaction product, relative to the starting material. The

process is said to be fractionating. This is mainly a consequence of the smaller diffusion coefficients

(lower velocities) of the molecules which have heavy isotopes incorporated, relative to the “normal” light

molecules. The fact that the velocities for the heavier molecules are lower can easily be seen from the

definition of kinetic energy: k T m v⋅ = ⋅ ⋅12

2 (k = Boltzmann constant, T = absolute temperature,

m = molecular mass and v = average molecular velocity). Consequently, heavier molecules have a

slower diffusion rate and experience a lower number of collisions per unit time. Moreover, the strength

of chemical bonds involving different isotopic species will usually be different. In general, molecules

containing heavier isotopes are more stable than their counterparts with lighter isotopes and will thus

react slower. The reason for this difference is found in the potential energy surface of the molecule

involved. Heavier molecules (isotopomers) have lower zero-point energies and are situated deeper in the

potential energy “well” than lighter ones. At higher temperatures the density of (energy) states increases

and the difference in potential energy between light and heavy isotopes will thus decrease. Both the

kinetic energy effect and the potential energy effect are very small compared to the total binding energy

of a typical molecule and the resulting isotope effects are therefore very small as well, resulting in small

natural variations in the isotope concentration of different materials.

Two kinds of isotope fractionation processes can be distinguished: Equilibrium and kinetic

fractionation.

1.2.2.1 Equilibrium fractionation

Equilibrium fractionation involves a redistribution of isotopes among various species or

compounds in an equilibrium process or reaction. When such an equilibrium is established, the forward

and backward reaction rates are equal and the isotope abundances in the reactant and product remain

constant (although usually not identical). The slowest reaction rate will determine the time needed to

establish the equilibrium. Both this equilibration time and the equilibrium position itself are temperature

dependent. The reactant and product can be different chemical compounds, or different phases of one

compound. It is relatively easy to study these equilibrium processes in the laboratory. A typical example

of an equilibrium process in nature is the condensation of raindrops in clouds.

Introduction

9

1.2.2.2 Kinetic fractionation

When in a fractionating process equilibrium can not be established (an irreversible process) one

speaks about kinetic isotope fractionation. Completely kinetic fractionation is only found in processes

were the reaction product becomes instantly isolated from the reactant. It is often difficult to describe

the processes in a quantitative manner, as the underlying physical or chemical kinetic processes are

generally complicated. In nature, most processes are not (truly) kinetic, rather a contribution of

equilibrium fractionation is often present. For example, evaporating water could only be considered to be

a fully kinetic process if the created vapour is immediately and instantaneously removed from the liquid

source and this is virtually never the case. However, the adsorption of gasses by a solid species, the

burning of a material or evaporation through skin could be considered kinetic processes.

1.2.2.3 The fractionation factor

For both equilibrium and kinetic processes, the magnitude of the fractionation is expressed by

the isotope fractionation factor α:

αZ YZ

Y

RR− = (1.3)

where RY and RZ are the isotope abundance ratios of the two compounds Y and Z (starting material and

product, respectively) in the equilibrium or kinetic reaction under consideration. Often Aα is used to

indicate the mass number of the isotopes involved. The exact magnitude of α is dependent on many

factors. For equilibrium processes, temperature is the most important one, while kinetic processes often

involve other factors as well. Usually, the value of α differs little from unity. Therefore, also the deviation

of α from unity, referred to as the fractionation ε, is frequently encountered:

ε α= −( )1 (1.4)

and usually expressed in per mil. Thus, for a process with a fractionation α of 0.99, ε equals –10‰.

Note that ε δδ

δ δδ

δ δ= ++

− = −+

≈ −11

11

Y

Z

Y Z

ZY Z , where δY and δZ are the isotope ratios for the two

materials Y and Z, respectively, provided that δZ << 1, as is most often the case.

As explained in the previous paragraph, for kinetic processes it is hard to measure the

fractionation factor with high accuracy, since it is almost inevitable that some equilibrium contribution

Chapter 1

10

exists in a kinetic process, while it is generally impossible to quantify this equilibrium contribution. For

the quantification of equilibrium fractionation factors, it is much easier to assure proper process

conditions and therefore they are well known for many processes.

1.2.3 Relations between fractionation constants

Some isotopes exist in two rare forms next to the most abundant one. The best known examples

are the carbon isotopes 14C (radioactive), next to stable 13C and 12C and the oxygen isotopes 18O, 17O

and 16O, which are all stable. In the first case one most often assumes 14 132ε ε= ⋅ , and also in the latter

case the fractionation factors follow in good approximation:

( ) /18 1 2 17α α≈ or 12

18 17⋅ ≈ε ε (1.5)

More exactly, Meijer (1998) showed that the relation in δ-values for all meteoric waters (i.e. waters that

take part in the water cycle of the troposphere) is given by:

1 117 18+ = +δ δ λO O( ) (1.6)

with λ as a constant with value 0.5281 (± 0.0015).

Whether the process is completely dominated by equilibrium fractionation or involves a kinetic

contribution to some extent, the same relation between 17O and 18O of Equation 1.6 holds (at least as far

as measurement accuracy enables us to verify). Thus, in the meteoric water cycle, 17O behaves in an

analogue manner as 18O. Therefore, it can be concluded that (for meteoric waters) in principle no new

information can be deduced from the additional measurement of 17O next to the customary 18O

measurements.

1.2.4 Natural variations in isotope abundance ratios

The variations in isotope abundance ratios found in nature are generally small and are a result of

small differences in fractionation. The largest variations are found for hydrogen. In Figure 1.1, an

overview for 18O and 2H is given of the isotope abundance ranges that are encountered in different

natural compounds.

Figure 1.1 clearly shows that δ2H and δ18O behave qualitatively very similar. For example, for

both isotopes enrichments are found for water from the dead sea, while strong depletions can be found

in antarctic ice. In fact, for meteoric waters at a given geographic location, δ2H changes in phase with,

Introduction

11

but roughly 5 to 9 times as fast as δ18O. The functional relation between δ2H and δ18O is known as the

“meteoric water line” (MWL; Craig 1961a). In Chapter 4, this phenomenon is discussed in more detail.

Figure 1.1: Natural range of some common materials for δ18O and δ2H with respect to VSMOW. Note that

the scales are different for both isotopes. Values as low as – 450‰ have been measured for δ2H in

polar ice.

From Figure 1.1 it can also be seen that it is necessary to measure the isotope ratios with high

accuracy, since the signal present in the isotope signature of natural water samples is generally small.

−−−−200 −−−−150 −−−−100 −−−−50 −−−−0 +50

δδδδ2H (‰)

Ocean water

Marine moisture

(sub)Tropical precipitation

Dead Sea/Lake Chad

Alpine glaciers

Arctic sea ice

Greenland ice

Antarctic ice

−−−−60 −−−−40 −−−−20 020

Ocean water

Marine moisture

(sub)Tropical precipitation

Dead Sea/Lake Chad

Alpine glaciers

Arctic sea ice

Greenland ice

Antarctic ice

δδδδ18O (‰)

Chapter 1

12

Typical measurement accuracies are 1‰ for δ2H and 0.1‰ for δ18O. Thus, unless the fact that the

absolute δ18O signal is much smaller, its measurement can provide at least the same amount of

information as the δ2H signal.

1.2.5 Calibration materials and normalization

As stated in Paragraph 1.2.1, Vienna Standard Mean Ocean Water (VSMOW) is the

internationally accepted calibration material for δ2H and δ18O measurements on water. It is virtually

equal to the original SMOW material and defined as δ = 0‰ for both δ2H and δ18O (Craig 1961b). As

can be seen from Figure 1.1, this ocean water is one of the “isotopically heaviest” of the naturally

occurring species.

Using one calibration material (VSMOW) the isotope scales are, in principle, fully defined.

However, it is also important to be able to compare the results of different laboratories. For this purpose,

it turned out to be necessary to define a second calibration material in order to be able to reliably correct

for the mean deviation made, and thus fixate the scale. This second calibration material was chosen to

represent values at the other (lower) end of the natural scale: Standard Light Antarctic Precipitation

(SLAP) is used. The δ-values of SLAP were fixed with respect to VSMOW at δ2H = – 428‰ and

δ18O = – 55.5‰, based on gravimetric remixing and tuning of SLAP from isotopically pure water

standards (Gonfiantini 1977).

For hydrogen, it is indeed possible to produce isotopically pure H2O and D2O and therefore the defined

value of – 428‰ for SLAP is believed to reflect the real value very closely (i.e., better than the accuracy

of the isotope ratio measurements). For oxygen, however, it is virtually impossible to produce isotopically

pure H16OH and H18OH and some uncertainty exists as to the “true” δ18O value of SLAP. Still, the value

was agreed upon in order to fix the δ-scale and facilitate international data comparisons.

For both 2H and 18O, all sample values (in the natural range) are presented on the VSMOW-SLAP scale.

This is referred to as normalization (Coplen 1988) and it is very important to reduce the interlaboratory

differences to acceptable levels (Brand 2001).

An alternative approach for determining the “real” δ-value of SLAP would be the measurement of

the absolute concentrations of the isotopes in VSMOW and SLAP. If these could be determined with high

enough accuracy, the true δ–values of SLAP could easily be calculated. Some efforts to perform these

absolute measurements for 2H have been undertaken, and the results do agree with the defined value

within the errors (Hageman 1970, De Wit 1980, Tse 1980). Baertschi (1976) determined the absolute

abundances of 18O, but here the accuracy is not high enough in order to study the “real” δ-value of

SLAP.

Introduction

13

A third material is in use as reference material: Greenland Ice Sheet Precipitation (GISP). It may

be used as a check on a correct VSMOW-SLAP calibration in a particular experiment. Its values are

roughly half way between VSMOW and SLAP and have been determined in an interlaboratory

comparison to be δ2H = – 189.5‰ and δ18O = – 24.78‰, normalised on the VSMOW-SLAP scale

(Gonfiantini 1984, Gröning 2000).

The normalised VSMOW-SLAP scale improves the inter-laboratory accuracy considerably, thereby

facilitating the interpretation of data from different sources. However, it is a rather pragmatic solution

which leads to the fact that the “permil” enrichment or depletion on the VSMOW–SLAP scale is no longer

a real arithmetic per mille.

VSMOW, GISP and SLAP are nowadays distributed by the International Atomic Energy Agency

(IAEA; http://www.iaea.or.at) and the National Institute of Standards and Technology (NIST;

http://www.nist.gov).

For enriched samples, as often employed in biomedicine, the disagreements about the “true”

isotope ratios are even higher. The internationally available enriched standards have values assigned

after an interlaboratory comparison (Parr 1991). Within the 95% reliability interval, their values span

quite a broad range (typically 1 to 2% of their value). These standards are still only moderately enriched

(up to 1000‰ for 2H and 500‰ for 18O, aimed at administration to humans). In experiments in small

animals, often ten times higher enrichment levels are needed in order to measure their turnover rates

during 24 hours. For enriched samples, scale problems with IRMS are even higher than for samples

within the natural range and it can thus be expected that measurement inaccuracies are higher as well.

Strictly spoken, the values of enriched samples should also be normalised on the VSMOW-SLAP scale,

but in practice this is never done, since extrapolation far outside the natural range is then required.

Instead, local standards, which are mixed from extremely highly enriched waters, are often used for

calibration purposes. In that case, the enrichment stated by the supplier is the only guarantee for an

isotope scale with a real physical meaning. However, of course the supplier has had the same problems

with obtaining the right enrichment levels. More on this subject will be presented in Chapter 3.

The primary calibration materials VSMOW and SLAP are not available in unlimited quantities.

Each stable isotope laboratory is therefore expected to maintain its own set of local standards, which are

regularly checked against the calibration materials. At the CIO a range of local water standards is used.

The Groningen Standards (GS-##), span the entire natural abundance range and the biomedical (BM-#)

and triply labelled water standards (TLW-#) cover the regular range of enriched samples and have been

gravimetrically mixed from highly enriched waters.

Chapter 1

14

1.2.6 Accuracy and precision

A very clear graphical representation of the terms accuracy and precision was given by

Speakman (1997) and is reproduced after slight modification in Figure 1.2.

Figure 1.2: Graphical representation of accuracy and precision. Accuracy is increasing from left to right

and precision is increasing from below to higher up. Reproduced from Speakman (1997).

From Figure 1.2 it is clear that a precise method is not necessarily accurate. Precision has to do

only with the reproducibility of a measurement and, thus, with random errors. Accuracy, however,

quantifies the systematic errors of the measurement set-up. This can be improved by correct calibration

procedures of the initial measurements. IRMS machines often have a very good precision

(reproducibility), but a careful calibration must always be carried out in order to obtain accurate

measurements.

Prec

isio

n

Accuracy

Introduction

15

1.2.7 Some applications

The best known application of isotope measurements is in archaeology: By measuring the

remaining amount of radioactive 14C in a sample, it can be dated. However, many applications exist for

stable isotope measurements as well. This thesis deals with these stable isotope measurements only,

and especially those of water.

Stable isotope ratio measurements have most often been used to provide information on the

history of the material in terms of isotope fractionating processes that it has experienced in the past. The

information is often used in addition to concentration data and in such cases may enable the

identification and quantification of different sources and sinks of the material of interest. For example,

one can often distinguish between the sources of a river: Melting water or rain. Another example is the

discrimination between sugar derived from cane or from beets. The most demanding application in terms

of precision and accuracy is the mapping of the different sources and sinks of greenhouse gasses (e.g.,

CO2 and CH4) and their regional and worldwide distribution. In medicine, an important application is the

determination of 13C in respiratory CO2 after administration of labelled urea as proof of the presence of

the Heliobacter Pylori bacteria. Yet another example is the measurement of δ13C and δ18O of foraminifera

as indicators for seawater temperatures in the past. These are only a few of the many possible

applications of stable isotopes. Within the CIO many of the necessary measurements are routinely

applied.

In this thesis two major applications will be discussed: The doubly labelled water method to

measure energy expenditure in free-ranging animals or humans (Chapter 3) and the measurement of

isotope ratios in ice cores as a proxy for the past climate (Chapter 4).

1.3 Techniques

The traditional method for measuring stable isotopes in water makes use of an Isotope Ratio

Mass Spectrometer (IRMS). First, a short overview of the state of the techniques at the time the

research described in this thesis started (1993) will be given. Subsequently, an inventory of the

remaining problems using these traditional techniques and also a short description of more recent IRMS

developments will be presented. Finally, an overview of alternative optical techniques will be given.

Chapter 1

16

1.3.1 Overview of methods for isotope ratio measurements on H2O

samples until 1993

1.3.1.1 Isotope Ratio Mass Spectrometry (IRMS)

The IRMS method has originally been developed by Nier (1937). The IRMS distinguishes itself

from other Mass Spectrometer designs by it being dedicated to the extremely accurate measurements of

only a few (typically 2 or 3) selected, fixed, masses and by performing these measurements sequentially

on the sample as well as a reference gas. Most machines switch a number of times between the

measurement of sample and reference gas (dual inlet) and compare the detector current at the different

masses to obtain the isotope ratio of the sample relative to that of the machine reference gas.

The measurements are being performed on the molecular species that the IRMS was designed

for (usually CO2 or H2) and that, if necessary, have quantitatively been made out of the sample material

via chemical conversion.

The easiest approach would be the direct measurement of H2O, thus finding H18OH at mass 20

and 2HOH at mass 19. However, H17OH would show up at mass 19 as well. For natural samples 2HOH

and H17OH have abundances of 0.030% (2 times 0.015) and 0.038%, respectively. Because of this mass

overlap it is thus not possible to determine either of the two accurately. Further, due to the wall

adsorption properties (“stickiness”) of the water molecule it is hard to maintain proper high vacuum

conditions of the IRMS apparatus. Still, a commercial apparatus (the aqua-SIRA) was built using direct

δ18O measurements combined with an on-line reduction of H2O to H2 over hot uranium

(Paragraph 1.3.1.2; Hagemann 1978, Wong 1984). This concept, however, was apperently not succesfull

enough, and the principle has been abandonned.

In virtually all designs, the 18O abundance is measured in the CO2 molecule. In this case, the

most abundant 12C16O16O molecule is then found at mass 44, whereas the 12C18O16O molecule is found at

mass 46. The relatively rare (0.038%) 12C17O16O molecule is observed at mass 45, but so is the much

more abundant (1.1%) 13C16O16O molecule. Therefore, accurate measurements on CO2 can in practice

only be done for 13C and not for 17O. Instead, δ17O is calculated from the measured δ18O using

Equation 1.6 and its value is used to correct the initial δ13C result.

Sometimes O2 is used as the gas to measure the oxygen isotope ratios. The most abundant16O16O molecule has mass 32, the 17O16O isotopomer is found at mass 33, and the 16O18O molecule at

mass 34. The 33/32 and 34/32 molecular ratios are virtually equal to the atomic 17O/16O and 18O/16O

ratios, respectively, since the concentration of the isotopes is so low that double isotopic substitution of

Introduction

17

the oxygen molecule does not play a significant role. The chemical conversion of water into O2, however,

is still problematic.

As in the aqua-SIRA, H2 gas is usually produced by a reduction of H2O to H2 over hot uranium or

zinc. The H2 that is formed is let into the IRMS and the masses 2 and 3 are detected to determine δ2H.

Hydrogen gas with known isotopic composition is used as the machine reference gas. The amount of H3+

(also at mass 3) that is produced by the source must be corrected for.

1.3.1.2 Sample preparation

Since their first use in isotope ratio measurement, IRMS equipment has gradually been improved

substantially. Nowadays, dedicated IRMS instruments can be purchased which are able to achieve a very

high precision and sample throughput. Still some serious problems remain. The main problems are found

in sample preparation, rather than in the IRMS measurement itself. The necessary chemical conversion

or exchange from water to either H2, CO2 or O2 is a possible source of errors. For many different

materials, techniques have been developed which aim to make the conversion quantitative. A 100%

conversion is the best guarantee that fractionation effects are eliminated from the conversion process.

For δ2H measurements on water, often conversion to H2 is achieved by reduction of the water

over hot (800ºC) uranium (Bigeleisen 1952) or zinc (Friedman 1953, Coleman 1982). Only 10 µl of water

is needed to produce sufficient hydrogen gas for the IRMS analysis. A serious disadvantage is that

uranium is a poisonous and radioactive heavy metal with danger of explosion, when in contact with air at

the high temperatures used. Nickel, manganese, chromium and especially zinc (with special treatment)

can also be used as alternative reducing agents in batch processes (Tobias 1995, Shouakar-Stash 2000,

Gehre 1996, Socki 1999). They do not have the disadvantage of being extremely poisonous, but their

reducing capabilities are lower than that of uranium. Their efficiency is probably dependent on small

amounts of impurities (sodium) that are present (Karhu 1997). All these reduction methods are difficult

to automate in a continuous process and suffer from memory effects due to adsorption of water.

Moreover, contaminations in the sample can influence the efficiency of the reducing metal.

As an alternative for the reduction of H2O, H2-H2O equilibration, catalysed by platinum, can be

exploited (Horita 1988, Coplen 1991). In this process, platinum, supported by a porous hydrophobic

polymer or alumina, is used as a catalyst to establish an isotopic exchange between water and hydrogen

gas of known isotopic composition that is added to the sample. It is very important that the temperature

at which the equilibrium is established is stable and known with high accuracy, since the temperature

dependence of the isotope equilibrium position is very large (~ 6‰ per degree). The process can be

automated, but rather big amounts of water (~ 1 ml) are needed. Further, the isotopic equilibrium is

Chapter 1

18

accompanied by a very large fractionation of about –750‰, such that the H2 gas to analyse contains

almost four times less deuterium than the original water sample. This aggravates the already serious

problem of H3+ production in the ion source of the IRMS.

For δ18O measurements, nearly always the Epstein/Mayeda method is applied (Epstein 1953).

This involves the transfer of the isotope signal of H2O to CO2 by way of the bicarbonate reaction. Prior to

the reaction, CO2 with known isotopic composition is added to the water sample. After an equilibration

period (at rest and at room temperature in the order of one or two days, but shorter when stirred or

shaken), the CO2 is removed and measured on IRMS. From the measured isotopic ratio (with corrections

for initial CO2 composition and molar CO2:H2O ratio), the original 18O content in the water sample can be

calculated. For the best results, typically 1 ml of water is needed, but as little as 10 µl is routinely being

used (Speakman 1997). Automatisation is relatively easy and preparation machines are commercially

available.

All sample pre-treatments for conversion of water to H2 or CO2 are very laborious and are often

the limiting factor in isotope ratio determinations, both in throughput and in precision. In a typical

isotope laboratory with manual sample preparation and an off-line IRMS set-up, a skilled technician can

do 50 18O measurements or 20 2H measurements per day.

The precision of the entire preparation, measurement and calibration process that is often

claimed for natural samples, is typically in the range between 0.03‰ and 0.2‰ for δ18O and between

0.3‰ and 1‰ for δ2H. In interlaboratory comparisons, however, the observed variation is often larger.

Even in a recent ring test (Lippmann 1999) a 2σ spread of ± 0.25‰ for δ18O and ± 3‰ for δ2H is

found after removal of outliers (about 10 of 80 laboratories). It thus seems that many laboratories are

considerably overestimating their own accuracy, or claim the intra-laboratory precision to be their inter-

laboratory accuracy.

1.3.2 New developments since 1993

The above mentioned disadvantages have led to the attempts to develop totally different

techniques. Since the start of the project described in this thesis, other developments with the aim of

measuring more samples in the same time span with higher accuracies have been underway. This is

especially true for the measurement of δ2H, since the traditional methods for measuring this isotope are

more laborious and harder to automate. Automated methods to measure 18O already existed before

1993. As a first example of recent improvements, one can mention the H2-H2O equilibration technique,

which was automated and integrated with the CO2-H2O equilibration method for use in the doubly

Introduction

19

labelled water method (Thielecke 1997, see also Chapter 3). For a more extended, although somewhat

older overview on the automatisation of measurement techniques for 2H, see Brand (1996).

An enormous breakthrough was made by the development of continuous flow IRMS (CF-IRMS)

systems. CF-IRMS has first been used to miniaturise the existing techniques for H2 preparation. The

batch reduction processes can be coupled to a mass spectrometer in such a way that the H2 gas

produced can be led directly to the IRMS after the reduction process is completed (“on-line” IRMS).

Tobias (1995) used hot nickel to reduce his water samples. Gehre (1996) used chromium to reduce 1 µl

water samples. Vaughn (1998) used 0.5 µl to 5 µl samples with uranium as reducing agent. Socki (1999)

applied zinc to 10 µl water samples. And Shouakar-Stash (2000) showed that also manganese can be

used as on-line reduction agent for 5 µl water samples. All claim accuracies and precisions in the same

range as seen in the traditional techniques, but are able to measure more samples in the same time

span. However, Hopple (1998) showed that, for example, the new uranium method still has some

reliability problems.

The biggest problem in CF-IRMS is the accurate measurement of mass 3 (1H-2H gas) in the

presence of an overwhelming amount of the carrier gas, He, with mass 4. Their relative amounts can

differ by five orders of magnitude and the detection of a small fraction of the low-energy helium ions can

thus lead to large errors. Brockwell (1992) tried to quench the He+ ions by addition of some N2 gas and

also tried to form C2H2 instead of H2. Unfortunately, this approach was not very successful (Hilkert

1999). The problem was already tackled more effectively by Tobias (1995), using a hot palladium filter

which is permeable for hydrogen, but not for helium. He also tried to use argon as carrier gas instead of

helium. Prosser (1995) designed an IRMS detector with larger dispersion (physical separation) to avoid

peak overlap and that seemed to provide a sufficient separation for measuring H2 accurately. Hilkert

(1999) used an energy filter (retardation lens) to prevent He+ ions from arriving at the same detector as

H2. Merren (2000) developed an electrostatic filter, basically a second mass separation step.

All developments mentioned are additions to the toolbox with techniques for measuring isotope

ratios. As a result, the sample throughput and the ease of operation increased. However, the accuracy of

the measurements did not dramatically improve.

On-line pyrolysis, coupled with CF-IRMS was the next big breakthrough. The term elemental

analyser (EA) is also often used in the literature to describe a pyrolysis system. Begley (1997) developed

a method in which the H2O sample is led over nickel metal on which a hydrocarbon is deposited. The

nickel catalyst is packed in a furnace at 1050 ºC and the water is, by reaction with the deposited carbon,

converted into H2 and CO. Both are simultaneously measured in the on-line coupled CF-IRMS, which

rapidly switches between the masses. This method is also applicable to volatile organic materials. The

reported precision is 2‰ for δ2H and 0.3‰ for δ18O at natural abundance. The amount of water

Chapter 1

20

needed is extremely low: 5 nl. The same approach (using nickel and carbon) is described in an

application note of Micromass, a producer of commercial isotope ratio mass spectrometers (Fourel

1998). Using another catalyst, based on chromium, and at 1450ºC, they claim to achieve a mean

standard deviation of about 0.5‰ for δ2H for repeated measurements (precision) on water samples and

about 0.2‰ precision for δ18O for the same extremely small sample size (Morrison 2001). In addition,

the δ18O value in organic and even ionic compounds may be measured using this method.

As mentioned before, the measurement problems for 18O are smaller, and consequently fewer

efforts have been taken to improve the existing automated systems, based on the traditional

Epstein/Mayeda process. Still, some alternatives were published. On-line pyrolysis (with formation of CO)

coupled with CF-IRMS is applied; comparable to H2 measurements (Kornexl 1999, Wang 2000).

Subsequently, a new approach using on-line isotopic exchange with CO2 bubbles in a long capillary at

elevated temperatures was described by Leuenberger (2001). A more fundamental, alternative method

for measuring δ18O is electrolysis of water in the presence of CuSO4 electrolyte to produce O2 gas (Meijer

1998). This way it is also possible to measure δ17O. A disadvantage is that almost 1 ml of water is

needed.

Again, the newly developed techniques are additions to those previously available. The sample

size decreased and the new methods have improved the ease of operation. The overall accuracy of

isotope abundance ratio measurements, however, did not increase.

1.3.3 Spectroscopic techniques

Parallel with the developments in conventional IRMS-based methods for the determination of

isotope ratios as described above, optical techniques have been developed.

The deuterium concentration of enriched water samples has been measured in the condensed

phase (liquid water), using a specially designed infrared filter photometer based on absorption

spectrometry, using 0.2 mm path-length cells with calcium fluoride windows (Turner 1960, Stansell

1968, Byers 1979, Lukaski 1985, Fusch 1988). Even when the temperature was kept constant to within

0.005ºC, considerable analysis uncertainties persisted. Typically, 10 ml of distilled water sample was

required. For these reasons, Shakar (1986) measured water in the vapour phase, reducing the sample

size to a few microliter. The measurement was performed using a regular spectrophotometer in the

2760 – 2670 cm-1 range, with dispersive gratings and a sample cell with 10 cm path-length kept at

125 ºC. The researchers claim to be able to detect a change (sensitivity) in the deuterium concentration

of 60 ppm (natural abundance = 150 ppm). The method is therefore only useful in the high2H–enrichment regime for determining the amount of total body water by the dilution of an

Introduction

21

administrated amount of enriched sample. More recently, it was found that optothermal detection could

be used for the same purpose (Annyas 1999). By periodically heating of a sample, detectable thermal

waves are produced. The sample (~ 300 µl) is pipetted onto a disc and periodically illuminated with

4 µm radiation. Precisions are not too good (typically 2σ equals 75 ppmv for a value of 350 ppmv), but

since the set-up was far from ideal, improvements are expected to be made.

In contrast to the above-mentioned, Site-specific Natural Isotopic Fractionation studied by

Nuclear Magnetic Resonance (SNIF-NMR) is a matured technique for isotope ratio analysis and

instruments are commercially available. It has been used for measuring stable isotope ratios of 2H, 13C,15N and 17O in a variety of substances in order to check their purity and identify their origin. For

example, it was applied in the authentication of salmons (to distinguish wild and farmed salmons) and to

determine the origin of vanillin (Aursand 2000, Martin 1996). The precision of this technique, however, is

not sufficient for most other applications.

The precision of spectroscopy in the condensed phase was hugely improved to values

comparable to the IRMS (below 1% relative error) by using Fourier transform infrared (FT-IR)

spectroscopy (Fusch 1993). Distillation of samples is still required, but less sample (down to 60 µl) is

needed for the analysis. The technique of FT-IR spectroscopy has also been applied to the measurement

of 13C/12C ratios in CO2 in ambient air (Esler 2000a). The analytical precision achieved is 0.1‰. Further,

using FT-IR on air, the δ15N, δ18O and δ17O isotope ratios in N2O are determined with precisions of about

1.0‰, 2.5‰ and 4.4‰, respectively, besides the CO2, CH4 and CO concentrations (Esler 2000b). Also

flux measurements of NH3, N2O and CO2 have been done using this technique (Griffith 2000). A

disadvantage is that the instrumentation is quite bulky and expensive.

After some attempts in order to design a nondispersive infrared (NDIR) spectrometer, which did

not lead to a precision useful in any application (Milatz 1951, Irving 1986), it was successfully applied by

Haisch (1994a) in a measurement of the 13C/12C ratio in breath CO2. By using separate channels for the

measurement of 12CO2 and 13CO2, both with their own acousto-optical detector filled with the gas to

measure, a reproducibility of 0.4‰ for CO2 concentrations in exhaled air was achieved for the range of

2.5% to 5%. This is sufficient for biomedical applications, in particular the 13C urea breath test for the

detection of Heliobacter pylori bacteria (Haisch 1994b). However, for many other molecules including

H2O, this technique cannot be applied to the measurement of all of the isotopes since the resolution of

the apparatus is too low to distinguish between absorption features that are close together. Moreover,

one has no built-in check on sample contamination since no high-resolution information is available.

Becker (1992) measured δ13C in CO2 gas with a tunable diode laser in the region around

2291 cm-1 as light source. The achieved precision amounted to 4‰. Schupp (1993) and

Chapter 1

22

Bergamaschi (1994) designed an apparatus based on a tunable lead-salt diode laser in order to measure13C/12C and 2H/1H abundances in methane. The precision reported for this apparatus is 0.44‰ for δ13C

and 5.1‰ for δ2H, but it is not possible to measure both isotopes within the same run. Uehara (1998,

2001) built a comparable system based on three different tunable diode lasers with fixed, different,

center frequencies between 1.5 µm and 2.0 µm and using wavelength modulation. They were able to

measure 13CH4/12CH4 and nitrogen isotopes of N2O in a site-specific way.

1.4 Summary

It is necessary to be aware of the importance of some background theory on isotope ratios when

attempting to measure them. Especially calibration and normalization and the difference between

precision and accuracy need special attention. In the interpretation of results, it is important to realise

that different fractionation effects may have consequently occurred.

Isotope ratio measurement techniques have been improved enormously in the last years, especially in

terms of sample throughput, sample size and ease of operation. Especially in the case of CF-IRMS

coupled with on-line pyrolysis a lot of progress has been made. The fact that sometimes precisions are

reported and sometimes accuracies, makes comparisons between different techniques hard. It is

therefore not always possible to judge the usability of the methods. Of the optical measurement

techniques, the laser-based methods offer the highest spectral resolution (selectivity). Moreover, they

are favourable over the other techniques in the sense that their application is not limited to a selected

number of special molecules or matrices: By changing the light source all of the important small

molecules can be considered. Diode lasers have the additional advantage that they are cheap compared

to other devices for isotope ratio measurements, but they are not available for all spectral regions.

2Laser spectrometry: Technique

and apparatus

Set-up

25

2. Laser spectrometry: Technique and

apparatusThis chapter will give an extensive description of the principles and present set-up for

measuring the stable isotopes in water by means of laser spectrometry (LS). It is partly based on

previously published material (Kerstel 1999, Van Trigt 2001a, Kerstel 2001b). In Chapter 6 some

future developments of the apparatus as well as the method will be described.

2.1 Measurement principle

The newly developed method for measuring stable isotopes in water is based on direct

absorption laser spectrometry (LS). For most relatively small molecules the room-temperature, low

pressure, gas phase, infrared spectra reveal absorptions due to individual ro-vibrational transitions

(“lines”) that can each be uniquely assigned to one of the various isotopic species present. The

absorption intensities of the isotopomer lines, relative to that of a line belonging to the most

abundant isotopic species, can be used to calculate the relative isotope abundance ratio of interest.

The measurement of the absorption intensity profiles is done by recording the attenuation of a laser

beam with narrow spectral line width as a function of its wavelength.

2.1.1 Infrared spectrum of water

An extended section of the IR absorption spectrum of water is depicted in Figure 2.1.

Thousands of lines are plotted here; all four of the isotopomers of interest (i.e., 1H16O1H, 1H17O1H,1H18O1H, and 2H16O1H) are included in the figure. Their relative intensities are based on their

abundances in natural water.

The first challenge in the process of developing the desired laser spectrometric measurement

method is to identify a section in this range in which all of the isotopomers of interest have

transitions that are:

(1) of comparable intensities (thus a weak absorption line for the most abundant 1H1H16O, relative to

the absorption strengths of the other isotopomers)

(2) within a small spectral range (to make fast continuous scans possible) and

(3) without interference from other strong lines.

The second challenge is to find a reliable light source that is continuously tunable in the

selected section of the absorption spectrum.

Chapter 2

26

Figure 2.1: Overview of the high resolution near- and mid-IR H2O absorption spectrum for gaseous

natural water, in the range from 1 µm to 8 µm (10000 cm-1 to 1250 cm-1). All four of the

isotopomers of interest are included. The arrow shows the LS wavelength of about

2.7 µm (3664 cm–1).

An excellent section that satisfies all of these demands has been found from 3664.00 cm-1 to

3662.80 cm-1 (2.7293 µm to 2.7302 µm) and is shown in Figure 2.2. The most important lines in this

section are listed in Table 2.1. Note that this is an extremely small part of the spectral range

depicted in Figure 2.1.

0.0 100

5.0 10-20

1.0 10-19

1.5 10-19

2.0 10-19

2.5 10-19

3.0 10-19

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Inte

nsi

ty (

cm/m

ole

c)

Wavelength (µm)2.73 µm

Set-up

27

Figure 2.2: Experimentally acquired spectrum of the lines of Table 2.1, and three other transitions

that are present in this section, for a natural water sample. The numbering of the lines shown here

will be used throughout this thesis. Note that the most intense line (#3) is more than 3 orders of a

magnitude weaker, in terms of transition strength, than the strongest lines in Figure 2.1.

The water absorptions around 2.7 µm are due to ro-vibrational transitions belonging

primarily to the ν1 (symmetric OH-stretching) and ν3 (antisymmetric OH-stretching) vibrational

bands. As an added bonus, the transitions in question have only relatively small temperature

coefficients. Reliable, accurate isotope ratio measurements can thus be performed without resorting

to complicated temperature stabilisation schemes, as will be demonstrated in this thesis.

In the case of a natural water sample, the 2HOH line (#7) shows the smallest absorption in

comparison to the other selected lines. This is actually an advantage in the case of enriched samples,

since the range of δ2H values encountered in practice is typically one order of magnitude larger than

that for the other isotopic species. The enriched water samples used in bio-medical studies yield2HOH extinction ratios that are comparable in size or even larger than those of the other lines (see

also Chapter 3). At the same time, the strength of line #7 is sufficient to study “natural” samples.

3662.6 3662.8 3663.0 3663.2 3663.4 3663.6 3663.8 3664.0

Ab

sorp

tio

n (

arb

. u

)

wavenumber (cm-1)

1

2

3

4

5

6

7

1H18O1H

1H16O1H

1H17O1H

2H16O1H2H16O1H

2H16O1H

1H16O1H

Chapter 2

28

Table 2.1: The ro-vibrational transitions used in this study.

wavenumber

(cm-1)

Intensity b)

(cm·molecule-1)

temp. coeff. c) at

300 K (K-1)

assignment d) Line

#

Isotopomer

3662.920 1.8·10-23 1.3·10-3 ν = (001) ← (000)

J = 515 ← 514

2 1H18O1H

3663.045 7.5·10-23 4.4·10-3 ν = (100) ← (000)

J = 624 ← 717

3 1H16O1H

3663.321 6.4·10-23 -1.5·10-3 ν = (001) ← (000)

J = 313 ← 414

5 1H17O1H

3663.842 1.2·10-23 -3.4·10-3 ν = (001) ← (000)

J = 212 ← 313

7 2H16O1H

a) All values are taken from the HITRAN 1996 spectroscopic database (Rothman 1998).

b) The intensities are for a natural water sample with abundances: 0.998, 0.00199, 0.00038, and

0.0003 for 1H16O1H, 1H18O1H, 1H17O1H, and 2H16O1H, respectively.

c) The temperature coefficients give the relative change with temperature in absorption intensity of

the selected transitions. They are calculated using the HITRAN 1996 database. See also

Equation 2.4.

d) The notation for the vibrational bands is (ν1,ν2,ν3), whereas the rotational levels are identified by

the three quantum numbers JKaKc.

2.1.2 Spectrometry

The spectroscopic isotope ratio measurement relies on the fact that the attenuation of a

laser beam of initial intensity I0 passing through a gaseous sample is directly related to the number

of molecules absorbing at the frequency ν of the laser radiation. The relation between the

transmitted intensity I(ν) and the molecular density n is given by the Lambert-Beer law (Demtröder

1981):

I I e I e f n l( ) ( ) ( )ν α ν ν ν= ⋅ = ⋅− − ⋅ − ⋅ ⋅0 0

0S (2.1)

The quantity α(ν) will be referred to as the absorption coefficient. Further, S is the line strength, f(ν-

ν0) the normalised line shape function and l the optical path length. In the case of a Doppler

broadened line with a half-width at half-maximum (HWHM) of ΓD, the line shape function takes on

the value f(0) = [√(ln(2)/J)]/ΓD at centre frequency ν0. Given a typical line strength of

2·10–23 cm/molecule for the rotational lines of interest and a gas cell filling of about 10 µl (10 mg)

Set-up

29

water in a 1 litre volume (resulting in a pressure broadened line width of 0.008 cm-1), one calculates

a relative attenuation (I0 - I(ν0))/I0 of about 73% for an optical path length “l” of 20.5 m in the

multiple-pass cell. Not accidentally, this is very close to the optimal value, providing the highest

signal-to-noise ratio (S/N). This can be seen as follows: Assume that the measurement of the power

entering the gas cell, as well as the measurement of the signal transmitted through the gas cell, are

inflicted with a measurement error δI that is independent of the signal level (this will be the case if

detector and/or amplifier noise is the limiting noise factor). The S/N of the measurement of the

absorption coefficient at line centre, α(ν0) ≡ S·f(0)·n·l , then equals:

S NI I

I I II

I/

( )( )

( )( )

ln( )

= = ⋅⋅ +( ) ⋅

α να ν

νδ ν ν

0

0

0 0

0 0

0

0∆(2.2)

It is straightforward to show that the maximum S/N is obtained for I(ν0)/I0 = 0.28, corresponding to

an absorption coefficient of 1.28. In fact, if one demands that the S/N be larger than 50% of this

maximum value, I(ν0)/I0 should be between 0.048 and 0.71 (i.e., the attenuation should be between

29% and 95%, or the absorption coefficient between 0.33 and 3.0). This implies that for any given

combination of path length and line strength a one-order magnitude range of molecular densities can

be accommodated. This is important, as we want to have the ability to work with strongly enriched

samples. As mentioned before, the 2HOH line can become 10 times more intense in certain

biomedical applications (See also Chapter 3).

In a spectroscopic measurement, the isotope ratio (or rather its deviation from that of a

well-defined standard), is obtained in a way illustrated in Figure 2.3. Here, two spectral features are

present in the region scanned by the laser, of which one belongs to the most abundant isotopic

species a (i.e., H16OH), the other to the less abundant species x (in this case H18OH, but it may as

well be H17OH or 2HOH).

The curve labelled “r” in Figure 2.3 represents the spectrum of a reference water (working

standard). The spectrum of the (unknown) water sample is given by the curve “s”. Both spectra have

already been converted from transmittance to absorption coefficient by the application of

Equation 2.1. The “super-ratio” of the peak intensities αz = α(ν0,z) now yields:

α αα α

xs

as

xr

ar

xs

as

xr

ar

xr

ar

xs

as

xs

as

xr

ar

n n

n n

( )( ) =

( )( ) ⋅

( )( ) ⋅

( )( )

S S

S S

Γ ΓΓ Γ

(2.3)

There is no dependence on the optical path lengths in the sample and reference cells as

these are necessarily the same for both isotopic species. The line widths and their temperature

Chapter 2

30

dependence are for most practical purposes the same for both isotopic species. The line strength S

depends on the number of molecules in the lower state of the ro-vibrational transition and is

therefore in general temperature dependent (it also includes the effect of induced emission, which,

however, is negligibly small in our case). The first two factors on the right-hand side in Equation 2.3

will therefore reduce to unity only if the two gas cells are kept at the same temperature. However, if

one allows for a small temperature difference between the sample and reference gas cells, say

∆T = Ts – Tr, then this factor will in first order equal:

S S

S S

S S

S S S

S

S

Sxs

as

xr

ar

xr

ar

xs

as

xs

as

xr

ar

xr

x

r

ar

x

r

T T T TT

( )( ) ⋅

( )( ) ≈

( )( ) ≈ +

−

Γ ΓΓ Γ

∆11 1( ) ( )

∂∂

∂∂

= + −[ ]1 ζ ζx a T∆ (2.4)

in which ζ represents the temperature coefficient, as shown in Table 2.1. These are

relatively small in the case of the absorption lines used in this study. Consequently, only passive

control of the gas cell temperature is needed.

0

0.5

1

1.5

2

2.5

3662.85 3662.9 3662.95 3663 3663.05 3663.1 3663.15 3663.2

αααα (

arb

. u.)

Wavenumber (cm-1)

r

sαααα

xs

ααααa

r

ααααx

r

ααααa

s

νννν0,x

νννν0,a

Figure 2.3: Two spectral features, the smaller one belonging to a less abundant isotopomer “x” (in

this case H18OH), the bigger one to “a” (here H16OH). “s” is the spectrum of a sample, while “r”

represents a reference water. Their line intensities are a direct measure of the abundance.

Set-up

31

In general, the isotope ratio of a sample is given by xR=nx/na, see also Equation 1.1.

However, it is customary to use xδ, the relative change in the isotope ratio with respect to that of a

standard water. Without loss of generality our reference water can be chosen to be this standard, in

which case (in accordance with Equation 1.2):

xx s

x rx a

s

x ar

RR

n nn n

δ ≡ − = −1 1( / )( / )

(2.5)

Again, it should be noted that the δ-values so-defined now refer to molecular, rather than

atomic isotope ratios. However, in Section 1.2.1 I was already concluded that the difference is much

smaller than our measurement precision. One can therefore neglect this principle difference.

Combining Equations 2.3 through 2.5 yields the expression for xδ we are after:

x xs

as

xr

ar x a Tδ

α αα α

ζ ζ=( )( ) ⋅ + −[ ]( ) −1 1∆ (2.6)

The relation with the δ-value without temperature correction, δ*, is then given by:

δ δ ζ ζ ζ ζ= ⋅ + −[ ]( ) + −[ ]* 1 x a x aT T∆ ∆ (2.7)

Therefore, the effect of a temperature difference between the gas cells would be that the calibration

curve, in which the measured δ-value is plot against the “true” value, shows both a zero-offset and a

slope different from unity.

2.2 System Description

As explained before, the system we developed is a direct absorption spectrometer. This

paragraph describes consecutively the laser system and its operation, the optical set-up, and the

measurement procedures. In the appendix with this chapter, all equipment is listed.

2.2.1 Laser system

The absorption spectrometer uses an infrared laser source, the Color Center Laser (or Farbe

Center Laser; FCL), which is optically pumped by a krypton ion laser.

Chapter 2

32

2.2.1.1 Krypton ion laser

For pumping the Color Center Laser (Section 2.2.1.2) with the Li:RbCL crystal, the light of a

krypton ion laser is the most suitable. It’s wavelength (647 nm) has the highest excitation efficiency

for this crystal. We have been using a commercially available Lexel Krypton laser. This laser is water-

cooled. Power consumption is about 25 A at 220V. The light output is intensity stabilised by means of

a feedback to the current. Although its maximum output power is up to 3 W, the laser was operated

at a modest 700 mW, thus considerably extending its lifespan.

2.2.1.2 FCL

The Color Center Laser is a unique tunable source of continuous wave (CW), single mode,

infrared laser light. It combines wide tuning characteristics with a narrow bandwidth. It’s gain

medium consists of solid alkali halide crystals, which contain point defects or color (F) centers

(Burleigh 1994). These can in their simplest form be described as electrons trapped in a “hole” in the

alkali halide lattice: Their characteristics are determined by the type and number of dopant cations

the trapped electron has as its neighbours.

Laser action of a FCL is based on a four-level scheme (see Figure 2.4): The ground state (1) is

excited to (2) by absorption of light from a pump laser, after which rapid (10-12 s) non-radiative

relaxation occurs. The system is now in the so-called relaxed excited state (3, RES, stable for about

100 – 200 ns) and in practice it remains there until it is de-excited by the stimulated emission of

laser action. The state it decays to (4) experiences once again a very rapid non-radiative transition

back to the ground state (1), thus creating a population inversion between (3) and (4). These levels

are substantially homogeneously broadened, meaning that the positions of the energy levels of the

different active centers are fluctuating in time, due to interruptions of the dipole oscillations by

collisions (Milonni, 1988). This fact enables the laser to be continuously tunable over a wide range of

wavelengths.

Most laser active color center crystals need to be operated at cryogenic temperatures. The

first reason for this is to reduce or avoid the diffusional mobility of the color centers in the alkali

halide crystals that can lead to complex (re)combination of F centers and therewith diminishing laser

action, i.e., to avoid degradation of the crystal. The second reason is that cryogenic operation

ensures that the equilibrium population of state (4) is essentially zero (giving population inversion

with respect to state (3) and that the fluorescence quantum efficiency of the system is large. To

achieve and keep cryogenic temperatures for the 2 mm thick crystal, also when illuminated by a

pumping laser (up to a few watts), it is attached to a cold finger that is in contact with a dewar

containing liquid nitrogen (77 K).

Set-up

33

Figure 2.4: Typical energy level diagram for the laser action of color centers.

We have used a RbCl crystal, doped with Li+-ions built into a Burleigh FCL-20 series laser.

This active medium provides a continuous tuning range from 2.65 µm to 3.4 µm with an output

power that may exceed 20 mW. We have operated the laser routinely at about 12 to 15 mW. The

pump laser output power current is accordingly relaxed, resulting in a longer lifetime of the Kr+ laser

tube.

The laser system is able to lase at many different wavelengths. To ensure single frequency

operation and tunability, a number of elements is placed in the cavity (Figure 2.5). The first, coarse

tuning element is a gold-coated grating that acts as both a cavity end mirror and output coupler. It is

rotated using a stepper motor. The second element is an intracavity tunable etalon (ICE), consisting

of two Littrow prisms. It is used at Brewster’s angle to avoid reflection at the outside surfaces. The

air gap separation is controlled by a piezoelectric element. The third and finest tuning element is a

piezoelectric translator, which displaces the (other) cavity end mirror. To operate the laser on just a

single mode and to tune it completely continuously, the grating and the ICE transmissions are made

Chapter 2

34

to follow the cavity mode, whose frequency is in turn determined by the cavity length (the position

of the end mirror). The mode spacing is about 295 MHz. The maximum length of a continuous scan

is determined by the range of the piezoelectric controllers. This is 6 to 8 GHz (~ 0.25 cm-1) for the

end mirror piezo and about 90 GHZ (~ 3 cm-1) for the ICE piezo. In Paragraph 2.2.2 a more detailed

description of scanning the FCL will be given.

Figure 2.5: FCL cavity in CW frequency configuration.

The FCL has a line width of approximately 3 MHz. When scanning the laser, the etalon

chamber is evacuated to better than 10-3 mbar. The laser requires only occasional re-adjustments; in

practice, this is only needed when deliberate changes to the optical layout are made.

2.2.2 Scanning of the FCL

In order to scan (tune) the laser wavelength (frequency), the cavity length is adjusted by

applying a voltage to the end-mirror piezo actuator. At the same time the ICE and the grating are

made to follow the cavity mode in a feed-forward manner. The stepsize of a single grating step is

accurately known by calibration. The ICE, however, suffers from severe hysteresis and it is therefore

necessary to actively lock the ICE to the cavity mode, in a feed-back loop.

The cavity end-mirror is not used over it’s entire range, but rather returned to its original

position every ~295 MHz (the cavity mode spacing). This can be done without introducing a

detectable discontinuity in the scan. In contrast, when the ICE needs to be returned to its starting

position, the laser does not always return to exactly the same cavity mode (i.e., frequency). At this

point, the wavelength meter and/or the 8 GHz Free Spectral Range (FSR) spectrum analyser need to

be consulted in order to assure a continuous frequency scan. Fortunately, the laser can be tuned

Set-up

35

over more than 3 cm-1 before the upper limit of the ICE piezo voltage is reached, and this is more

than sufficient for our purpose. Scanning of the FCL has previously been described by Kerstel (1991),

and references therein.

The laser scanning is controlled by a personal computer. The application we use for this

purpose is written using the LabVIEW graphical programming language. The application also takes

care of recording, transfering and saving of the data.

2.2.3 Optical lay-out and set-up

The final optical layout is shown in Figure 2.7. Some other approaches we have tried are

described in Paragraph 2.7.

The system has been set-up on a dedicated optical table equiped with a clean-air laminar

flow hood. To further avoid dust contamination the table is protected with plastic curtains. Its

position in the room is chosen in order to minimise the transmission of floor vibrations to the table.

All windows and beam splitters are 1º or 2º wedged to avoid interference of the beams reflecting

from the front and back surface.

The output of the FCL is first split into two beams by means of a 90% beamsplitter. The

largest part of the power is directed to the wavelength meter, the ICE feedback detector (see 2.2.2)

and two external etalons (150 MHz and 8 GHz FSR). The wavelength meter directly measures the

wavelength of the output laser beam (with a precision of ±0.02 cm-1) and receives about 6 mW of

total laser power. The ICE feedback detector receives about 3 mW. Both external etalons need about

1.5 mW.

The remaining 10% (~ 1.2 mW) of the laser power is directed towards the experiment. At

present we have four gas cells in use. To minimise problems with absorptions of atmospheric water

vapour, each beam travels the same distance through air before arriving at the detector. Moreover,

power must be measured separately for each gas cell and the four power-measurement beams must

have the same length as the signal-measurement beams. To meet these requirements, the main

beam travels diagonally across the optical table while at four positions wedged uncoated windows

are positioned which pick off a few percent of the main beam (each typically 10 µW). Their positions

are chosen in order to make all of the path lengths equal. Because we pick off such a small part of

the main beam, it is possible to do so sequentially. It is not necessary to have exactly the same

amount of light for each cell and since there is sufficient light available, we are only restricted by

space and budget in the number of parallel measurement lines (gas cells).

For alignment purposes, an red (633 nm) He/Ne laser is used. By means of a flipping mirror

it is possible to overlap the IR and the red beam. Since the index of refraction is slightly different for

IR and red light and the main beam passes through a number of wedged uncoated windows, the

beams do not follow exactly the same paths: The angles at which the beams leave one of the optical

Chapter 2

36

components differ slightly. To correct for this it is necessary to place the wedged uncoated windows

for light pick off at alternating angles in the beam.

The set-ups following the split off of the main beam are equal for the four gas cells. A lens

with a focal length of 1 m focuses the beam in the middle of the gas cell (or, rather, at the entrance

hole to reduce beam cut-off). Subsequently, the beam is split again in (1) a beam (90% of the

power) that is directed towards the cell via two mirrors to be able to steer the beam in three

dimensions and (2) a beam (10%) that is led directly to an InAs detector. The light emerging from

the gas cell is focussed at the same detector that measures the laser power arriving at the gas cell

entrance. For both the signal and power beams, the same 50 mm focussing lens is used. Both beams

(signal and power) can be distinguished by modulation of their amplitudes at different frequencies

using separate optical choppers. The intensities of the beams can be recovered by using phase

sensitive detection, using two different digital signal processing (DSP) lock-in amplifiers (LIAs).

The gas cells are equipped with two gold-coated mirrors that are basically sphere segments

with a radius of 0.5 m. They are used in the Herriot scheme (Herriot 1964, Altmann 1981). Because

of the mirror shape and alignment, the cells refocus the light after every reflection. One mirror has

an entrance hole at 22 mm from the centre. The beam is led into the cell through this hole. A circular

pattern builds up in the cell. The beam leaves the cell again after 47 reflections (48 passes),

resulting in a 20.5 m path length. See Figure 2.6 for a visual impression. The intensity of the

outgoing beam is decreased, due to non-ideal mirror reflectivities. The ratio between the intensity of

in- and outgoing beams is given by Rn, with R as the mirror reflectivity (typically 98%) and n the

number of reflections. For example, for 47 passes and 98% reflectivity, ideally 39% of the light

passes the cell. In practice, the magnitudes of the power and signal beams are about the same when

arriving at the detector.

Figure 2.6: Open multiple pass gas cell. The beam of a red He/Ne laser on one of the gas cell mirrors

and the beams in air are shown, operated in the multiple-pass mode as described in the text. The

beams are made visible by condensing water in air produced with liquid nitrogen. On the mirror the

round spot pattern can clearly be observed.

Set-up

37

Careful alignment of the gas cells (distance of the mirrors and their tilt and correct steering of the

incoming beam) is absolutely necessary (1) to get the desired path length, (2) to avoid the loss of

light at the edges of the mirrors and (3) to avoid interferences associated with overlapping reflection

spots.

Figure 2.7: Lay-out of the optical system. The optical path of the four signal and four power beams is

very closely equal (in the present set-up about 267 cm).

2.2.4 Operation

Both external etalons are used in spectrum analyser (scanning) mode to monitor the single

mode performance of the laser. Their information is stored along with the spectra in the form of a

signal proportional to the mirror piezo voltage at which transmission through the etalon occurs

(Kerstel 1991).

The IR detectors are thermo-electrically cooled InAs photovoltaic devices with an active area

of 2 mm diameter. Their output signals (one for each gas cell) are amplified with home-built pre-

amplifiers. The output signal is used as the input of a DSP LIA to retrieve both the signal and power.

The DSP LIAs we have presently in use (EG&G 7265), are able to demodulate the incoming signal at

both the signal and power modulation frequencies as long as one of the modulation frequencies is

equal to the internal LIA oscillator frequency. This is achieved with an optical chopper that is able to

follow the imposed frequency. This double-modulation, one-detector, source compensation technique

reduces the effects of detector non-linearity and (temperature induced) responsitivity changes. In

addition, since we require only one LIA per gas cell, temperature drifts of the amplifiers are largely

Chapter 2

38

cancelled in the signal to power ratio. The choppers have typical modulation frequencies are around

1 kHz, the usual LIA settings are 200 mV full scale, 50 ms time constant and a dynamic reserve of

24 dB/oct.

As mentioned before, scanning of the FCL is performed by a LabVIEW application (National

Instruments). It offers the possibility, amongst others, to set the scan range, scan speed and

stepsize and then calculates the desired output voltages for the tunable elements based on earlier

calibrations. It also sets the laser to its desired single mode position prior to the beginning of the

scan (using the 8 GHz external etalon signal and wavelength meter readings) and it controls the

on/off-switch of the ICE feed-back loop. During the scan the LIAs store their output simultaneously

in their own internal 32k data point buffer memory. These buffers are divided into three parts: One

for the signal, one for the power and one for an external input (16-bit resolution A/D converter

input; e.g., for the external etalon signals). The LIAs serve as D/A converters as well by translating

the voltages set by the computer to output voltages needed for scanning the laser. Also the TTL

pattern for the grating stepper motor is generated by one of the LIAs. After completing one scan

(typically 2000 – 5000 laser steps), the memory buffers are read by the computer and written to file.

A scan with 8 MHz stepsize (giving ~5000 values over the selected spectral range) typically takes

2.5 minutes. Finding the single mode position and reading the data from the internal memory buffer

together takes typically one minute. Thus, one measurement series (typically 8 scans at this

resolution) takes about 30 minutes.

2.2.5 Measurement procedures

The gas cells are made of stainless steel, the tubes of glass and they have a volume of about

1 l. Attached to the glass tubes is a small compartment (~1 ml) separated from the main volume of

the cell by a valve. These compartments are filled with dry N2 prior to each sample introduction.

Using a 10 µl syringe, liquid water samples are injected through a silicon membrane (“septum”).

After retraction of the syringe the valve is opened, letting the water evaporate into the multiple-pass

cell. This results in a gas cell pressure of about 13 mbar (the room temperature saturated vapour

pressure is about 32 mbar). After exactly 5 minutes the valve is closed again. This procedure avoids

problems with the vacuum integrity of the septum and with freezing of the water during injection.

Before each sample introduction the gas cell and its septum compartment are thoroughly cleaned by

a pump-flush-pump cycle (using dry N2 at about 1.5 bar). In a typical measurement, 8 to 15

individual laser scans are recorded before a new sample is introduced. Stepsizes of about 8 MHz to

16 MHz are used. This way a measurement, including sample introduction and gas cell evacuation

takes about 45 minutes.

One cell is always reserved for the working standard, the others for calibration standards

and samples. The working standard and calibration standards are waters with a well-known isotopic

Set-up

39

composition with respect to the internationally accepted calibration material “Vienna Standard Mean

Ocean Water” (VSMOW, see also Chapter 1).

The signal attenuation is between 25 and 90% for each of the selected transitions and

natural water samples. The lines are pressure broadened to about 0.008 cm-1 (~ 240 MHz; HWHM).

To avoid cross-contamination of the gas cells they are separated with cryogenic traps made

out of glass. The traps are connected to a common pump line and simultaneously pumped.

2.3 Calculations

This paragraph describes the necessary steps for calculating the isotope ratios from the

measured absorption spectra, principally based on Equation 2.1 and 2.5. The further correction and

calibration steps will also be described. In the first step the raw isotope ratios are calculated from the

spectra, in the following steps correction for pressure differences and zero-point are made and finally

calibration and normaliztion are performed.

2.3.1 Raw isotope ratio calculations

The first step on the way to determining the isotope ratios is to correct the gas cell

absorption spectrum for laser power variations. This is done by dividing each gas cell spectrum S

(reference and sample) by its accompanying power spectrum P (measured at the gas cell entrance),

to calculate the absorbance Α. Αpart from a constant term, A is equal to the absorption coefficient α

of Equation 2.1:

ASP

and ASP

samplesample

sampleref

ref

ref( ) ln(( )( )

), : ( ) ln(( )( )

)ν νν

ν νν

= − = − (2.8)

where ν again represents the laser frequency. The center of a spectral feature z (i.e., one of the ro-

vibrational lines belonging to the isotopic species z = H16OH, H17OH, H18OH, or 2HOH) is given by νz.

For each spectral feature (line) in the spectrum the corresponding section of the sample

absorbance Asample is fit to the sum of the reference absorbance Aref and a quadratic base-line:

A A Rsample refz z( ) ( ) ( )ν ϕ ν β β ν ν β ν ν ν= ⋅ + + ⋅ −( ) + ⋅ −( ) +0 1 2

2 (2.9)

This yields for each isotopic species a set of constants (ϕ, β0, β1, and β2) that minimises the

sum of the squared residuals [R(ν)]2 for ν in a selected interval of datapoints around the line center

νz.. This procedure is depicted graphically in Figure 2.8.

Chapter 2

40

Figure 2.8: Spectral features of interest in the selected part of the spectrum. The other lines are

removed, the base-line sections are as long as possible to determine their position. The upper plot

shows the residuals of the best possible fit, the lower plot shows the reference and sample spectrum,

corrected for laser power fluctuations. “i” is a measure for the laser frequency “ν”.

Since the experimental frequency calibration of the spectra is not perfect, the exact positions

of the spectral features are re-determined for each laser scan. The range over which the sample

spectral feature is compared to the corresponding line in the reference spectrum is fixed and

Set-up

41

determined by the position of the neighbouring lines (chosen to minimise the effects of overlap). It is

always relative to the line center. The isotope ratio is now calculated from:

x x

a

δ ϕϕ

= − 1 (2.10)

This is analogue to Equation 2.5. The subscript a refers to the most abundant isotopic species,

H16OH, while x refers to one of the rare species, H18OH, H17OH, or 2HOH. As mentioned before, these

molecular concentration δ-values are for most practical purposes equal to the corresponding values

based on atom concentrations. An important requirement is that the temperature of the gas cells is

supposed to remain the same. A constant temperature difference, however, can be corrected for by

proper calibration.

One of the most important advantages of the data analysis procedure is that non-linearities

and/or irregularities (such as a cavity mode-hop) in the frequency scan of the laser have no adverse

effect on the quality of the fit of Equation 2.9. If one were to determine the line intensities by

performing a line profile (Voigt) fit to each individual transition, frequency scale errors may

propagate through the line profile fit into the final δ–value (even though these should ideally cancel

in the ratio of line profiles).

The application that we use for performing the fit is written in (CodeWarrior) Pascal.

At this stage, we have obtained the raw δ value, for which we will write δ*.

2.3.2 Pressure dependence correction

Changing the amount of water in both the reference and sample cells from 8 µl to 12 µl per

gas cell (corresponding to pressures between approximately 11 mbar and 16 mbar) does not result

in a significant shift of the measured δ-values as long as the water vapour pressure in the two gas

cells remains equal. The effect of changing the quantity of water is that the line width (and the line

shape) changes due to pressure (collision) broadening (Demtröder 1981), but since the same change

occurs in both the sample and reference spectra, the effect cancels in the line intensity ratio (i.e., the

parameter ϕ in Equation 2.9).

However, a pressure difference between the two gas cells does have a significant effect on

the δ–values determined from Equation 2.10, even when the isotopic composition of sample and

reference waters is the same. This is shown in the upper half of Figure 2.9. Such pressure

differences occur in practice due to our inability to inject the 10 µl water samples with an accuracy

better than approximately 0.1 µl. The effect is that the line widths in the sample and reference

spectra can be measurably different.

Chapter 2

42

-40

-30

-20

-10

0

10

20

30

-100 -50 0 50 100

δ(18O)

δ(17O)

δ(2H)

δδδδ (‰

)

8

9

10

11

12

-100 -50 0 50 100

Sam

ple

Cel

l Am

ou

nt

(µl)

∆∆∆∆ΓΓΓΓ////ΓΓΓΓr (‰)

Figure 2.9: Experimentally determined apparent δ-values (upper half) for the different isotopomers

and the amount of water in the sample cell (lower half), both as a function of the line width

difference.

Set-up

43

In the upper half of Figure 2.9, the measured shift in the apparent δ’s, with respect to the

situation with 10 µl of water in each gas cell, is plot as a function of the relative average line width

difference ∆Γ/Γr. Here ∆Γ = Γs−Γr, with Γs and Γr the average line widths of the observed lines in the

sample and reference spectra, respectively. The bottom half of Figure 2.9 shows the experimentally

determined relation of ∆Γ/Γr with the amount of water in the sample gas cell (the reference gas cell

always contains 10 µl). Although the pressure broadening coefficients are in general dependent on,

among other factors, the rotational quantum number and the isotopic make-up of the molecule, they

are not expected to be sufficiently different to explain our observations. In fact, we have not been

able to establish any difference in pressure broadening coefficients for the four ro-vibrational lines of

Table 2.1, based on the spectra we recorded at pressures between 10 mbar and 36 mbar. For water

vapour pressures near 13 mbar (10 µl) we find experimental pressure broadening coefficients that

for all 4 lines are equal within one sigma to (0.31 ± 0.01)⋅10-3 cm–1/mbar.

The shift in the apparent δ’s determined for both δ17O and δ18O varies appreciably with the

amount of water injected into the sample cell, while at the same time δ2H changes much less and in

the opposite direction. The cause of the apparent shift is different and can be understood by

considering the differences between the isotopomers. Both the H17OH and H18OH lines, and to a

lesser extent also the H16OH line, are relatively near to other lines in the spectrum. This makes it

necessary to limit the range over which the least-squares approximation of Equation 2.9 is made

(see also Figure 2.9), to the extent that a very significant portion of the wings of the lines is cut-off.

In other words: At the extremes of the fitting range, the line intensity is still significantly different

from zero. Obviously, the larger the vapour pressure, the larger the line width and the more serious

this effect becomes. If the line widths in the sample and reference spectra are equal (same pressure,

temperature and isotope abundance ratios in the two gas cells) the fitting procedure should not

suffer too much. However, a difference in line width will lead to a systematic fitting error. Since the

calculation of the δ-value involves the (super–) ratio of rare and most abundant isotopic species line-

ratios, the two systematic errors associated with the line-ratio determinations may (partially) cancel,

especially if the two fits (rare and most abundant isotopic species) are carried out over a similar part

of the line shape. Clearly then, this is not the case for δ(17O) and δ(18O) where the H17OH and H18OH

lines are more severely truncated than the H16OH line. For δ2H the situation is slightly different. Since

the 2HOH line is much better isolated with respect to the H16OH line, a sufficiently large section of

the spectrum can be used to perform the fit of Equation 2.9, and the systematic error mentioned

above remains very small. In this case, the shift of the apparent δ is mostly due to the error made in

the H16OH line-ratio determination.

The observed apparent shifts in δ-values suggests a correction to the measured value, δ*, of

the form:

Chapter 2

44

δ δ γ *= + ⋅−Γ Γ

Γsample ref

ref

(2.11)

The value of γ was determined experimentally: γ(H18OH) = –0.248(16),

γ(H17OH) = –0.330(8), and γ(2HOH) = 0.016(17) where the values in brackets indicate the standard

deviation (Figure 2.9).

As will be discussed later, we can reproduce the experimental differential pressure

observations by numerical modelling of the data analysis procedure, while assuming equal pressure

broadening coefficients for all of the ro-vibrational lines. We will then see that Equation 2.11 is not

complete yet. The fact that the peak intensity influences the level of line cut-off means that changing

the concentration of an isotopomer has the same effect on the corresponding line as changing the

total amount of water. The later derived relation will be used for the routine measurements.

The gas cell pressure differential is determined for each series of scans by measuring the

relative difference in the (average) line widths in the sample and reference spectra. The value used

is the mean of the median line widths determined from the consecutive scans within a series. The

relative difference is then used to calculate the correction to the measured δ-value, accordingly to

Equation 2.11.

At this stage we have obtained the δ-values, corrected for pressure differences.

2.3.3 Filtering and calculation of mean values

Accidental outliers (all values removed from the median by more than twice the absolute

deviation) are removed (rarely more than 2 out of 15). The average value of the remaining

measurements is reported as the final result, together with its standard error. If, however, a

significant trend in the consecutive δ-values is observed, the end-point of the best (linear) fit through

the individual measurements, back interpolated to the moment of injection (opening of the valve) is

used as the final result. Especially for (highly) enriched samples this trend analysis procedure

provides us with better results in a shorter measurement time. It is then namely unnecessary to flush

the cells repeatedly with sample until a totally stable signal is reached (due to memory effect, see

Paragraph 2.5.4).

At this stage from all series of pressure-difference-corrected-δ-values we have obtained an average

value and an indication of its precision.

2.3.4 Zero point adjustment

We find values that are non-zero if we measure a water sample against itself. This is

probably due to reasons of alignment (i.e., beam cut-off or detector alignment changing with laser

frequency). Thus, all of the cells have a certain offset that differs for each cell and isotope. The value

Set-up

45

of the offset is typically between zero and 10‰ and can have both a positive or a negative sign. In

order to adjust the zero point and thus deduce the true δ-values, we have to apply a correction in

the form of:

δ ω δ ω= + ⋅ +( ), ,x cmeas

x c1 (2.12)

Where ωx, c represents the offset, depending on the isotopomer and sample cell of interest. Note that

this correction does not only remove the offset, (such as simple subtraction would do), but also

influences the slope. This is similar as the temperature difference dependency from Equation 2.7.

To be able to make a reliable zero point adjustment, frequent measurements of the offset

(e.g., working standard against working standard) are needed. The correction has proven to be

stable in time, provided the optical alignment is not (deliberately) changed.

At this stage we have obtained the non-calibrated δ-values.

2.3.5 Calibration and normalization

The last step in the process of the calculation of the final result is the calibration of the entire

system.

One of the local standards is mostly used as the working standard in the reference gas cell.

Consequently, the laser-spectrometer values are initially referenced to this material. These have to

be converted to values relative to VSMOW, ideally using the laser determined value of the working

standard with respect to VSMOW (or vice versa). Note that this inherently takes care of the zero-

point adjustment of the laser spectrometric δ–scale. We can, however, also use the known values of

the working standard to make this conversion, after the zero-point adjustment is completed. In order

to calibrate the instrument, we have to measure a series of local water standards (preferably

spanning the total expected range of the series of samples) that are well-characterised with respect

to VSMOW by repeated mass-spectrometer analyses in our laboratory. Alternatively, international

calibration and reference materials (SMOW, SLAP and GISP) can be used to create the calibration

curve. For δ18O and δ17O the calibration curves show a very good linear relation. For δ2H

measurements, however, a quadratic correction must be applied for high enrichments:

δ ξ δ ψ δcalibrated = + ⋅ + ⋅( ) ( )# #1 2 (2.13)

Where δ# represents the uncalibrated δ-value.

The quadratic contribution is not significant, except for δ2H enrichments above ~5000‰. In

IRMS a similar phenomenon is observed caused by so–called cross-contamination (Meijer 2000).

Chapter 2

46

The slopes of the calibration curves are in most cases significantly different from unity: Laser

spectrometry usually under-estimates the isotope abundance ratios. The magnitude of the deviation

is often the biggest for δ2H and has an observed maximum of 4% of the value. After careful

alignment, however, it is often much closer to zero and the sign can even change. We therefore

believe that this deviation must be due to residual etalon fringes (interferences) in the optical

system.

As has become apparent over the years in numerous international ring tests (e.g., Lippman

1999, Araguas–Araguas 2000), IRMS-based measurements too often exhibit calibration curves with

slopes smaller than unity, and in particular for 2H the deviations found are sometimes even larger

than the maximum deviation found in the present laser system. A pragmatic approach to these

problems, in which the δ–scales are defined by a linear calibration using two different calibration

materials (e.g., SLAP in addition to VSMOW), has been generally accepted, and in fact is

recommended by the IAEA (Coplen 1988). The same solution is applied to our laser-based method

by the approach described above. It is reliable since the reproducibility of the measurements turned

out to be good over an extended period of time.

At this stage, the calibration (“stretch”) factors are known, and one can easily calculate the final

δ–values from the mean values after zero-point correction, obtained in the previous section. The now

obtained values are the final results.

Set-up

47

2.4 Precision and accuracy of laser spectroscopy

The LS system has proven to be able to measure isotope ratios in both the natural (Kerstel

1999) and enriched regimes (Van Trigt 2001a). The text in this paragraph is based on parts of those

publications. The reader should realise that the measurements presented for the natural range are

not the most recent ones. They serve as a demonstration of the procedures described in the

preceding paragraphs. More recent measurement results in the natural abundance range will be

presented in Chapter 4.

2.4.1. Measurements in the natural abundance range

We demonstrate the first successful application of infrared laser spectrometry to the

accurate, simultaneous determination of the relative 2H/1H, 17O/16O, and 18O/16O isotope abundance

ratios in water. The method uses a narrow line width color center laser to record the direct

absorption spectrum of low-pressure gas-phase water samples (presently 10 µl liquid) in the 3 µm

spectral region. It thus avoids the laborious chemical preparations of the sample that are required in

the case of the conventional isotope ratio mass spectrometer measurement. The precision of the

spectroscopic technique is shown to be 0.7‰ for δ2H and 0.5‰ for δ17O and δ18O (δ represents the

relative deviation of a sample’s isotope abundance ratio with respect to that of a calibration

material), while the calibrated accuracy amounts to about 3‰ and 1‰, respectively, for water with

an isotopic composition in the range of the Standard Light Antarctic Precipitation (SLAP) and Vienna

Standard Mean Ocean Water (VSMOW) international standards.

2.4.1.1 Experimental section

In order to calibrate the instrument, we measured the (IAEA) reference material GISP

(“Greenland Ice Sheet Precipitation”), as well as a series of local water standards that are well-

characterised with respect to VSMOW by repeated mass-spectrometer analyses in our laboratory

(see Table IV). The local standard “GS-23” was used as working standard in the reference gas cell.

Consequently, the laser-spectrometer (LS) values are initially referenced to this material. These have

been converted to values relative to VSMOW, using the laser spectrometrically determined value of

GS-23 with respect to VSMOW. This inherently takes care of the zero-point adjustment of the laser

spectrometric δ-scale.

In Figure 2.10 we present the resulting calibration curves for the three isotopic species. In

the case of 1H17O1H, the GS-32 local standard was excluded from the test. For all other water

samples (Meijer 1998):

1 117 18 0 5281+ = +[ ]δ δ( ) ( )

.O O (2.14)

Chapter 2

48

As the 2H-, and to a lesser extend the O-, measurements are afflicted with a large memory

effect (the influence of the previous sample on the current measurement), it turned out to be

occasionally necessary to inject 3 or more water samples before the measured δ-value reached its

final value. To minimise the influence of this memory effect on the calibration procedure, large steps

in δ-values between subsequent samples were avoided as much as possible. For the same reason,

Figure 2.10 includes data recorded both in increasing and in decreasing order of δ-value. In the

future, the gas cells may be operated at an elevated temperature in order to promote a quicker

water removal from the cells.

The calibration data of Figure 2.10 are fit to linear functions with variable slope. The RMS

value of the residuals gives a good indication of the over-all accuracy of the method, including all

effects of sample handling. The values are: 2.8‰, 0.7‰, and 1.3‰ for δ2H, δ17Ο, and δ18Ο,

respectively. The precision of the method is given by the standard error of the individual results of

one series of (typically) 15 laser scans. The current average values of these are: 0.7‰, 0.3‰, and

0.5‰, for δ2H, δ17Ο, and δ18Ο, respectively. In the case of 17O and 18O the precision can still be

improved by increasing the number of laser scans in one run (i.e., increasing the measuring time).

For δ(2H) the minimum standard error has already been reached at this point, indicating that the

precision in this case is limited by sample-handling errors, including memory effects and isotope

fractionation at the gas cell walls. In fact, extensive fractionation at the walls is to be expected, in

particular for hydrogen. However, such fractionation is only observable to the extent that the two

gas cells behave differently. If such is the case, injecting exactly the same water sample in both cells

will result in a δ-value significantly different from zero. This we do not observe. It should be noted

that fractionation between the liquid and gas phases of water is avoided by working at a

substantially lower pressure (13 mbar) than the saturated vapour pressure (32 mbar at room

temperature): all liquid water injected quickly evaporates inside the evacuated gas cell.

The slopes of the calibration curves are all different from unity: laser spectrometry under-

estimates the isotope ratios. It appears as if the sample is mixed with reference water (but not vice

versa, as cross-contamination would lead to a quadratic deviation, which is not observed, Meijer

2000). Although we have established that the sample introduction procedure cannot be blamed, we

have not yet been able to eliminate this residual effect (perhaps caused by memory effects in the

vacuum system).

Set-up

49

Figure 2.10. The calibration curves for a) δ2H, b) δ17O, and c) δ18O. The root mean square deviations

of the residuals are 2.8‰, 0.7‰, and 1.3‰, respectively.

Chapter 2

50

Table 2.2 SLAP δ-values (‰) (referenced to VSMOW).

Isotope Laser Spectrometera) Consensus Valueb)

δ(2H) -415.47 (0.85) -428.0δ(17O) -28.11 (0.23) -29.70δ(18O) -53.88 (0.37) -55.50

a) Based on 11 measurement series (or runs, each consisting of 15 individual laser scans) and

acquired over a one-month interval. The standard error is given between brackets.

b) Consensus value: recommended by the IAEA (Gonfiantini 1984). The δ2H value results from a

mixture of isotopically pure synthetic waters and is regarded to be correct in absolute terms. The

δ18O is the consensus value of 25 laboratories; the true value is likely somewhat more negative

(Meijer 2000). The δ17O value is based on the consensus δ18O value in combination with

Equation 2.14.

As has become apparent over the years in numerous international ring tests, IRMS-based

measurements too exhibit calibration curves with slopes smaller than unity, and in particular for 2H

the deviations found are often much larger than those of the present laser system. A pragmatic

approach to these problems, in which the δ-scales are defined by a linear calibration using two

different calibration waters (e.g., SLAP in addition to VSMOW), has been generally accepted, and in

fact is recommended by the IAEA (Gonfiantini 1984, Hut 1986). The same solution can be applied to

our laser-based method. Even more so, since the reproducibility of the measurements is rather good,

especially considering that the results of Figure 2.10 were gathered over an extended period of time

(about two months). This means that the system is now ready to be applied to the bio-medical

doubly-labelled water method to measure energy expenditure, as well as to the accurate

measurement of natural abundances, for which especially the δ2H determination is already

competitive.

The VSMOW-SLAP linear calibration and its results are summarised in the Tables 2.2, 2.3 and

2.4. In Table 2.2 the mean of the LS δ-values (referenced to VSMOW), that determined the scale

expansion factor, is compared with the respective IAEA consensus values for each of the isotopes. In

Table 2.3 the individual measurements for VSMOW and SLAP are presented, again referenced to

VSMOW and this time after linear calibration (i.e., the mean of these measurements equals the

corresponding IAEA consensus value). Finally, Table 2.4 confronts the LS results with the MS results

by comparing the current “best” values for a series of 7 water samples (including VSMOW and SLAP,

which define the linear calibration).

Set-up

51

Table 2.3: The results for VSMOW and SLAP, against VSMOW and scaled to the SLAP consensus

values as reported here in Table II, with in parentheses the standard errors (all in per mil points), as

well as the standard deviation.

Sample δ2H δ17O δ18O

VSMOW 1.17 (0.60) 0.49 (0.27) 1.25 (0.66)

VSMOW -1.63 (0.65) 0.25 (0.24) 2.11 (0.44)

VSMOW -0.07 (1.05) -0.22 (0.31) 0.35 (0.70)

VSMOW 0.23 (0.99) 0.29 (0.28) 0.70 (0.73)

VSMOW -0.09 (0.80) -0.09 (0.32) -1.00 (0.33)

VSMOW -1.41 (0.97) -0.28 (0.39) -1.66 (0.46)

VSMOW 1.30 (0.96) -0.66 (0.19) -1.82 (0.72)

VSMOW 0.49 (0.83) 0.22 (0.31) 0.07 (0.38)

Standard Deviation 1.07 0.38 1.40

SLAP -426.04 (0.47) -31.23 (0.18) -56.52 (0.33)

SLAP -426.21 (0.43) -30.00 (0.25) -56.35 (0.38)

SLAP -422.76 (1.55) -28.74 (0.28) -55.35 (0.43)

SLAP -426.30 (0.39) -29.52 (0.23) -55.55 (0.31)

SLAP -428.77 (0.29) -29.84 (0.22) -57.44 (0.52)

SLAP -429.92 (0.33) -30.73 (0.32) -55.73 (0.48)

SLAP -425.71 (0.80) -29.39 (0.38) -53.91 (0.46)

SLAP -430.83 (1.19) -28.61 (0.47) -55.70 (0.82)

SLAP -430.74 (0.51) -29.63 (0.55) -56.50 (0.56)

SLAP -432.60 90.39) -30.12 (0.28) -54.06 (0.54)

SLAP -428.11 (0.36) -28.88 (0.38) -53.38 (0.38)

Standard Deviaition 2.89 0.81 1.26

Chapter 2

52

Table 2.4: Laser spectrometry (LS) compared to mass spectrometry. The LS results are based on

between N = 4 and 11 δ-determinations (of 15 individual laser scans each), spread out in time over

a period of between 4 and 10 weeks. All values are expressed in per mil points. The standard error

of the mean values reported for the LS measurements is given in parentheses.

Mass Spectrometry Laser Spectrometry

Standarda) δ2H δ17O b) δ18O δ2H δ17O δ18O

VSMOW 0.0 0.0 0.0 0.0 (0.4) 0.0 (0.13) 0.0 (0.5)

SLAP -428.0 -29.70 -55.5 -428.8 (0.9) -29.7 (0.2) -55.5 (0.4)

GISP -190.0 -13.21 -24.76 -188.8 (0.3) -13.2 (0.3) -25.0 (0.5)

GS-23 -41.0 -3.36 -6.29 -41.4 (0.8) -3.3 (0.3) -6.7 (0.4)

GS-31 -257.8 -75.48 -137.3 -260.5 (0.4) -76.5 (0.9) -139.3 (0.2)

GS-30 -403.3 -127.55 -227.7 -405.4 (0.8) -128.0 (0.4) -232.5 (0.5)

GS-32 -91.5 --- -56.84 -98.6 (0.5) -46.9 (0.10) -58.0 (0.5)

a) The VSMOW and SLAP values for δ2H and δ18O are those recommended by the IAEA (Gonfiantini

1984). The reference material GISP has the consensus values: δ2H = –189.7 (1.1)‰ and

δ18O = –24.79 (0.09)‰. The Groningen GS local standards have been established by repeated mass

spectrometric analysis in our laboratory over a period of several years. GS-23 is a natural water;

GS–30, GS-31, and GS-32 are synthesised.

b) The δ17O values of those water samples that exhibit a natural relation between the 17O and 18O

abundance ratios (i.e., all except GS-32) have been calculated from: (1+ δ17O) = (1+ δ18O)λ, with

λ = 0.5281 (0.0015).

2.4.1.2 Summary of measurements in the natural enrichment range

We have shown that laser spectrometry presents a promising alternative to conventional

mass spectrometric isotope ratio analysis of water. In particular, the laser based method is

conceptually very simple and does not require cumbersome, time-consuming pre-treatments of the

sample before measurement. This excludes an important source of errors. Moreover, all of the three

isotope ratios, 2H/1H, 18O/16O, as well as 17O/16O (virtually impossible by means of IRMS), are

determined at the same time without requiring different (chemical) pre-treatments of the sample.

The precision of the method is currently about 0.7‰ for 2H/1H and 0.5‰ for the oxygen

isotopes. We have shown a calibrated accuracy of about 3‰, respectively 1‰. Since the calibration

data were collected over an extended period in time it is expected that more frequent calibration will

enable us to achieve an accuracy closer to the intrinsic precision of the apparatus. In addition, the

calibration procedure will be improved by the simultaneous measurement of more than one standard

water (i.e., for natural abundance measurements one would use two local laboratory standards, one

Set-up

53

close to VSMOW in isotopic composition, the other close to SLAP). In particular, the standards should

be chosen each at one end of the expected range of δ-values, not near to one end as is the case

here for δ17O and δ18O.

Currently, the throughput is limited to about one sample per hour, comparable to that of the

original, conventional methods when both δ2H and δ18O are determined and the sample preparation

time is added to the actual IRMS time. With modest improvements in the detection (faster amplitude

modulation and a shorter lock-in time-constant) this can probably be increased by a factor of two,

the final limiting factor being the evacuation and flushing of the gas cells. However, the throughput

is most easily increased by the use of multiple gas cells, allowing the parallel measurement on many

more than just one sample. Considering the very modest demands on laser power, relative to the

output power of the available laser system, the number of gas cells is only limited by budgetary and

space constraints.

2.4.2 Measurements in the enriched range as applied in the

doubly labelled water method

We demonstrate the feasibility of using laser spectrometry (LS) to analyse isotopically highly

enriched water samples (i.e., δ2H ≤ 15000‰, δ18O ≤ 1200‰), as often used in the biomedical

doubly labelled water (DLW) method to quantify energy metabolism. See Chapter 3 fore detailed

information on the DLW method. This application is an important extension of the possibilities of a

recently developed infrared laser direct absorption spectrometer. The measurements on highly

enriched, small-size (10 µl liquid water) samples show a clearly better accuracy for the 2H/1H ratio.

In the case of 18O/16O, the same level of accuracy is obtained as with conventional isotope ratio mass

spectrometer (IRMS) analysis. With LS the precision is better for both 18O/16O and 2H/1H. New is the

ability to measure 17O/16O with the same accuracy as 18O/16O. A major advantage of the present

technique is the absence of chemical sample preparation. The method is proven to be reliable and

accurate and is ready to be used in many biomedical applications.

2.4.2.1 Experimental section

In the following section we will first discuss the preparation of the standards that are used

for calibration purposes, as well as the unknown samples used in this comparative study.

Subsequently, we will describe the experimental procedures and techniques for the isotope

measurements.

Standards

The only reliable way of obtaining “absolute” isotope standards is by gravimetrical methods.

For 2H, accurate gravimetrical preparation of standards is possible, thanks to the fact that isotopically

pure 2HO2H and 1HO1H are readily available. In fact, the 2H/1H abundance ratio of the calibration

Chapter 2

54

materials VSMOW and SLAP are known absolutely by way of gravimetrical mixing (Hagemann 1970,

De Wit 1980, Tse 1980).

For 18O and 17O the situation is not so simple: Neither is it possible to obtain 100% pure 16O,17O, or 18O containing water, nor it is possible to know the isotope composition with a high degree of

accuracy, although some efforts toward this goal have been published (Baertschi 1972, Li 1988,

Jabeen 1997, Gonfiantini 1995). It is possible, however, to construct a dilution series of working

standards while maintaining a well-known, linear relation between the enrichment levels of the

different isotopes. We prepared our working standards for this study by gravimetric mixing of a

distilled water with a certified heavily enriched water (18O = 94.5 and 17O = 19.2 atom %) and

almost pure D2O (2H = 99.9 atom %), and using a calibrated balance (Sartorius Analytic). The

independent 17O enrichment of the standards is a novelty, required here to test the unique capability

of our LS system to measure δ17O in addition to the usual δ18O and δ2H (see Kerstel 1999). A range

of enrichments was created from one “mother mixture”, to avoid an accumulation of errors. The

weighing uncertainties yield uncertainties for the linearity of the isotope ratio scale that are in all

cases smaller than the measurement accuracy of either the IRMS or the LS instrumentation (see

Table 2.5). We will come back to this point in the discussion.

Table 2.5: Calculated values of the gravimetrically mixed enriched standards.

δ2H (‰) δ 17O (‰) δ 18O (‰)

TLW-0 -41 (1) -3.1 (1) -6.3 (1)

TLW-1 1273 (10) 28.9 (6) 97.8 (5)

TLW-2 2585 (20) 60.9 (10) 201.8 (18)

TLW-3 5217 (50) 125.1 (20) 410.3 (20)

TLW-4 10820 (100) 261.7 (40) 854.3 (40)

The values rely on the specified enrichments of the commercial starting material. Errors are worst

case estimates of the effect of weighing uncertainty in the mixing process and are given in units of

the least significant digit.

Unknown samples

As unknown samples we used 51 vials of blood of Japanese quails (Coturnix c. Japonica)

obtained in a validation study of the DLW method against respiration gas analysis (Van Trigt 2001c).

All blood samples were distilled on a microdistillation column. Among the samples were backgrounds,

taken prior to the administration of enriched doubly labelled water, initials with expected values of

δ2H ≤ 15000‰, δ18O ≤ 1200‰, and δ17O ≤ 350‰, and finals with isotope enrichments between

the initial and background values.

Set-up

55

Isotope measurements

We measured all samples using both IRMS and LS. Samples were regularly alternated with

our working standards in order to calibrate the instruments and check their performance. The order

of the measurement of samples and working standards in both systems was determined such that

large steps in enrichment (read: memory effects) were avoided.

The IRMS measurements were carried out in four short periods (5-10 days) between

February and July, 2000. The LS measurements were carried out in 16 days in July, 2000.

Isotope Ratio Mass Spectrometry procedures

All samples were prepared and measured at the Centre for Isotope Research (CIO) using

routine procedures and standard equipment. For each water sample, four glass microcapillary tubes

were filled, each containing between 10 and 15 µl of water. The capillaries were flame sealed

immediately after filling. The use of these capillaries was dictated by the available instrumentation

and was in no way essential to the method. To obtain the isotope ratios, the capillary tube was put

in an on-line vacuum distillation system, mechanically broken and cryogenically frozen into a quartz

vial. The Epstein-Mayeda equilibration method (Epstein 1953) was used to determine δ18O of the

samples: 2 ml of CO2 gas of known isotopic composition was added to the vial, which was

subsequently kept in a thermostated water bath at 25ºC for at least 48 hours. After this, the

isotopically equilibrated CO2 was removed for IRMS analysis and the remaining water was led over a

uranium oven at 800ºC to produce H2 (Bigeleisen 1952). The 18O/16O and 2H/1H isotope ratios of the

CO2 and the H2 gases, respectively, were determined using dual-inlet isotope-ratio mass

spectrometers: a Micromass SIRA 10 for CO2 and a SIRA 9 for H2. In this way, we obtained four

independent isotope ratio determinations for both isotopes and for each sample.

Laser Spectrometry procedures

A detailed description of the LS method is available elsewhere (Kerstel 1999, Kerstel 2001b)

In brief, we measured the gas-phase direct absorption spectrum from a water sample in the 2.7 µm

region, determined the strength of the absorption of the different isotopomers, and compared these

to the absorption strengths of a simultaneously recorded reference water spectrum. To record these

spectra, a single mode Color Centre Laser (Burleigh) was scanned over the range from 3664.05 cm-1

to 3662.70 cm-1 in about 2500 steps. During the scan, both the laser power after passage through

the gas cells containing the water vapour and the laser power before the cells was measured using

phase-sensitive detection with amplitude modulation at about 1 kHz. Currently we have four gas cells

available. These are equipped with multiple pass optics to achieve an optical path length of about

20 m. The cells are made of stainless steel (mirror holders) and a glass tube; their volume is about

1 l. They show a memory effect (i.e., contamination with previously measured water) that amounts

to up to about 5% of the difference in enrichment levels between two samples. This implies that

generally the first measurement after a large step in enrichment (for example, 2000‰ for δ2H and

Chapter 2

56

300‰ for 18O) must be discarded. We tried to avoid such large enrichment steps by taking care of

the sample injection order; to this end we used the expected values from the biomedical experiment,

in agreement with common IRMS procedures (where the 2H preparation system produces even

larger memory effects: see Calibration). The glass tube of the cell is equipped with a valve that has a

small (1 ml) chamber behind it, the injection chamber. The injection procedure was the following:

After removal of the previous sample by evacuating the cells, we flushed all four of the cells

simultaneously with dry nitrogen gas. Cross-contamination between the cells was avoided by

cryogenic traps between each gas cell and the vacuum pump. After filling the cells with 1 atm of

nitrogen gas, the injection chambers were closed. The cells were then evacuated again, while in the

meantime we injected 10 µl of liquid water samples with syringes through rubber septa into all four

of the injection chambers. After closing the main pump valves the injection chambers were opened

and the water evaporated, along with the nitrogen, into the main volume of the cells. The final

pressure was about 13 mbar, well below the saturation vapour pressure of water at room

temperature. The laser started scanning after a five-minute waiting period to ensure that all of the

water had evaporated. The entire sample introduction procedure took fifteen minutes. One gas cell

was reserved for the reference water; of the other cells, one contained a working standard (thus,

giving us a permanent check against standards over the entire measurement period), and the two

remaining cells contained unknown water samples. As an extra precaution, the reference was treated

in the same manner as the samples and refreshed after every measurement to ensure its isotope

ratio could not change as a result of slow mixing with external water or isotope fractionation effects.

The infrared absorption spectra of the waters injected into the four gas cells were measured

simultaneously. For each injection, 12 successive scans were recorded, each taking about two

minutes. A full measurement, including injection and removal of the sample, takes around

40 minutes. The sample throughput for the LS is, thus, currently about 4 measurements (samples

and/or working standards) per hour. All samples and standards were injected and measured (at

least) five times to collect some statistical data and to be able to remove measurements affected by

memory effects. The exact procedure for calculating the raw, uncalibrated, δ-values from the

recorded spectra is straightforward and is described elsewhere (Kerstel 2001b).

Set-up

57

Figure 2.11: Squares represent the (a) δ2H and (b) δ18O IRMS measurements after application of the

known corrections. The solid line is the normalization curve obtained in a linear regression analysis.

Also shown are the residuals (measured value minus fit). The broken line is a least-squares fit to the

raw measurements.

Isotope Ratio Mass Spectrometry calibration

Calibration for both of the IRMS machines was maintained by daily tests with local reference

gases (one at natural abundance, the other enriched) as well as with several local water standards,

in addition to the standards that were specific for this project. For H2, the H3+-correction was

measured on a daily basis and in the current range amounted to up to 12% of the value measured.

Further, a correction for cross contamination up to 0.5% of the value was applied, as described

previously (Meijer 2000). Both of these effects are thought to be well-understood and can be

Chapter 2

58

quantified independently. Therefore, these corrections, together with the conversion from machine

reference gas to the VSMOW standard, were applied before the usual scale expansion correction

(normalization). In the case of the oxygen isotope ratio, corrections were applied for cross-

contamination (smaller than 1%), and the water correction (for the amount of oxygen in the added

CO2 causing dilution of the original oxygen in H2O; between 10 and 20%). Again, these corrections

were applied before conversion to the VSMOW scale and the final scale expansion or normalization.

The scale expansion correction for the H2 and CO2 IRMS machines was similar to the one

recommended by the IAEA for the natural range between SLAP and VSMOW (Gonfiantini 1984, Hut

1986). However, in the current enrichment range, the usual VSMOW-SLAP normalization would lead

to a large (and inaccurate) extrapolation and was, therefore, not applied. Instead, we used our

series of 5 gravimetrically determined standards to define the scale in a linear fit with equal

weighting factors. Unfortunately, the δ2H measurements involving the least enriched standard had to

be rejected because of an excessive memory effect in the H2-gas preparation system. Figure 2.11

shows the IRMS measurements before and after application of the known corrections mentioned

earlier. The figure also gives the residuals of a linear regression analysis. The slope of this fit is the

scale expansion factor, which is presented in Table 2.6.

Table 2.6: Normalization factors for IRMS and for the different sample cells in the case of LS.

IRMS Cell I Cell II Cell III

ξ(H218O) •102 2.01 (8) 1.60 (7) 1.33 (7) 1.60 (9)

ξ(H217O) •102 -- 3.5 (2) 3.99 (2) 3.3 (2)

ξ(2HOH) •102 3.2 (2) 1.3 (3) 0.5 (3) 0.2 (5)

ψ(2HOH) •103 - 1.6(3) 2.6(3) 2.5(4)

The errors between brackets represent one standard deviation in units of the least significant digit.

δ ξ δ ψ δcalibrated = + ⋅ + ⋅( ) ( )* *1 2, with δ* the measurement value after initial

corrections (see text). The quadratic term applies only to 2HOH.

Laser Spectrometry calibration

In contrast to IRMS, LS does not require large corrections of the raw measurement values.

The only correction applied before scale normalization was due to the effect on the final

measurement of small pressure differences between the gas cells. This correction has been

described in detail in the literature (Kerstel 1999, Kerstel 2001b) and, with proper sample

introduction, amounts to no more than 2‰ and 6‰ in terms of the δ-values for the oxygen isotope

ratios (δ17O and δ18O) and δ2H, respectively. Note that this is much smaller (~0.1%) than the

corrections that were applied in the mass spectrometer case. Again, the gravimetric working

Set-up

59

standards were used to determine the correct scale expansion factors, now also for δ17O. It turned

out that for 17O and 18O, a linear normalization is sufficient, but for 2H a second order correction was

needed to reduce the residuals of the measurements at higher enrichments. The normalization

factors for the three sample cells differed slightly. For all three of the measurement cells, the

normalization plots and corresponding residuals are given in Figure 2.12. The scale expansion factors

are listed in Table 2.6, together with the corresponding IRMS corrections.

2.4.2.2 Results and discussion

From Table 2.6 it is evident that IRMS requires a still substantial scale expansion. For both18O and 2H, IRMS initially underestimates the true isotope ratios. The magnitude of the scale

expansion factor found here in the high enrichment regime is similar to the one found in the natural

isotope abundance range (VSMOW-SLAP normalization). Although this normalization has become

common practice, the underlying physics is not understood. That no quadratic component is

necessary to obtain a good fit in the normalization process may simply be due to the missing data at

the lowest end of the scale.

Despite the very different and conceptually much simpler measurement technique, LS turns

out to need a quantitatively similar normalization (see Table 2.6). Surprisingly, the scale expansion

factor for 17O is nearly twice as large as for 18O, whereas the opposite might be expected if residual

isotope fractionation effects were to blame (Meijer 1998). Moreover, fractionation effects are, in

general, much larger for 2H than for 18O and certainly when compared to 17O, are in apparent

contradiction to the data. Therefore, we strongly believe that the results indicate that our series of

gravimetric standards contain 2% to 4% less 17O than calculated from the specifications provided by

the supplier of the starting material. To a lesser extent, the same may be true for 18O. This should

not surprise us, considering the difficulty in determining the absolute oxygen isotope concentrations

(see Standards).

In any case, for the DLW application, the absolute value of the isotope ratios is not

important: the calculated energy expenditure depends on the ratio of initial and final isotope

concentrations (above background) and requires only a good linearity of the scale. The latter is

assured by the calibration and normalization procedure carried out here.

The normalization factors of the sample gas cells are sensitive to the optical alignment

causing small differences between the three sample gas cells. This is almost certainly due to residual

etalon fringes (interference effects) in the optical system that persist despite the use of

antireflection-coated, wedged optics and careful alignment.

Chapter 2

60

Accuracy

A good measure of the accuracy of the entire sample handling and measurement procedure

is the root-mean-square (rms) value of the residuals of the standards (i.e., calibrated measurement

value minus gravimetric value).

For the IRMS measurements on the working standards, the rms values of the residuals, as

they appear in Figure 2.11, increase in size with enrichment. For δ18O, the values increase from

about 1‰ to 3‰ over the range of enrichments studied here, whereas for δ2H, the rms values of

the residuals increase from 17‰ to 68‰ (note that the measurement of the lowest enrichment

standard was not included).

The rms values of the residuals of the LS measurements, as they appear in Figure 2.12, are

also increasing in size with enrichment. Their values range from about 1.5‰ to 3.5‰ for 18O, from

3‰ to 55‰ for 2H, and from 1‰ to 2‰ for 17O. Especially if one excludes the measurement at

the highest enrichment level (which appears to break with the trend established at the lower

enrichment levels), LS performs significantly better for 2H than IRMS.

For both IRMS and LS, all unknown samples are corrected and normalised as described for

the standards.

In Figure 2.13 we directly compare IRMS and LS, for all measured samples (standards and

unknowns). From the preceding, it may be clear that over the range spanned by the standards, the

two methods agree within their precisions. However, at the even higher enrichment levels

encountered in the δ2H measurements of the unknown samples, the LS method gives slightly higher

values than IRMS. This may indicate that IRMS, just as LS, needs a quadratic component in its

normalization of the δ2H scale in addition to the one already applied for cross-contamination.

Precision

The precision is given by the standard deviation (SD) of repeated measurements on the

same sample (standards as well as unknowns). Their values increase with increasing enrichments,

just as the rms values do. The SD of the IRMS measurements ranges from about 1‰ to 5‰ for

δ18O, and from 5‰ to 100‰ for δ2H. For the LS measurements, the range for δ18O is from 1‰ to

4‰ and for δ2H from 5‰ to 60‰. LS can also measure δ17O, and its precision ranges from 1‰ to

2‰. These are essentially the same numbers as those obtained in the previous section for the

accuracy, which indicates that the calibration procedure is not limiting the overall accuracy of the

method.

Set-up

61

Figure 2.12: Squares represent the (a) δ2H, (b) δ18O, and (c) δ17O LS measurements after application

of the known differential pressure correction. The solid lines are the normalization curves obtained in

a linear regression analysis (three, one for each sample cell, but overlapping at the current scale).

Also shown are the residuals (measured value minus fit).

Chapter 2

62

Figure 2.13: (a) δ2H and (b) δ18O values of all LS measurements vs the corresponding IRMS values

as well as their differences (residuals). Circles represent the measurements of working standards;

squares give the measurements of unknown samples. Each point represents the mean of repeated

runs (LS, 5; IRMS, 4) involving the same sample, the error bar gives the corresponding standard

deviation, and the solid line represents the line with unity slope (y=x).

Further improvements

In principle, the ability to measure δ17O with the LS system, could be used to extend the

DLW method to a triply labelled water (TLW) method. The idea is to use the known difference in

fractionation behaviour between 17O and 18O to estimate the fractional water turnover by means of

evaporation (as opposed to water loss due to, e.g., urine). This has been shown to work with tritium

as the third isotope, but this has not found widespread acceptance because of the radioactive nature

Set-up

63

of this isotope (Haggarty 1988). Unfortunately, however, we estimate that the required accuracy of

the oxygen isotope measurements is almost one order of magnitude beyond our current level.

Although the memory effect of the LS method is smaller than that encountered with H2-gas

production by reduction of water over uranium, as used in our IRMS laboratory, it is still limiting the

ultimate accuracy for δ2H, as well as δ18O, measurements, especially at high enrichment levels. We

expect that this effect can be reduced dramatically by moderate heating of the gas cells (up to 40˚C

or 60˚C). We are currently making preparations to do so.

The sample throughput can be further improved by automation of water injection and

evacuation sequence and/or by increasing the number of gas cells. The laser provides enough power

to add many more cells and this is relatively cheap when compared to the costs of an IRMS system.

The only preparatory step used is the distillation of blood samples prior to measurement. In

the IRMS sample preparation system, this is usually done in an on-line set-up, which can easily be

connected to our gas cells, as well. That would eliminate the extra labour of off-line distillation and a

possible source of errors. The degree of enrichment that can be measured with the LS method for 2H

is currently limited to about 15000‰. In biomedical experiments on small animals exhibiting very

high water turnover rates, initial enrichments for deuterium of up to 50000‰ are sometimes

encountered. With so much 2HOH present in the gas cell, the absorption of the corresponding

transition will make the sample optically practically black, leading to a serious decrease in accuracy

of the 2H/1H isotope ratio determination. However, by switching to a nearby and much weaker 2HOH

absorption, we should be able to extend our measurement range upward to values satisfying

biomedical requirements in all cases and with acceptable accuracy.

The most fundamental improvement would be the replacement of the FCL laser system with

a diode laser. This would not only have technical advantages, which would be expected to lead to

improved precision and higher sample throughput, but would also result in a more compact and

cheaper apparatus. We are currently investigating the possibilities of using such a diode laser.

2.4.2.3 Summary of measurements in the enriched regime

The LS system is a reliable tool for measuring the stable isotopes in water from biomedical

applications in a wide range from natural up to 15000‰ for δ2H, 1200‰ for δ18O, and 350‰ for

δ17O. The accuracy and precision of isotope ratio determinations with LS are comparable to those of

IRMS for δ18O and are clearly better for δ2H. Sample throughput of the LS apparatus (30 to 40

measurements per day) is comparable to that of our IRMS laboratory but can be increased easily and

at moderate cost. The biggest advantage of the new system is its conceptual simplicity and the

absence of chemical sample pretreatments that are necessary with the traditional IRMS method. Also

new is the possibility of measuring 17O, which conceivably may be used in a triply labelled water

method, once further improvements in accuracy have been made.

Chapter 2

64

2.5 Current status

In this paragraph, the limiting factors of the system and the causes for the mentioned

measurement uncertainties are described, together with some of the minor and major improvements

that can be made to the LS set-up.

The existing drawbacks of the current LS set-up can roughly be divided into three groups:

The laser (apparatus) related problems, the isotope (fractionation) related ones and the problems

with the memory effect of the system. Some relatively easy improvements to the set-up can be

made. These will especially reduce fractionation and memory effect.

2.5.1 Apparatus related

In most experiments, we have performed 8 to 15 subsequent scans in each series (separate

sample introductions) with about 8 MHz or 16 MHz step sizes. The results suggest that the limit in

precision (~0.6‰ for δ2H, ~0.5‰ for δ18O and ~0.3‰ for δ17O for natural samples and to 60‰,

4‰ and 2‰, respectively for enriched samples as described in Chapter 3) for both 17O and 18O may

not always be reached yet. In other words, performing more scans might slightly improve the single

measurement (series) precision of a series for the oxygen isotopes. Thus, the apparatus itself is the

limiting factor. The precision for deuterium measurements, however, is limited by fractionation

problems as will be discussed in the next paragraph. We have chosen to make this number of scans

as the best compromise between measurement time and precision.

The greatest limitations of the measurement system come from the color center laser (FCL).

Tuning through adjustments of the macroscopic elements in the cavity gives rise to amplitude and

frequency noise on the output. Therefore it is necessary to divide out the noise, using a separate

power detector. Many of the mechanisms, which are responsible for the output noise, are at least

dependent on temperature (but other variables may also be important). They effect the mode quality

of the laser beam and the characteristics of a scan and therewith the measurement results. The rest

of the set-up can be sensitive to the laser alignment as well. Beam–splitters with parallel surfaces

can cause optical interferences (“fringes”). Although all these have been replaced by wedged optics,

some residual fringes are sometimes observed, probably coming from the laser itself (caused by

feedback) or the gas cells. If the position or amplitude of these interferences changes in time (e.g.,

temperature induced), the measurement will be influenced. Gas cell alignment is stable, as is

detector alignment. Although the procedure to fit the recorded spectra is in first order approximation

insensitive to some of these effects, a dependence is observed. However, if all precautionary

measures are being taken, the system is very stable and routine measurements can be performed.

Set-up

65

The above limitations all influence the precision of the method. The speed with which

measurements can be done, however, is also limited by the apparatus. As described in Chapter 2,

the FCL needs to be scanned by tuning three different elements at the same time. With the present

computer interface the scan speed is limited to about 25 steps/s. A typical scan with a step size of

about 8 MHz thus takes about 3 minutes. Improvements in computer software and interfacing can

slightly reduce the time needed for a single scan. Moreover, the sample throughput could be

increased by automating the sample introduction and pumping procedure. It will then be possible to

build a continuously working system.

2.5.2 Fractionation related

In contrast to the 17O and 18O measurements, the data suggest that increasing the number

of scans within a series will not yield a higher precision for 2H abundance measurements. In the

latter case, the fractionation that occurs is the limiting factor, instead of the noise of the

measurement system. Fractionation can occur during or after introduction of the sample in the gas

cell, but we may assume that all gas cells behave in the same way, since their design and

preparation are the same. Possible cross-contamination of the different gas cells is effectively

prevented by the use of separate cryogenic traps for each cell. Consequently, fractionation must be

due to sample handling (and sample introduction). In the current measurement scheme, all samples

are removed and refreshed after each series, including the local standard in order to avoid any

problems with vacuum integrity of the reference cell. Not replacing the local standard after every

series could lead to an improvement in precision.

In order to be able to measure pure blood samples, an improvement could be made by

building a system to introduce the samples directly from their capillaries into the gas cells, without

using a syringe. This on–line distillation will eliminate the distillation and sample introduction steps

and might thus prevent any fractionation in this step. The variability in the introduced amount of

water must then be adjusted or corrected for.

Several factors can influence the mode quality and scanning behaviour of the FCL. Possible

factors are variability in temperature, humidity and air pressure, and vibrations of the floor/building

or the cooling water pump. These, but probably also other differences in the scan or beam

characteristics (e.g., single mode quality) cause differences in the scan to scan measured δ-values,

and thus influence the obtained precision within a series.

Obviously, the optical set-up is always aligned with great care. Besides the laser, the other

elements in the beam can cause problems too, for example by way of optical interferences

(“fringes”). As described before, this is avoided by using wedged optics everywhere. Furthermore,

drifts and uncertainties in δ-values due to vibrations or back reflections of optical elements, are other

possible sources of errors.

Chapter 2

66

Long term drift from the set-up might also cause a change in the measured values, thus

causing a lower accuracy over an extended period. Since there are so many factors that influence

precision, it is very hard to quantify their individual contributions.

For the spectral region we selected, it turns out that uncertainties increase considerably at

enrichment levels higher than about δ2H ≥ 15000‰, δ18O ≥ 1200‰, δ17O ≥ 1000‰. In this case,

the absorptions in the gas cell become too strong to be able to measure the intensity of the

transmitting light accurately (see Equation 2.2). In addition, for H218O and H2

17O increasing overlap

with neighbouring lines becomes more problematic. These high enrichments are sometimes used in

biomedical applications. To be able to measure δ2H in samples with such high enrichments, we can

use the two small lines (#4 and #6 in Table 2.1). Their intensity at natural abundance levels is low

enough to permit a 50-fold increase.

2.5.3 Cell offsets

Extensive isotope fractionation effects for adsorption-desorption processes at the walls are to

be expected, in particular for hydrogen. However, such fractionation is only observable to the extent

that the two gas cells behave differently. Only due to such a difference, injecting exactly the same

water sample in both cells (with the same sample history) would result in a δ-value significantly

different from zero (“offset”). As described before, we do indeed observe offsets between cells, but

since we do not find a fixed relation between the 17O and 18O offsets (as one would expect if

fractionation effects are the cause) it can be excluded. Thus, the cause of these offsets must be

something else. Moreover, the fractionation of 2HOH would likely be about 8 times larger than that of

H18OH as it is in many equilibrium processes (Chapter 1). Consequently, the precision for 2H would

be 8 times worse than for 18O, but that is not observed. Both observations proof that the gas cells do

not behave differently from each other as far as their wall-fractionation is concerned. As mentioned

before, we have reason to believe that different optical alignments are the reason of the observed

offsets. It should be noted again that fractionation between the liquid and gas phases of water is

avoided by working at a substantially lower pressure (13 mbar) than the saturated vapour pressure

(32 mbar at room temperature): All injected liquid water quickly evaporates inside the evacuated gas

cell.

In the case of 17O and 18O, the precision can still be improved (although not by much) by

increasing the number of laser scans in one run (i.e., increasing the measurement time). For δ2H the

minimum standard error has already been reached at our normal working conditions, indicating that

the precision in this case is limited by other errors instead of the intrinsic precision of the apparatus.

These can be accounted for by differences in the (long term) sample history of the different cells,

which can after all introduce different behaviour of the cells. This so-called memory effect will be

discussed in the next paragraph.

Set-up

67

The introduction procedure could cause the accuracy to become worse than the

measurement precision as well, if, for example, the injection syringes introduce a memory effect or if

the vacuum integrity of the septum is not perfect. Since these effects, if they exist at all, are of a

highly variable nature, it is hard to quantify them. We do not have evidence, however, that they

would be limiting at all.

2.5.4 Memory effect

In contrast to what is discussed in the previous paragraph, the gas cells will not behave

equally in the case that the sample history of the sample and reference cell has been different.

Inherent to the nature (“sticky”) of the water molecule, the LS measurements are inflicted with a

serious memory effect, in particular in the case of δ2H. We can define the memory effect as the

interaction of the newly introduced sample with water that remained in the gas cell after the

preceding measurement (mostly adsorbed on the walls of the gas cell). It basically leads to a mixing

of “new” and “old” water in the gas phase of the sample cell. The stickyness of the water molecule is

also the reason that attempts to measure the isotope abundance ratio of a water sample directly

using IRMS, were only marginally successful (Wong 1984). In order to reduce this problem in our

set-up, we make sure that no large steps in isotope enrichment of the samples is made in successive

measurements.

The adsorption of water on the walls of the gas cell is referred to as physisorption (Pulker

1984). From Figure 2.14, it appears that two distinct pools of remaining water (despite the pumping

and flushing procedures) can be distinguished, which mix or exchange isotopes with the new sample

at different time scales. We propose the following two mechanisms (which act at different time

scales). The first mechanism is the fast mixing with adsorbed water on the walls. It is a physical

process and the mixing with the “new” water occurs instantaneously on the time-scales of repeated

measurements and can be seen in Figure 2.14 as an offset of the first measurement in each series,

but it is mostly pronounced in the first series. Although not shown in this figure, the same process

occurs for 2H and 17O. The second mechanism that can be observed in Figure 2.14 is a slower

process acting on a time scale of hours. In our opinion, it must be due to the mixing with less

accessible, adsorbed, water molecules. It can be recognised in Figure 2.14 by the gradual rise

(trend) of the subsequent scans during the first measurement series. This mechanism is also

observed for all of the three isotopes.

The difference between both processes might be explained by assuming that a number of

molecular layers of water are adsorbed on the walls of the gas cell. These behave as a rigid structure

and only the upper layers are easily accessible and therewith available for the fast exchange. The

deeper layers must then be responsible for the slower processes.

Chapter 2

68

Figure 2.14: Repeated measurements of δ18O of VSMOW in time. The time axis is approximate. Note

that after the fourth series, the sample was left in the cell overnight and the time-axis is broken.

With each new series fresh sample and reference water was injected. The previous sample was

TLW–4: δ18O ≈ 850‰.

From Figure 2.14 it is clear that (for 18O) the memory effect has almost disappeared after

about 8 subsequent sample introductions. The same is true for 17O. Consequently, due to the slower

mixing mechanism with the less accessible layers, it is not sufficient to introduce a sample and

remove it immediately: Some waiting period (hours) must be respected for the system to reach full

equilibrium, but overnight equilibration is favourable (the right hand side of Figure 2.14).

Measurements suggest that the amount of water that remains in the cell, even after thorough

evacuation is about 7% of the 10 µl sample size that is most often employed. Indications exist that

the pumping procedure removes some water from the surfaces: Immediately after opening the gas

cell’s injection valve a peek in the gas cell pressure is observed. However, within seconds the

pressure drops to the final cell pressure. Probably some of the sample has found a free hydrophobic

position at one of the inner surfaces of the cell. Changing the glass tube of the cell, before making

the large step in enrichment has showed qualitatively that the memory effect is (also) caused by

adsorption onto stainless steel and not to glass adsorption only. It is in our set-up not possible to

0

10

20

30

40

50

60

0:00 1:00 2:00 3:00 20:00 21:00 22:00 23:00

δδδδ18O

(‰

)

Time of measurement (h)

Set-up

69

measure directly whether the glass plays an important part as well, but due to its material properties

this can be expected.

We propose an additional (third) mechanism for the memory effect for 2H. This involves a

chemical process, namely the exchange of hydrogen (and deuterium) atoms of the sample water

with the cell walls. The glass of which our gas cells are made of, has Si–O–H groups at its surface

and the hydrogen atom is exchangeable with a 1H or 2H atom of sample water, thus introducing an

additional memory effect. This mechanism acts on longer time-scales than the physisorption

processes, probably since the binding sites are not easily accessible for the water vapour of the fresh

sample (covered by layers of adsorbed water). This process is referred to as chemisorption. In

Figure 2.15 it is not easy to distinguish it from the physisorption process, but it is illustrated by the

fact that a larger number of series shows a significant memory effect compared to Figure 2.14.

Figure 2.15: Repeated measurements of δ2H of VSMOW in the same series as presented in

Figure 2.14. The previous sample was TLW–4: δ2H ≈ 10800‰. There is no evidence that the

memory of the cells has disappeared, even after eight subsequent sample replacements.

To be able to compare the behaviour of the memory effect of the two isotopes in more

detail, the individual measurement values of δ2H were divided by those of δ18O. See Figure 2.16. In

0

200

400

600

800

1000

1200

0:00 1:00 2:00 3:00 20:00 21:00 22:00 23:00

δδδδ2H

(‰

)

Time of measurement (h)

Chapter 2

70

the first series, δ2H/δ18O increases faster than the enrichment ratio of the previous sample

(δ2H/δ18O ~ 13) would suggest. However, after a few series, the ratio approaches the expected

value of 13. Within each series, the mixing ratio declines, indicating that the fast exchange proces for

δ2H decreases faster than that of δ18O. After the sample was left in the cell overnight, the mixing

ratios have (on average) values around the expected value, but the mixing ratio increases within a

measurement series. This is an indication for the slower mechanism caused by chemisorption, the

role of which becomes significant now the fast initial mixing is completed.

Figure 2.16: Ratio of δ2H and δ18O for the measurements in Figure 2.14 and 2.15. First, δ2H

increases relatively slower than δ18O. After the overnight waiting time, however, the increase in δ2H

is stronger. The horizontal line is indicating the enrichment ratio of the previous sample. The scatter

becomes larger in time, since the measured δ-values become close to zero.

In order to account for memory effects, the data analysis software checks whether the

subsequently measured δ–values show a clear trend. If such a trend is stronger than certain limiting

conditions, it is accepted as being real. A linearly back–extrapolated value to the moment of injection

is accepted as the series result, instead of the mean of the measurements in the series. This has

proven to yield better values, but the very fast component of the physisorption can not be corrected

for. Therefore, the first measurement after an enrichment step must be neglected after large

enrichment steps. However, it can be used to separate the two memory effects caused by

0

5

10

15

20

25

30

35

40

0:00 1:00 2:00 3:00 20:00 21:00 22:00 23:00

δδδδ2H

/ δδδδ1

8 O (

‰)

Measurement time (h)

Set-up

71

physisorption. In Figure 2.17, the natural logarithm of the back-extrapolated values of each series is

taken, and plotted against the series number. For 18O the series values reach values that do not

significantly deviate from zero after 1 or 2 series and the decrease is probably logarithmic. For 2H,

initially a similar decrease is observed, until the chemisorption effect gets a significant influence. Due

to this additional mechanism, the linear back-extrapolation does not work as well as for 18O. A

second process seems to become the limiting step.

Figure 2.17: Natural logarithm of the initial value of each series as calculated from linear back-

extrapolation. For 18O (squares), one process exists, for 2H (circles) a second process becomes

limiting after a few scans.

To reduce this complex memory problem, we apply a hydrophobic coating that is applicable

to both glass and stainless steel. The coating (commercially available, PS-200) contains molecules

with a polar head, which form covalent bonds to the silanol groups of the glass and the polar sites of

the stainless steel surfaces. The long apolar tail makes the coated surface hydrophobic. Application

of the coating only involves cleaning, shaking and rinsing steps of the material with readily available

chemicals and means. To our best knowledge, this is the best hydrophobic coating that is easy

applicable to both glass and steel. The manufacturer gives no further specifications, but for water in

liquid form we can visually observe an enormous effect of its application on a glass beaker.

-6

-4

-2

0

2

4

6

8

1 2 3 4 5

ln δ18O

ln δ2H

ln( δδδδ

-val

ue)

serie #

Chapter 2

72

Despite of the hydrophobic coating we still observe a memory effect in our measurements.

In fact, it is only decreased to about half of the magnitude without coating. It was shown, by

replacing an entire gas cell tube, that this is especially due to water adsorption at the stainless steel

parts of the cell. The effect is again more severe for 2HOH than for the oxygen isotopes, partly due

to the fact that its natural range is bigger and high enrichments are more common here, but

probably also to the fact that deuterium is actively incorporated (exchanged) in the surface of the

cell.

With increasing temperature, all of the exchange reactions are expected to speed up

(Deyhimi 1982, Morrow 1991). Working at elevated temperatures can thus reduce the equilibration

time of the physisorption and process, making less flushing procedures needed. Moreover, by

elevating the temperatures the evacuation procedure might become more efficient, thus leaving less

water behind in the cell. In addition, the chemisorption exchange process is also expected to speed

up, thus taking less time to fully equilibrate.

In the present set-up, the remaining memory effect is under control if we are carefull with

the order in which the samples are introduced. The largest step in enrichment that can be made

without flushing the cells with sample first, is estimated to be in the order of 2000‰ for δ2H and

500‰ for δ18O when already working in the highly enriched regime. When working in, or just above,

the natural abundance range, the largest steps that can be made are in the order of 200‰ for δ2H

and 50‰ for δ18O. These values differ from each other, since the errors (caused by memory effect)

must be compared to the measurement precision.

Again, one should keep in mind that in traditional IRMS sample preparation systems

(especially for 2H) severe memory effects occur as well. Still, the practical accuracy of the LS is

limited by the memory effect.

Note again that memory effects of both kinds would be totally unimportant for the

measurement result as long as the cells behave equal and have the same sample history. All

fractionation effects will then cancel out. In practice, however, the reference cell will hold the same

water over an extended period of time, while the contents of the sample cells often change.

2.5.5 Interference with other species

From the HITRAN 1996 database (Rothman 1998) we know that almost no other natural

occurring molecules absorb in the chosen spectral region. The only exception is 12C16O2, which shows

an absorption profile at 3663.851 cm-1, very close to a line of 2HOH (3663.842 cm-1, #7). The CO2

line has an intensity of 1.0.10-21 cm.molecule-1 (compared to 1.2.10-23 for the 2HOH line) and has

therefore in normal air (which contains ~2-3% H2O and ~0.04% CO2) about the same intensity as

the 2HOH line. Thus, we have to be careful to avoid contamination of CO2 in the gas cell. On the

other hand, if we calculate the maximum possible amount of CO2 in a typical LS water or blood

Set-up

73

sample, it is not a problem whatsoever (about three ordersof magnitude weaker line). The existence

of this CO2 absorption line should be kept in mind when we start injecting blood samples for

biomedical purposes in the near future.

2.6 Numerical simulations

To get an indication of the reliability and robustness of the described approach and

calculations, we have tested the total data analysis procedure on simulated data. To this end we

used synthesised sample and reference spectra. These numerical simulations let us easily isolate the

various possible sources of errors and may enable the identification of the physical effects that cause

the measured δ’s to deviate from the true values. In the next paragraph the influences of spectral

overlap will be discussed, the differential pressure effect and base-line and noise, determined by

simulation of the absorption spectra.

2.6.1 Spectral overlap

In order to investigate the effect of partially overlapping spectral features (lines), absorption

spectra were calculated with the line parameters of Table 2.1 (and the other lines present, see

Figure 2.2). The absorptions were simulated by a Voigt line profile (Whiting 1968) with a total half-

width-at-half-maximum (HWHM) of 0.008 cm-1 and a 0.0053 cm-1 HWHM Gaussian Doppler

contribution. These are typical values for the spectra as routinely measured. All of the line intensities

in the reference sample, as well as the intensity of the H16OH line in the sample, were kept constant,

while those of the other lines in the sample spectrum were systematically and individually changed to

simulate samples with a range of δ2H, δ17O and δ18O values. No noise was added to the synthesised

spectra. The results show that the input δ-values are very well recovered by the data analysis

procedure. The observed deviations ∆(δ) are small and proportional to the δ – v a l u e :

δ = δ∗ + ∆(δ) ≅ δ∗(1 − χ), where δ∗ represents the recovered δ–value. These ∆(δ) shifts reach values of

∆(δ2H) = –16‰ for δ2H = 10,000‰, ∆(δ17O) = –2.5‰ for δ(17O) = 1000‰, and ∆(δ18O) = –1.2‰

for δ(18O) = 1000‰. In principle, these corrections should be applied to all measurements, but since

they are small compared to other corrections and a cell-specific stretching is needed anyway, it can

be included in the stretching factor. Further, due to the proximity of the H17OH line to two smaller2HOH lines, and their overlap with the H16OH line, a (significant) cross-correlation between the

experimentally determined δ17O, δ18O and δ2H values is expected. Fortunately, the simulations show

that the data analysis procedure is quite insensitive to this effect. The largest effect is seen in the

simulations for δ2H, but even then the δ17O and δ18O values react to a change in δ2H from 0 to

10,000‰ with a shift of only 0.2‰ and 0.3‰, respectively. This is insignificant with respect to

other sources of error that play a role at such large enrichment levels.

Chapter 2

74

In conclusion it can be said that the fitting procedure is reliable and gives a good reflection

of the true values.

2.6.2 Differential pressure effect

The pressure dependence of the calculated δ–values was also simulated, at first for identical

sample and reference waters. As expected, no effect is observed of changing the Lorentzian

component of the line profile by ±20% (changing the total line width by roughly ±10% from

0.008 cm−1 HWHM), as long as the line widths in the sample spectrum are the same as the

corresponding line widths in the reference spectrum. The fitting procedure is thus insensitive to the

exact amount of water, which is injected. However, varying the line widths in the sample spectrum

by an amount ∆Γ=Γs−Γr, while keeping the line widths Γr in the reference spectrum fixed (thus

simulating different amounts of water in different cells), changes the calculated (apparent) δ-value.

The changes are in good agreement with the experimental observations (Figure 2.9).

-40

-30

-20

-10

0

10

20

30

40

-100 -50 0 50 100

δ(18O)

δ(17O)

δ(2H)

δ (

‰)

∆Γ/Γr (‰)

Figure 2.18: Dependence of the apparent δ-values from the line width difference between the

sample cell and the reference cell, derived from numerical simulations.

Set-up

75

Figure 2.18 shows the simulated slopes ∂(xδ)/∂(∆Γ/Γr) for the three isotopes. In this case,

both sample and reference gas cells contain water of identical isotopic composition. The calculated

slopes of Figure 2.18 are in reasonable agreement with the measured slopes of Figure 2.9. See also

Table 2.7.

In addition, the differential pressure induced δ-shifts turn out to be dependent on the

amount of isotopic enrichment. Since the samples in practice are occasionally strongly enriched, this

effect may be important. The corrections of Figure 2.18 were therefore re-calculated with simulated

spectra of strongly enriched samples (up to 1000%, 1000%, and 10,000‰ for δ17O, δ18O, and δ2H,

respectively). The changes in the slopes ∂(xδ)/∂(∆Γ/Γr) turn out to be proportional to xδ. Moreover,

the differential pressure correction approaches zero for an isotope-free sample, for which xδ = -1.

Thus: ∂(xδ)/∂(∆Γ/Γr) = γ⋅(1+xδ). As can be seen in Table 2.7, the simulated values of γ agree

reasonably well with those determined experimentally.

2.6.3 Realistic base-line and noise

In order to investigate the effect of (detector) noise and residual base-line modulations

(due to optical interferences), experimental empty gas cell spectra were added to the synthesised

spectra. The addition of realistic noise enables the determination of the intrinsic precision of the

method or apparatus. That is, without taking into account external effects, such as temperature

drifts, sample introduction errors, and isotope fractionation due to wall adsorption. Inclusion of the

experimentally observed base-line modulations lets us calculate the δ-shift, βcalc, based on this

account only (see Table 2.7). Note that these simulations are based on real measurements,

including all problems with the laser and optical system. The values for β can therefore be

considered as a reliable indication (value changes with short term alignment) for the values to be

expected. From Table 2.7, it can be seen that typical values for the offset, only due to laser and

optics, are around 1‰. Comparable values are always found in experiments, again indicating that

the cell-offset is not an isotope related phenomenon. Instead, alignment is very important for

reducing its absolute values. The typical uncertainties in this number, the standard deviation, are

up to 1‰ for the given set of data. This shows that noise is limiting the performance.

2.6.4 Round up

The results of these exercises can be summarised as follows:

δ β δ χ γ δ= + ⋅ −( ) − ⋅ +( )* *1 1∆ΓΓr

(2.14)

Chapter 2

76

where δ is the true δ-value of the sample (with respect to the particular reference used in the

measurement) and where δ* represents the measured, apparent δ-value of the sample. The

numerical values of the coefficients β, χ and γ are summarised in Table 2.7. For the correction of

the measurement results the latest experimentally determined values of the zero-offset β (since it

changes with alignment) and χ (since it can not be distinguished from the stretch factors) were

always used, but the calculated values of γ (since it is an intrinsic correction of our approach that is

quantitatively understood) were applied.

Table 2.7: Calculated correction coefficients

δ2HOH δH17OH δH18OH

βcalc -2.2 (8)·10-3 -0.1 (6)·10-3 -0.4 (6)·10-3

γexpt 0.016 (17) -0.330 (8) -0.248 (16)

γcalc 0.066 -0.270 -0.212

χcalc 1.6·10-3 2.5·10-3 1.2·10-3

The values in brackets represent one-sigma errors in units of the last digit. The superscripts “calc”

and “expt” refer to calculated and experimental determined values.

The intrinsic precision of the method is given by the standard deviation of β, which is

dependent on alignment. The best accuracy is determined by the values of χ and γ.

2.7 Other attempts to improve precision and accuracy

In this paragraph, some of the different set-ups and approaches that were tried will shortly

be described. These were not successful enough to integrate into the current system, but still worth

mentioning. Only more fundamentally different ideas are described here and not the regular

developments or automatisation or small modifications in, for example, settings in software or the

electronics. Most often, the results of the efforts to different approaches turned out to be not good

enough for our demands on precision. The mentioned attempts are not necessarily in chronological

order.

First, we have started with two gas cells. All of the early set-ups were too bulky to give room

to two more cells. Later, the focus was more on building a compact apparatus. The original idea to

split the main beam in 8 beams of equal intensity was to use three consecutive 50% beam splitters

(in total 6 beam splitters were used). The amount of light available for each gas cell was much

higher in that set-up, but interferences occurred: Wedged 50% beam splitters were not present.

Set-up

77

Moreover, a difference in the beams would exists as some were more often reflected, while others

had a higher number of transmissions through optical elements. Since the light intensity needed is

not a limiting factor, we have later chosen for a serial set-up to circumvent these problems, and thus

providing the possibility to easily enlarge the number of cells.

Originally, we scanned the laser over a spectral region at slightly higher wavelengths than

the section we have finally chosen. Since it appeared not to be necessary to use the stronger H18OH

absorption present in that section, we changed to the currently used region. The advantage is that

this spectral section is shorter and therefore it is easier to scan the laser neatly over the lines.

Originally, we used one chopper for the entire set-up, and a separate detector for each

power and signal channel. Positioning the elements was much easier in this set-up: No choppers are

needed close to the gas cells and the position of the power detectors is free. However, it turned out

that it was needed to cancel or reduce (temperature induced) responsitivity changes from the

detectors by dividing signal and power from the same detector, making separate optical choppers a

necessity. Stabilisation of the FCL output by a feed-back with the Krypton ion laser output power did

not work either, because of the same reason: If one detector signal was kept stable, the others were

not. The same problem arose again when trying to stabilise the FCL output by the use of an acousto-

optic modulator (AOM) and an electronic feed-back loop. On top of this the AOM introduced

additional problems, such as feed-back into the laser, interferences and a change of the polarisation

of the light. All ideas of stabilising the power of the laser beam were therefore rejected. It turned out

that dividing each gas cell signal separately by its own input power measured on the same detector

is the best solution.

The amount of water in the cells has been varied. It is possible to reduce the amount of

water to 3 or 5 µl, however with some loss of precision (Tinge 2001). The attempt to use more than

25 µl water (saturated) introduced problems with condensation of water vapour at the mirrors.

Another attempt was to remove the (10 µl) water sample periodically from the vapour phase

by freezing it with liquid nitrogen or a Peltier element. In this way, it would no longer be necessary

to scan the laser. Instead, the FCL could be put and kept on top of an absorption line and, by

consequently removing and re-introducing the water, isotope ratio measurements could be made.

However, the freezing of the water lasted too long and the results were not good at all (not

surprisingly since we know about the problems with the memory effect). The needed temperature

for efficient freezing was even lower than –40ºC, since the vapour pressure of ice is still too high at

moderate temperatures.

We tried to place the power detector for following the power changes due to the ICE

modulation inside the tuning arm chamber. The signal of this detector is used to electronically lock

the etalon to the cavity (see Paragraph 2.2.2). In principle, this change could improve the quality of

the laser scan, especially at frequencies where strong water absorptions occur, since it removes the

Chapter 2

78

influence of atmospheric absorptions. This modification seems to work, but has to be tested more

extensively.

The detectors have AR/AR coated wedged windows in order to prevent interference fringes.

With flat windows, the reflection of the second surface of the window back to the first and back

again could interfere with the directly transmitted beam. This effect is very small, but we have clearly

observed it with our first detector types and its magnitude is too large to neglect. From the moment

we started using wedged windows we do not observe it anymore. Still, to fully avoid interferences,

we have tried detectors with special 7.5 cm long tubes in between the detector surface and the

window. Because alignment turned out te be very problematic, these tubes have been removed

again.

2.8 Conclusions

In the last years at the Center for Isotope Research a totally new system, based on Laser

Spectrometry, has been developed. It is a very elegant and straightforward method, which is

theoretically well understood: The corrections for the pressure differential are quantitatively

reproduced by numerical simulations, the other described effects can at least be understood

qualitatively. The accuracy of LS after calibration and normalization depends on the enrichment level

of the sample, but it outperforms or at least competes with traditional methods for δ2H

measurements. For δ18O, however, only in the enriched regime it can compete with existing systems.

Its possibility to measure δ17O is, on the contrary, almost unique. Moreover, LS does not require

cumbersome, time-consuming pre-treatments of the sample before the actual measurement.

LS is currently able to measure three samples simultaneously for all of the important

isotopomers in typically 45 minutes, providing sample throughput competitive to traditional methods

using IRMS. The practical limit to the number of measurement lines that can simultaneously be used

is by no means reached yet. LS has shown to produce stable and reproducible results over an

extended period of time. It is therefore ready to be applied to many applications, to begin with the

biomedical doubly labelled water method in order to measure energy expenditure, and the accurate

measurement of natural isotope abundances in ice cores, in order to reconstruct the past climate.

Set-up

79

Appendix : Specifications present set-up

In this appendix all important equipment as used in the described LS system is listed.

Optical Table: Vibraplane model no. 5108-4896-11, Kinetic Systems, Boston, MA 02131, USA

Air cleaning system: 6 MAS 1200, Clean Air, Woerden, The Netherlands

Color Center Laser: FCL-20, serial no. N7261086, Burleigh Instruments, Inc., Fishers NY 14453, USA

Step motor: RS, type 4440-284, Gear box: RS, type 718-896, ratio 1:100, Control: Home built

External Ion Pump: Leybold-Heraeus 85172Br1

Ramp generators: RG-91, Burleigh Instruments, Inc.

Temperature controller: TC-238, Graseby Infrared

Sine generator: Home built

Summing amplifier: Home built

Laser cavity lock: Electronics designed and built by M. Giuntini of the European Laboratory for Non-linear

Spectroscopy (LENS, Firenze, Italy).

Detector: PbSe photodiode, Graseby Infrared, Orlando 12151, USA (for locking ICE to the cavity)

Kr+ laser: 3500 Krypton ion laser, Lexel Laser, Inc., Fremont, CA 94538, USA, 647 nm

Laser Power supply: 3500, Lexel Laser, Inc.

Laser Water Cooling: PD-2, Neslab Instruments, Inc., Newington, NH 03801, USA

He/Ne Laser: 633 nm, + 1 mW, type RC1, Limab

Power Meter: NOVA, Ophir Optonics, Ltd. Jerusalem, Israel (for alignment purposes only)

External 8 GHz etalon: SA-91 etalon assembly, SA-900 four-axis mount and DA-100 detector amplifier, Burleigh

Instruments, Inc.

External 150 MHz etalon: CF/CFT etalon, DA-100 detector amplifier, Burleigh Instruments, Inc.

CFT controller: RC-45, Burleigh Instruments, Inc.

Single mode monitor: Wavemonitor, home built

Wavelength meter (or wavemeter): WA-20IR, Burleigh Instruments, Inc.

Detectors: TE cooled InAs photodiodes 1A-020-TE2-TO66E with special mounted AR/AR coated 1º wedged

sapphire windows, Electro-optical systems, Inc., Phoenixville, PA 19460, USA

Temperature controller: Temperature controller PS/TC-1, Electro-optical systems, Inc.

Amplifiers: Home-built low noise amplifiers

Optical choppers:

651-1, EG&G Signal recovery, Workingham, UK, and model 650, Light chopper controller; SR540,

Chapter 2

80

Stanford Research Systems, Sunnyvale, CA 94089, USA and SR540 chopper controllers

Lock-in amplifiers:

7265 DSP lock-in amplifiers EG&G EG&G Signal recovery

128A, EG&G Princeton Applied Research

Computer: Apple Macintosh PowerPC G3, 266 MHz, 64Mb memory, 66 MHz bus

Software:

National Instruments LabVIEW 5.0.1f1 for Mac

NI-488.2 Configuration utility, revision 7.6.5

CodeWarrior for Macintosh

and a number of home written applications

Interfacing: National Instruments IEEE 488.2 GPIB board (PCI), revision G.

Gas cells:

Home built Herriot type multi-pass cells, operated in the 48 passes (20.5 m) configuration, possibility to tilt both

mirrors

Mirrors: concave (500 mm) mirrors (ø 50.8 mm) protected gold, one has drilled holes (ø 4.0 mm) @ 22 and 12

mm from the center, Molenaar optics, Zeist, The Netherlands

Windows: 2º wedged AR/AR coated CaF2, EKSMA, 2600 Vilnius, Lithuania

Valves: 26328-KA01-0001 / 1318, Demaco, Noord-Scharwoude, The Netherlands

Hydrophobic coating: Glasscad 18, PS-200, United Chemical Technologies, Inc., Bristol, PA 19007, USA

Syringes: 800 series,10 µl, Hamilton Company, Reno, NV 89520-0012, USA

N2: pure, PS-50-A, AGA Gas BV, Schiedam, The Netherlands

Optics:

Mirrors: CaF2, ø 25.4 mm, protected silver or gold, New Focus, Optilas, Alphen aan de Rijn, The Netherlands

and EKSMA

Lenses: CaF2, ø 25.4 mm, AR/AR, focal length from 5 mm to 2500 mm, EKSMA

Beam Splitters: CaF2, ø 25.4 mm, wedged @ 1º or 2º, different reflectivities, EKSMA

Windows: CaF2, ø 25.4 mm, wedged @ 1º or 2º, uncoated, EKSMA

Optical mounts: New Focus Hardware

Pumps: Drytel 31, Alcatel, 74009, Annecy, France

Cryogenic traps: Home built glass cryogenic traps, 45 cm diameter, connected to one main vacuum line (40

mm) and pump.

3Biomedical application

Biomedical application

83

3. Biomedical applicationIn this chapter, the application of the newly developed Laser Spectrometric (LS) method for

measuring stable isotopes ratios in enriched water samples will be described, enabling biological and

medical applications, in particular in the widely applied doubly labelled water (DLW) method. First an

extended general introduction about this method will be given, then some problems with the

standards and calibration of the method will be discussed, and some examples of real-world

measurements will be treated. Parts of this work have previously been published or are submitted

(Van Trigt 2001a, Van Trigt 2001c).

3.1 Introduction of the doubly labelled water method

The well-known and often applied doubly labelled water (DLW) method is used for the

indirect measurement of CO2 production (and therewith for the energy expenditure) of individual

animals (Lifson 1955). The main advantage of the DLW method over direct measurement of the CO2

output is that the animal under study can live freely and behave naturally, instead of being kept in

an air tight cage. The DLW method is based on the isotopic analysis of initial and final samples of the

individual’s body water pool after administration of water isotopically enriched in 18O and 2H. The

time interval between the initial and final sample is the measurement period.

3.1.1 History

Since the heavy isotopes of oxygen and hydrogen behave chemically and physically almost

identical compared to the normal, most abundant light isotopes (16O and 1H), the body of an animal

does not discriminate between them in large extent. In first approximation, the heavy 2H and 18O

isotopes behave equal to the light 1H and 16O and are therefore “ideal tracers” for oxygen or

hydrogen containing species (e.g., water) in the body. In this way, the routes of various molecules in

the body can be followed. The discovery that the oxygen in body water is in complete isotopic

equilibrium with the oxygen in respiratory CO2, led to the development of the DLW method (Lifson

1949). It was then realised that an administered dose of heavy oxygen is lost through both CO2

expiration and water excretion (urine and sweat), while deuterium is only lost through water

excretion. The difference in the turnover rate of these isotopes must then be equal to the 18O loss via

expiration and thus the CO2 production (Speakman 1997). A theoretical analysis of the method and

its assumptions that were made in the early stages of the development of the method can be found

in the literature (Lifson 1966).

Until the 1970s the method was rarely exploited and then only to study small animals. This

was a result of the high costs of isotopically enriched mixtures. As soon as the costs of the

Chapter 3

84

experiments decreased, however, it found a much more widespread application, both in animals

(e.g., Nagy 1972) and later even in humans (Schoeller 1982). Nowadays it has found its application

in many studies of free-living animals. A number of laboratories exist that is dedicated to the routine

analysis of enriched samples. For example, in Groningen on average about 5000 samples a year are

now analysed for both 18O and 2H.

3.1.2 Calculations

In practice, the method involves the introduction of heavy isotopes of both oxygen and

hydrogen into the body to quantify the size of the total body water pool (TBW). After the

administration of the dose, it takes a certain time to establish the equilibrium level for both isotopes

and after this period the initial sample is taken. Then, the isotopes are gradually washed out of the

body and, consequently, an exponential decrease of the isotope concentration will occur. The

different rates (k; d-1) of the isotope elimination of both isotopes are quantified from the exponential

declines measured from the initial (i) and a final (f) sample. In its simplest form the rate of CO2

production (rCO2; mol.d-1) can be calculated as:

r N k kCO2 2 18 2= ⋅ −( ) ( ) (3.1)

where the subscript “18” denotes 18O and “2” refers to 2H, N is the amount of TBW in moles and the

factor of 2 is due to the fact that CO2 contains 2 oxygen atoms, while H2O has only one. In Figure

3.1 the principle of the DLW method is depicted graphically.

Unfortunately, there are numerous complications to the very simple approximation of the

processes described in Equation 3.1. Among these are fractionation effects that do occur (or the

difference in behaviour between the “normal”, most abundant isotopomers and the rare, heavy

isotopes). In fact, this is the deviation of the behaviour that a perfect tracer would show.

Furthermore, biological (determination of the body water pool) and analytical (determination of

background level; measurement errors) complications have to be considered. In the remaining of

this paragraph, a more detailed description of the calculation procedure will be provided and a

survey of some of the above mentioned aspects will be made.

Biomedical application

85

Food

O2

CO2

H2O

work

HeatH18OD

( + 18O)

(+ 18O + 2H)

time

ln(i

soto

pe c

once

ntra

tion)

18O

2H•

•

•

a b

Figure 3.1a: The DLW method: After a pulse dose of 18O and 2H has been administrated, the heavy

isotopes leave the body via CO2 and H2O,

Figure 3.1b: Graphical representation of 2H and 18O enrichments in the body water pool of an animal

as a function of time. The lines represent the natural logarithm of the heavy isotope concentration in

the studied animal. These are scaled on the starting point, where the initial sample was taken. The18O decrease is slightly steeper than the 2H decrease, since an extra elimination route exists (see the

text). The italicised area is a measure for the amount of CO2 produced during the measurement.

3.1.2.1 Isotope abundance ratios

First, one has to remember the way isotope abundances xRs are usually presented (Chapter

1). Since in natural applications the ranges are small, we are used to express the value as a relative

deviation from the value of a calibration material. For water, the internationally accepted calibration

material is Vienna Standard Mean Ocean Water (VSMOW). The 18O/16O, 17O/16O and 2H/1H isotope

ratios of a water sample are generally reported as:

xs

xsample

xVSMOW

R

Rδ = − 1 (3.2)

and thus:

xs

xVSMOW

xsR R= ⋅ +( )1 δ (3.3)

Chapter 3

86

Where x represents the mass number of the rare isotope, and xRVSMOW the isotope abundance ratio of

Vienna Standard Mean Ocean Water (VSMOW). For further calculations, the absolute isotope

concentrations xCs of the samples are used. For example for 18O:

1818

17 181C

RR Rs

s

s s

=+ +

(3.4)

The concentrations are expressed in parts per million (ppm).

3.1.2.2 The amount of Total Body Water

The amount of Total Body Water (TBW; g) for each individual animal (N in Equation 3.1) can

be determined by simply measuring the dilution of the injected DLW with the body water:

TBW QC CC Cd

d i

i b

= ⋅ ⋅ −−

18 02. (3.5)

(Speakman 2001), where Qd represents the individual-specific quantity of the dose (mole), Cd the

isotope concentration of the dose, CI the individual-specific isotope concentration of the initial blood

sample, and Cb the population-specific average background concentration. The factor of 18.02 is

needed for the conversion of moles to grams. This method is often referred to as the plateau method

(Speakman 1997), and can be applied for each administrated isotope.

In fact, what is measured in Equation 3.5 is not exactly equal to the amount of TBW, but is

rather referred to as the isotope specific dilution space. For 2H, for example, more sinks exist (e.g.,

fat, protein and carbohydrate) additionally to the body water (IDECG 1990). Therefore, 2H typically

overestimates the TBW value derived with 18O by 3 to 5%, which in turn exceeds the body water

pool by 1% (Speakman 1997). The single-pool approach is the simplest approximation possible. Of

course, attempts have been made with more complicated models (two-pools). While reasonably

succesfull for larger animals (> ~ 5 kg), the single-pool model provides the best results in smaller

animals. The difference must be due to relatively large, extra elimination routes that exist for 2H in

small animals (Speakman 1997). It will not be discussed here any further.

3.1.2.3 Fractional turnover rates

For each isotope, the fractional turnover rate (k, d-1) can be calculated during the

measurement:

Biomedical application

87

kC C C C

ti b f b= − −ln[( ) /( )

(3.6)

where t is the time interval between initial and final sample (d), and Cf the isotope

concentration of the final sample. The fractional turnover rates of 2H and 18O are referred to as k2

and k18, respectively.

The k18/k2 ratio is typically between 1.3 and 1.5; oxygen has the larger turnover rate,

because of the additional path (respiration) for leaving the body when compared to deuterium

(excretion and evaporation only). For calculation of the CO2 production we can therefore use the

difference k18-k2 (see below). In studies where the water turnover is high (both slopes are steeper),

the relative differences in slopes are smaller, while the absolute difference remains the same.

Therefore, in these studies, analytical errors will have a relatively high effect on the calculated

energy expenditure. Moreover, it is obvious that the final sample concentration should still be

sufficiently elevated above the background to produce reliable results. When the water turnover is

high, the injected dose must therefore be increased, or the measurement period reduced. When it is

possible to collect more samples in between the initial and the final, this could be used to improve

the precision of the determined turnover rates to the price of a higher number of measurements

(IDECG 1990).

Water fluxes

The water efflux rH2O (g/d) can, in first approximation without corrections for isotope fractionation,

be calculated by:

r N kH O218 02 2= ⋅ ⋅. (3.7)

Where N is the amount of body water (mol), determined from 18O dilution. The water flux can be

manipulated by way of changing the diet of the animals.

3.1.2.4 CO2 production

The CO2 production is in first approximation given by Equation 3.1. In order to correct for

fractionation, three fractionation factors have already been defined by Lifson (1955). These take into

account the evaporation of water for 2H (f1) and 18O (f2) and the CO2-H2O fractionation for 18O (f3).

To enable incorporation of these factors into Equations 3.6 and 3.7, the proportion of water lost as

vapor (fractionated; rG) must be defined (Lifson 1966). This must be done, since only breath water

(vapour) shows a significant fractionation effect; water losses excreted as urine or sweat (liquids) do

not show isotopic fractionation (or only very little).

Chapter 3

88

After taking these fractionation effects into account, Equation 3.6 can be rewritten as:

N k r f r r rG H O G H O⋅ = ⋅ ⋅ + − ⋅2 1 2 21( ) (3.8)

For the oxygen isotopes, a similar relation holds, but with respiration as an additional sink

and using different fractionation factors:

N k f r r f r r rCO G H O G H O⋅ = ⋅ ⋅ + ⋅ ⋅ + − ⋅18 3 22 12 2 2

( ) (3.9)

Solving rH2O from Equation 3.8 and substituting it in Equation 3.9 than gives an expression for the

CO2 production rate:

r N2 f (k k ) r

f f2 f N kCO * 3

18 2 G2 1

322

= ⋅ − − ⋅ −⋅ ⋅ ⋅ (3.10)

The numerical values of f1 and f2 are dependent on the exact pathways that are responsible

for the evaporation of water: Both equilibrium and kinetic isotope fractionation processes will

contribute to the final values for f1 and f2. The values of the equilibrium and kinetic fractionation for

water evaporation can be obtained from literature (Speakman 1997). For 2H (f1) these are 0.941 and

0.9235, respectively at 37 ºC and for 18O (f2) 0.9925 and 0.9760. It is hard to define exactly what

fraction of the evaporating water is lost under which regime and this fraction may even be

temperature dependent (Speakman 1997). The most recent estimate for many small animals for the

relative contribution of both evaporation processes is equilibrium : kinetic = 3 : 1 (Speakman 1997).

This relation is now widely used. For f3 only equilibrium fractionation takes place, since CO2 is in

constant equilibration with the water in the blood stream (fast equilibrium establishment due to the

presence of the carbonic anhydrase) and in the lungs, before it can leave the body. This fractionation

factor has a value of 1.039. All values of the fractionation constants and their relative contributions

are based on lab experiments.

Filling in the above mentioned values in Equation 3.10 yields:

r N2.078 (k k ) r N kCO 18 2 G 22

= ⋅ − − ⋅ ⋅ ⋅0 0249. (3.11)

Biomedical application

89

3.1.2.5 Assumptions concerning evaporative water loss

Equation 3.10 can be applied for different assumptions concerning rG. To circumvent the lack

of knowledge on the individual-specific value for this parameter, it was originally taken as 0.5 for all

diets based on laboratory estimates of small mammals (Lifson 1966). Although this value has been

widely used to estimate rCO2 in free-living birds and mammals, a more detailed analysis suggested

that a value of 0.25 was more appropriate due to the fact that water fluxes tend to be higher in free-

living animals than in the laboratory (Nagy 1988, Speakman 1997). In Paragraph 3.4 a validation

experiment will be described to reveal the sensitivity of the DLW method to this assumption of rG.

3.1.3 Validation studies

The DLW method is based on a number of assumptions, some of which have already been

indicated above. Based on Speakman (1997), one can list the following five as the most important

ones:

1. Rates of CO2 production and H2O losses and gains are constant during the measurement interval,

2. Isotopic fractionation constants are known exactly, as is the contribution of kinetic and equilibrium

fractionation,

3. The size of the body water pool is known accurately and remains constant during the

measurement interval,

4. The pools for 18O and 2H are the same and, moreover, equal to the body water pool,

5. All substances entering the animal are isotopically labelled at the background level and no entry of

unlabelled CO2 or H2O through the skin occurs.

A more practical point that could cause errors in the determined CO2 production is the

equilibrium period for the isotopes that must be esteemed after the administration. Initially, there

will be a rapid increase in the isotope concentration in their respective pools. Then it will slow to

reach a maximum, after which the loss will dominate. If the chosen equilibration period is too long,

the body water pool is not known accurately, while if it is too short, on the other hand, no full

equilibration is reached and both the body water pool and the CO2 production will be wrongly

calculated (Matthews 1995). The right delay before taking the initial sample is dependent on the

weight of the studied animal and should be based on experience or preliminary measurements. The

estimation of the evaporative water loss is another practical point that could be a source of errors.

To validate the correctness of the DLW method and thus to check its applicability, a number

of studies has been performed that compare the CO2 production calculated with the DLW method to

another measurement or estimation of the CO2 production. Most of these studies have been done on

animals (e.g., Speakman 2001, Junghans 1997, Haggarty 1998, Blanc 2000) of different sizes and

sorts and at different circumstances, but also on humans (e.g., Morio 1997, Westerterp 1995).

Recommendations about equilibration times, the optimal formulae to calculate the CO2 production

Chapter 3

90

and values for the used constants in the equations originate from these investigations. From the

combined knowledge, nowadays reasonably good results for animals and humans in most

circumstances are achieved, although it is clear that if the method is applied in more extreme

situations, it needs additional validation.

3.1.4 Analytical errors

On top of the validation studies, which act as a real-world test for the DLW method,

theoretical studies on the propagation of errors have been done (e.g., Nagy 1980, Schoeller 1995).

When an analytical error is made, it can influence both the determination of the total amount of

body water, and the turnover rates for 18O and 2H. Therewith it will influence the CO2 production to

be calculated.

The measurement of enriched samples introduces errors, caused by, amongst others, IRMS

non-linearities, cross-contamination (Meijer 2000) and memory effects of the sample preparation

system. Moreover, different sample preparation techniques and measurement methods are in use.

This results in differences in the values determined by different laboratories. In interlaboratory

comparisons to clarify between-laboratory variability, some serious deviations can be found

(Speakman 1990, Roberts 1995, Schoeller 1995). Even in the study by the IAEA to define a value of

a novel enriched standard, a serious spread was seen (Parr 1991).

These different observations can lead to erroneously determined CO2 production rates, which

can differ many percents relative to each other. Although the fact that different laboratories measure

different values for the isotope ratios is not satisfying at all, it is not problematic by definition. This

is, because the determination of the absolute, normalised isotope abundance ratio (or scale

contraction or extension) is not important for the calculations as long as the entire scale is linear

(which will be discussed in more detail in Paragraph 3.2). However, as soon as second order effects

occur, the results become unreliable. Unfortunately, most effects are non-linear indeed and the only

way to obtain reliable values is, thus, by calibrating the measurements using internal gravimetrically

enriched water standards.

One should realise that when the water turnover is high, the washout curves of both

isotopes are steep and much more parallel than in the case of low water losses. This causes an error

in the initial or final sample, to propagate with a high amplification into the determined CO2

production. Furthermore, an error in the final sample in general has a greater effect than an error of

the same absolute magnitude in the initial sample, especially when it is too close to the background

value. Therefore, the dose and measurement period should be carefully chosen in such a way that

optimal values are found for the initial and final isotope abundances.

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3.1.5 Conversion from CO2 production to energy expenditure

After obtaining a value for the CO2 production, it must be converted into a value for the

energy expenditure (Speakman 1997). Most often, the glucose oxidation equation is used:

C H O O H O CO6 12 6 2 2 26 6 6+ → + (3.12)

From this reaction it is exactly known how much (useful) energy is released per molecule CO2

formed. Alternatively, the animal can also use fatty acids as its energy source, for example:

CH CH COOH O CO H O3 2 14 2 2 223 16 16( ) + → + (3.13)

with another amount of energy released for each CO2 molecule. Additionally, also proteins can be

metabolised, giving different energy yields again. Since it is hard to determine exactly how much of

each of these sources is used, assumptions about the rate of combustion of the different energy

sources are made. Discussion of this point is beyond the scope of this thesis, but it is clear that this

last conversion step can also contribute to the uncertainty of the method.

3.1.6 Extension with another label: The triply labelled water

method

In many studies on small animals, tritium was applied (Haggarty 1988) as a replacement for

deuterium in the DLW method. The disadvantage of tritium is its radioactivity, obviously causing

hazards for the subject as well as for the researchers. Therefore, it is not often used in studies on

humans. Of course, when tritium is applied instead of deuterium, the fractionation factors used in

the formulas to calculate water turnover and CO2 production should be correspondingly adjusted.

The advantage of using tritium instead of deuterium is that the amount of tritium (actually the rate

of radioactive decay) can be measured with high accuracy, much better and easier than the

deuterium abundance. The limitation in the measurements is the knowledge of the exact amount of

water (distilled biological sample) that is introduced into the measurement system. A typical

uncertainty amounts to 1% or 2% for a trained laboratory worker (Speakman 1997). Nowadays,

however, the techniques for measuring deuterium have improved and the use of tritium in DLW

studies has decreased. The disadvantage of the use of the radioactive tritium now exceeds its

advantage.

In 1988 it was reasoned that if, next to 18O, both tritium and deuterium would be

administered to an animal under study, it might be possible to measure the evaporative water loss

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92

independently (Haggarty 1988). This method is referred to as the triply labelled water (TLW)

method. For the third isotope, tritium, one can write down an equation that is fully analogue to 3.10:

r N2 f (k k3) r

f f2 f N kCO * 3

18 G2 1,3

332

= ⋅ − − ⋅−⋅ ⋅ ⋅ (3.14)

where k3 is the measured turnover rate of tritium and f1,3 the tritium isotopic fractionation constant

for evaporation, again based on a 3:1 ratio of equilibrium : kinetic processes. Equations 3.10 and

3.14 should result in equal CO2 production rates, so by combining the two, rG can be calculated.

Thus, using this method one could, in principle, measure the fraction evaporative water loss on

individual animals. The CO2 production could be determined with higher accuracy on the level of

individual animals as well. Unfortunately, the precisions that are needed for this individual rG

determination are very high. Since the elimination curves of the 2H and 3H are parallel in a high

degree (or the fractionations differ only slightly), a little measurement error will already have

dramatic consequences for the calculated value for rG. Moreover, it is assumed that the fractionation

constants are exactly known and this assumption might also introduce large errors. Because of the

constraints, the TLW method has not shown to be more useful for practical purposes than the DLW

method.

The same TLW approach can be followed by including 17O as the third isotope administered,

next to 18O and 2H. This will at least eliminate the use of the radioactive tritium. Thus, the equation

can be written, analogously to Equation 3.8 and 3.10:

r N2 f (k k ) r

f f2 f N kCO * 3,17

17 2 G2,17 1

3,1722

= ⋅ − − ⋅−

⋅ ⋅ ⋅ (3.15)

where k17 is the turnover constant of 17O, and f2,17 and f3,17 are the fractionation constants for 17O

fractionation in water evaporation and the CO2-H2O equilibrium, respectively. This manner of

applying the TLW method has never attracted serious attention, since it was never possible to

measure 17O reliably with IRMS systems. With the newly developed LS set-up, however, 17O can be

measured independently and therefore it is worth exploring the possibilities of the TLW method

again.

3.1.7 Exploring the possibilities of the TLW Method with 17O

In order to find the possibilities of the proposed TLW method, using 17O as the third isotope

next to 18O and 2H, the expected isotope signals in small animals were calculated. The masses and

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volumes used in the calculations are based on typical values for quails, that were intended to be

used for a validation experiment.

First, the fractionation constants for 17O must be calculated. These relate (in very good

approximation) to the corresponding fractionation constants of 18O as (Meijer 1998):

17 18f f=( )λ , with λ = 0.5281 (± 0.0015) (3.16)

Thus values for f2,17 and f3,17 (f1 does not change) can be calculated and substituted into Equation

3.15. For a quail of 250 g, which has a TBW of 60% (150 g), it is assumed that it has background

isotope abundance ratios equal to local meteoric water. Suppose we administer 0.58 g of triply

labelled water with the following isotope abundance ratios: 2R = fD/fH = 0.826, with fH = 1 –fD, and f

is the fraction of the corresponding species in the mixture; 17R = f17/f16= 0.0528 with

f16 = 1 –f17 – f18, and 18R = 0.901. These amounts have been chosen in order to reflect true

experimental values.

After equilibration, the initial blood sample will have the following values: δ2H = 10118‰,

δ17O = 245‰ and δ18O = 799‰, with respect to VSMOW. The animals are given free access to

food and water. We assume that they eat 30 g of glucose per day and drink 37 g of water. If the

mass of the birds does not change, the food is, through the metabolic processes, a source for

hydrogen and oxygen with the same background values as the bird had prior to the beginning of the

experiment. After 24 hours the final samples have the following isotope abundances, at an

evaporative water loss of 0.5: δ2H = 7120‰, δ17O = 148‰, δ18O = 484‰. It will later be shown

by experiments that these values are indeed a rather good description of reality. The values will

slightly change if the relative evaporative water loss is changed; for 2H the steepest dependency is

found, ranging from 7006‰ to 7235‰ if the evaporative water loss is changed from 0 to 1. This is

a range of almost 3% in the isotope abundance ratio R. 18O and 17O show a range in R of around

0.2%.

With these calculated values, the turnover rates for the three isotopes can now be calculated

and thus the amount of CO2 that is produced. If the sensitivity of the calculated values to different

influences is considered, the following observations are made: A measurement deviation of –1‰ of

the value in the final value of δ17O, results in an increase of the turnover rate that in turn results in

an about 4% higher calculated CO2 production. The same measurement deviation in 18O (–1%)

makes the calculated CO2 production just over 1% higher. The difference is due to the fact that the17O measurements are closer to the background and, thus, that an absolute error of 1‰ is relatively

bigger. When decreasing the 2H final measurement value with 10‰, an apparent decrease of CO2

production with ~1% for both 18O/2H and 17O/2H is observed. If the evaporative water loss is

decreased from 0.5 to 0.25, the calculated CO2 production increases with 0.3% only. Although the

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94

model used is very simple indeed and does take into account neither the effects of other routes of

metabolism than the glucose oxidation, nor the secretion of faeces, it is useful in testing the

sensitivity to some variables.

From the observations in the simulation described it must be concluded that for an

estimation of the amount of evaporative water loss, a measurement accuracy for 17O and 18O of at

least 0.03‰ is needed. Only than, the CO2 production can be calculated separately by both the17O/2H and 18O/2H DLW methods with an accuracy of better than 0.1%. This is a demand, since the

influence of the evaporative water loss is of this order of magnitude. We will, however, see that this

demand is more than one order of a magnitude too high to be satisfied by the LS (and for 18O for the

IRMS) for measurements on enriched samples. On top of this, the necessary measurement accuracy

of 2H should be better than 1‰.

With our present analysis method, it can not be expected that the results of the TLW method

can be used to calculate the evaporative water loss. However, the extra isotope combination (17O/2H)

can serve as a duplo measurement additional to the measurements obtained with the traditional

DLW method. This can improve the precision of the method and therewith still mean an overall

improvement.

3.2 Problems with standards, calibration

As already discussed in Chapter 2, the LS system needs calibration against known standards

in order to reflect reliable values for the measurements. As long as this calibration is linear (which is

the case for 18O, 17O and, at low and moderate enrichments, also for 2H) it does not influence the

outcome of the DLW calculations at all. In other words: When the measured background, initial and

final isotope abundances, R, are multiplied with an arbitrary factor, the calculated energy

expenditure will not change. Still, we attempt to determine well-calibrated isotope ratios (i.e., as

properly calibrated with respect to VSMOW as possible). However, one should realise the principal

problems one encounters for enriched samples, especially for the oxygen isotopes, due to

uncertainties in the enrichments of the heavily enriched starting materials of the gravimetric mixing

procedure. The enriched reference and working standards are always prepared by diluting

commercially available batches of highly enriched waters. In the validation study described in

Paragraph 3.4, three different certified waters have been used:

1. Enriched in 2H: fD ≥ 99.9%, normalised in 18O and 17O.

2. Enriched in 17O: f17O = 19.2%, f18O = 32.9%, normalised for 2H.

3. Enriched in 18O: f18O = 94.5%, f17O = 2%, normalised for 2H.

Although these numbers seem to be accurate and are even certified, it is unclear with what

assumptions they had been derived. The manufacturer and the reseller do not provide additional

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information on the method that is used for measuring the specified absolute abundances. For 2H it is

easy (e.g., using NMR) to quantify the remaining amount of hydrogen (1H) in the sample with

accuracies that are high enough to prove the specified number. For the oxygen isotopes, however, it

is very likely that a dilution series was made and that the mixtures were measured using traditional

IRMS after sample preparation. From the values obtained, the original values are then reversely

calculated. This method is not very accurate, since no internationally enriched reference standards

are available to check the IRMS against. And there is no guarantee whatsoever that it is possible to

simply interpolate the scale expansion corrections as determined using the SLAP-VSMOW calibration

materials. In fact, the same problem arises in the natural range, but here it is solved by just defining

a value for the δ18O and δ17O of VSMOW and SLAP, in other words, by redefining of the δ–scale

(Chapter 1). It will only be possible to make enriched standards in a reliable way as isotopically pure18O and 17O (and, ideally, 16O) become available, and this is, to the best of our knowledge, not the

case. Because no absolute calibration standards are available for the oxygen isotopes, it is necessary

to compare the measured values for the dilution series (using IRMS) against VSMOW. Even if the

scale expansion is unity or known accurately, we need to know the values for 17R0 and 18R0 of

VSMOW in order to calculate the absolute amounts in the original mixture from these measurements.

And for these values one finds a serious spread in the literature, again because it is not possible to

isolate the isotopically pure isotopomers of oxygen (e.g., Nier 1950, Hageman 1970, Baertschi 1976,

De Wit 1980, Li 1988, Zhang 1987). It is clear that this is circular reasoning: The exact values of the

standards can only be known when they can be compared to reference standards of which the

absolute composition is known. When one tries to mix such a reference standard, one needs to know

the exact content of highly enriched waters that can in turn only be known by making a dilution

series. The only two possibilities to break this dilemma is either when isotopically pure isotopomers

become available, or an absolute measurement method for isotope ratios with sufficient accuracy

becomes available (as in Valkiers 1993). For 2H, on the other hand, it is possible to obtain (almost)

pure 1HO1H and 2HO2H, so the absolute isotope abundance for VSMOW and the reference standards

are known.

From the certified mixtures, a mixture of water has been produced gravimetrically that can

be administered to animals for TLW experiments. This mixture has high enrichment values, thus

enabling us to reach initial values that are high enough to measure two to three turnover times and

still have acceptably high values in the final samples. The final injection mixture used in the

experiments of Paragraph 3.4 contains 45.2% 2H, 2.7% 17O and 46.1% 18O, based on the

enrichments specified by the supplier. For an average quail (~ 250 g) and with the amount we plan

to inject (0.6 g), initials will be about δ2H = 10000‰, δ17O = 250‰ and δ18O = 800‰. Starting

with the injection mixture, we also produced a dilution series of 6 standards. It is easy to show by

Chapter 3

96

calculation that the influence of any weight uncertainty is totally negligible compared to the

measurement accuracies.

Concluding from the above, it can be stated that the real enrichments for 17O and 18O in the

certified materials might be different from the enrichments claimed. For the final results of the DLW

method this does not introduce any error, but the measured values for the standards and samples

might all incorporate systematic deviations. Therefore, calibrations that have to be made for our own

measurements are not necessarily accurate.

3.3 First test measurements: Seal blood and infant urine

The LS system is, in principle, able to measure isotope abundance ratios in water vapour

derived from any sample. Impurities in the water will have no influence on the results, since the

infrared fingerprint spectrum is extremely selective. It would thus be possible to directly inject, for

example, blood or urine into the injection chamber of the gas cell. From a more practical point of

view, however, contamination of the gas cells is unwanted, mostly since we do not have made

precautions yet in order to avoid dirt on the mirrors. For this reason, all blood and urine samples will

be distilled prior to injection into the cells. In this paragraph a first test is described to see whether

this distillation introduces an error.

As test material, seal blood samples (background isotopic abundance), mixed with a known

amount of triply labelled water were used, as well as a series of DLW urine samples of early born

infants (1000 – 1100 g), who were kept in an incubator. Thus, the seal blood samples were

simulated enriched samples, while the infant urine samples were real initials and finals used in order

to measure energy expenditure. Both series of samples were small leftover batches from the

biomedical section of our laboratory. In order to calibrate IRMS and LS, the DLW standards were

used that have already been employed in our laboratory for a long time. Their enrichments span the

range from about 0‰ to 9650‰ for 2H, and 0 to 1240‰ for 18O. The 17O measurements are

neglected for now, since the samples were only deliberately enriched in 2H and 18O, and the

remaining enrichments of 17O are not known. Moreover, using the IRMS it is not possible to obtain

these values.

The main question was whether it is possible to distill the samples off-line without

introducing errors. This is answered using IRMS measurements only. From the bulk samples,

capillary tubes were filled and analysed using the traditional methods (see Chapter 1). In short, the

capillaries were broken in a vacuum system and the water content was cryogenically frozen into a

small quartz vial. Then, CO2 gas of known isotopic composition was added and the vial was placed in

a water bath at well-controlled temperature in order to establish the H2O-CO2 isotopic equilibrium.

After a 24 hours waiting period and removal of the CO2 for analysis, the remaining water was led

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over an uranium oven at 800 ºC to produce H2 gas. This was trapped in active coal at cryogenic

temperatures and thereupon analysed.

The remaining of the blood and urine samples was distilled over a cryogenic small-size

distillation set-up. Condensation was prevented by gently heating the glass connection tubes. Part of

the distillate was melted into capillaries as well and treated and analysed in the same way as the

dirty samples.

In Figure 3.2 it is shown that no significant deviation exist for 2H measurements with IRMS

after distillation. The same calibration is used for all measurements, and derived from the regular set

of standards and procedures used in our laboratory. The small deviations from unity for the slope

and from zero for the offset are not significant and caused by random errors in the sample

preparation and measurement procedures. In the residuals, which are shown in the upper part of the

plot, no trend or offset is observed. The same is observed for 18O (not plotted).

Figure 3.2: IRMS measurements of a number of blood (seal) and urine (infant) samples after

distillation, versus the same samples analysed without the distillation step. Every point represents

the average of four independent analyses. The error bars are a measure for the variability of

repeated measurements. Also the residuals are shown.

0

2000

4000

6000

8000

1 104

-20

0

20

40

0 2000 4000 6000 8000 1 104

Cal

ibra

ted

LS

val

ue

δδδδ2H

(‰

)residu

al (‰)

MS value δδδδ2H (‰)

y = M0*m1 + m2

ErrorValue0,00161330,99816m1

5,9438-9,0521m2 NA3682,3ChisqNA0,99998R

Chapter 3

98

From these measurements in an enrichment range of 0‰ to 9000‰ (for 18O: 0‰ to

1200‰) we observe no negative systematic effect at all of our careful distillation procedure. We

conclude that distillation can safely be applied to all samples.

3.4 Validation of the doubly labeled water method in

Japanese Quail at different water fluxes

The text of this paragraph is based on a paper submitted to the Journal of Applied

Physiology (Van Trigt 2001c).

3.4.1 Abstract

In Japanese Quail (Coturnix c. japonica; n = 9 males), the doubly labeled water method (2H,18O; DLW) for estimation of CO2 production (rCO2, l/d), was validated. To evaluate its sensitivity to

water efflux levels (rH2O, g/d) and, thus, to assumptions of fractional evaporative water loss (rG),

animals were repeatedly fed a dry pellet diet (average rH2O 34 g/d), or a wet mash diet (96 g/d). We

simultaneously evaluated a novel Infrared Laser Spectrometry (LS) method for isotope

measurement, compared to classical Isotope Ratio Mass Spectrometry (IRMS). At low rH2O, calculated

rCO2 exhibited little sensitivity to assumptions concerning rG, the best fit being found at 0.5, but little

error was made employing a rG-value of 0.25. In contrast, at high rH2O, sensitivities were much higher

with the best fit at rG = 0.25. Conclusions derived from IRMS and LS were similar, proving the

usefulness of LS. Within a three-fold range of rH2O, little error in the DLW method is made when

assuming one single rG value of 0.25, indicating its robustness in comparative studies.

3.4.2 Introduction

The doubly labeled water (DLW) method has frequently been used for measuring the rate of

CO2 production in free-living animals and humans and therewith their levels of energy expenditure

(Lifson 1955, Lifson 1966, Nagy 1980, Speakman 1997). Its usage is based on the measurement of

the turnover rates of both 2H and 18O. It is hereby assumed that, after administration of a dose of 2H

and 18O enriched water, 2H leaves the body water pool exclusively as water, and 18O both as water

and CO2 gas. Consequently, the difference between 18O and 2H turnover rates is proportional to the

CO2 production. However, due to mass differences between 1H and 2H, as well as 16O and 18O, heavy

isotopes leave the body water pool less readily in gaseous molecules such as water vapor and CO2

gas (fractionation effects). Due to these effects, the body water pool remains isotopically relatively

more enriched compared to the water vapor, and more so for 2H than for 18O. If no corrections are

made for fractionation, the calculated levels of CO2 production will be systematically too high. To

account for this, some specific assumptions must be made for the fractions of water lost as liquid

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(not fractionated) and as vapor (fractionated; rG; Lifson and McClintock 1966). Originally, the rG

value was taken as 0.5, as estimated from small mammals under laboratory conditions, but this

value has also been applied to free-living animals with all sorts of diets (Speakman 1997). However,

after having completed a more detailed analysis on water fluxes and evaporative water losses,

Speakman (1997) proposed the use of a rG value of 0.25 for free-living animals.

Speakman (1997) lists 22 validation studies of the DLW method for mammals and 18 for

birds. In all studies, animals were housed in small cages, at thermoneutrality, and were fed a

standard diet. Because birds in the field typically exhibit higher levels of energy expenditure, and

thus, higher levels of food and water intake, their water flux levels tend to be almost 60% higher

than in the laboratory (Nagy 1988). Therefore, it is questionable whether the results of the

laboratory-based validation experiments are directly applicable to free-living conditions. Moreover,

animals of some species tend to have diets with large differences in water content during their

annual cycle, resulting in large seasonal variations in water fluxes. For example, Red Knots (Calidris

canutus) feeding on insects during the reproductive stage exhibit water fluxes of about 80 g/d, while

feeding on bivalves during the migratory and wintering stages these levels can reach values up to

600 g/d (Visser2000a). Therefore, it is possible that the application of one specific rG-value for these

birds throughout the annual cycle is not valid. An erroneous estimation for rG will affect the over-all

accuracy of the DLW method by creating a systematic bias for the calculated levels of CO2 production

(Speakman 1997), potentially complicating the application of the DLW method in comparative

studies.

At high water fluxes (relative to the level of CO2 production), there is little divergence

between the elimination curves of both isotopes (Roberts 1989), resulting in a high sensitivity to

analytical errors, and, thus, in a reduction of the precision of the method (Speakman 1997).

Therefore, especially if the DLW method is to be applied in animals at high water fluxes, a

continuous need exists for improvement of analytical methods. The traditional way of determining

isotope ratios in body water is through equilibration with CO2 and conversion into H2 gas for 18O and2H, respectively, and subsequent measurement with dual inlet Isotope Ratio Mass Spectrometry

(IRMS). The analytical errors of the method are significant, especially for 2H (Wong 1990). Recently,

we developed a novel laser spectrometric (LS) method suitable for biomedical applications that has a

number of advantages over the traditional techniques, amongst these an enhanced precision of 2H

measurement, and a higher rate of sample throughput (Van Trigt 2001a). The LS method is based

on direct infrared laser absorption spectrometry of a water sample in the vapor phase, enabling a

measurement of isotope ratios without performing any sample preparation steps.

To investigate the sensitivity of the DLW method to assumptions concerning fractional

evaporative water loss, a validation study was performed in Japanese Quail (Coturnix c. japonica)

against direct respiration gas analysis. This technique is very straightforward, can be performed with

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100

high accuracy and does not rely on assumptions. As such we apply the outcome of measurements

using this technique as validation for our DLW experiments (in most validation studies referred to as

”golden standard”, e.g., Speakman 1997). To manipulate water flux rates within individuals, birds

were fed either a standard pellet diet (resulting in a “normal” water flux for a laboratory animal), or

the same standard diet but mixed with water to yield a wet mash diet with about 80% water

(potentially resulting in a “high” water flux). Additionally, to explore the advantages of the newly

developed LS with its higher precision for 2H measurements, LS based results were compared with

those derived from classical IRMS analysis.

3.4.3 Methods

3.4.3.1 Animals and housing

For the validation experiment, we used 9 male Japanese Quails of a fast-growing strain

(broiler) between 10 and 15 weeks of age. Prior to the validation measurements, birds were

individually housed at 20˚C in wooden keeping cages (l × w × h: 67 × 39 × 44 cm), and had ad

libitum access to drinking water. Food, also available ad libitum, consisted of either a dry pellet diet

(henceforth referred to as the “dry” diet) containing 27.7% (w/w) crude protein with a gross energy

content of 17 kJ/g (Boon 2000), or of a wet mash diet (“wet” diet) using the same type of pellets

dissolved in drinking water (mixing ratio of 1:4 w/w). The LD cycle was 16:8, with lights on at

08.00 h. In all cases, birds were allowed to adjust to a particular diet for at least one week. To avoid

any bias, in 5 birds the validation was performed first when fed the dry diet, and the wet diet

thereafter. In the other 4 birds we first performed the validation with the wet diet, and the dry diet

thereafter.

3.4.3.2 Experimental procedures

Each bird was intraperitoneally injected a dose water (with 45.2% 2H and 46.1% 18O) of

about 3 mg/g body mass using a sterile needle. Its exact quantity was determined by weighing the

syringe before and after the administration on a Sartorius BP121S balance to the nearest 0.1 mg.

After an equilibration period of 60 min (Speakman 1997, Visser 2000b), the bird was weighed on a

Sartorius QT6100 balance to the nearest 0.1 g. Subsequently a blood sample of about 0.4 ml was

taken from the bird after puncturing the brachial vein with a sterile needle (henceforth referred to as

the “initial” blood sample). Samples were always stored at 4˚C in a 1-ml glass tube until further

analysis (see below). Immediately thereafter, the bird was individually placed in a respiration

chamber (l × w × h: 35 × 25 × 25 cm) and the lid was closed. The respiration chamber was

connected to a controlled airflow unit with a ventilation rate of 90 l/h, and it was placed in a

climatized room with the same LD cycle as before. The temperature within the respiration chamber

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was kept between 15-16°C. The respiration chamber contained a metal grid above a 1.5-cm layer of

paraffin oil. During the measurement the bird had free access to water and food. Due to this setup,

we were unable to measure evaporative water losses during the experiments. 24 Hours after having

taken the initial blood sample, the lid was removed from the respiration chamber, the bird was

reweighed, and another blood sample was taken from the vein of the opposite wing (henceforth

referred to as “final” blood sample). To minimize interference of the sampling procedure with the

animals’ behavior during the validation experiments, we refrained from taking an individual-specific

blood sample immediately prior to the injection (the “background” sample). Pilot experiments had

revealed that an intensive sampling procedure negatively interfered with the animal’s food and water

consumption, particularly when fed the wet diet. Therefore, only from 4 animals a blood sample was

collected 2 days prior to the validation.

3.4.3.3 Infrared respiration gas analysis

Rates of CO2 production were measured in an open air flow system, as previously described

(Visser 1999, Visser 2000b). In brief, respiration air flow, which was adjusted at 90 l/h, was

controlled by a calibrated Brooks 5850 E mass-flow controller, to obtain an absolute difference in CO2

concentration between inlet and outlet air of about 0.5%. These concentrations were determined

every 2 minutes for each measurement with an infrared CO2 gas analyser (Leybold Heraeus BIONS-

IR). RCO2 was calculated as the difference between the CO2 fractions of the inlet and outlet air times

the flow rate. Unfortunately, we failed to downscale the calibration procedure of quantitative ethanol

combustion, as frequently used in validation studies of humans (Westerterp 1995). Due to the high

energy content of the ethanol, with ventilation rates of 90 l/h (as employed during the validation

study, being governed by the birds’ rates of CO2 production) even the lowest possible combustion

rates resulted in CO2 concentrations of the “respiration” air to considerably exceed our upper

detection limit of 1%. Therefore, this ethanol combustion can not be used to mimic CO2 production

levels of small animals at low ventilation rates, contrasting its application to mimic CO2 production in

humans. Alternatively, we used the following procedures to calibrate our equipment (see also Visser

1999, and Visser 2000b). Mass-flow controllers were calibrated with a soap foam flow meter (Bubble-

O-Meter, La Verne, CA, USA) before and after the trials, showing little variation over time (i.e., less

than 1%). The infrared respiration gas analyser was calibrated daily with two certified gas standards

(AGA, Amsterdam), spanning the observed CO2 gas concentrations between 0 and 0.5%. CO2

concentrations of these reference gasses were gravimetrically verified during an interlaboratory

comparison (Visser 2000b). Daily adjustments of the span of the CO2 gas analyser were very small,

and were typically less than 1% of the certified CO2 concentration. Therefore, we estimate the

maximum overall error of our gas respiration method to be about 2%.

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3.4.3.4 Isotope Analysis

First, each blood sample was distilled in a vacuum line, and the distillate was cryogenically

trapped in a 1-ml glass tube. In preliminary studies it had been verified that this distillation

procedure did not cause a change in the isotope enrichment level. Secondly, part of the distillate was

flame-sealed in 6 glass microcapillary tubes (10-15 µl each), as dictated by the IRMS analytical

procedure. The remainder of the distillate was used for LS analysis. As internal standards, a dilution

series of the DLW injection mixture with natural tap water of known isotopic composition was used.

These were analysed in the same batches as the distilled blood samples (see Van Trigt 2001a), and

had been calibrated against a range of IAEA standards.

IRMS

The capillaries were mechanically broken inside an evacuated system and frozen into a glass

vial (for more details, see Visser 2000a). In brief, we used the Epstein-Mayeda method to equilibrate

the water at 25.0°C with a known amount (2.00 ml) of added CO2 gas of known isotopic composition

(Epstein 1953). After an equilibration period of at least 48 h, CO2 was cryogenically trapped in a

glass vial for measurement with the IRMS. The remaining water was led over an uranium oven at

800 °C to produce uranium oxide and H2 gas, which was cryogenically trapped in a glass vial with

active charcoal. The CO2 and H2 were measured on a VG-SIRA 10 and VG-SIRA 9, respectively, to

obtain the 18O/16O and 2H/1H isotope abundance ratios of the original blood sample. All samples were

prepared in quadruplicate, and values were averaged. A correction for cross-contamination (Meijer

2000) was applied. Then, all isotope ratio measurements were calibrated as recommended by the

IAEA (Prentice 1990) against the gravimetrically mixed internal standards. Because relative

uncertainties in the weighing procedure are much smaller than the measurement precision with

IRMS, the internal standards are considered as absolute (See also Van Trigt 2001a).

LS

The same distilled water samples and standards were used (for more details, see Van Trigt

2001a). In brief, for each measurement 10.0 µl of liquid water was injected into the gas cell through

a rubber septum. After evaporation of the water sample, twelve absorption spectra of sample and

working standard were recorded and a mean 2H/1H and 18O/16O isotope abundance ratio was then

calculated from these spectra. For each sample this procedure was repeated 5 times, and values

were averaged. Calibration was done against the same internal standards. We have recently made a

comparison of the accuracy of IRMS and LS, a more detailed description can be found in Van Trigt

(2001a). Background samples were only measured with IRMS, due to their too small sizes.

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3.4.4 Results

3.4.4.1 Body mass, and isotope dilution space

Average body masses (Table 3.1) of the Japanese Quails were significantly higher when fed

the dry diet than when fed the wet diet (261.4 and 246.9 g, respectively, paired t-test [two-tailed]

t8 = 4.19, P < 0.002). However, total body water estimates (Table 3.1) did not differ significantly

between both diets, when calculated based on 2H or 18O dilution measured with IRMS (P = 0.29, and

P = 0.48, respectively), and when based on 2H and 18O measured with LS (P = 0.26, and P = 0.49,

respectively). These results indicate that body mass changes have been caused by a reduction of the

amount of body fat when the birds were kept on the wet diet.

To evaluate the effect of the analytical tool on the TBW estimates, 2H isotope dilution space

values were compared. It was found that for both the dry and wet diet, values based on IRMS

analysis statistically significantly exceeded those based on LS analyses (P = 0.002, and P = 0.015,

respectively), although this difference was very small (less than one percent). However, for the 18O

isotope dilution space values, it was found that they did not differ significantly for the two analytical

tools employed (dry diet P = 0.08, wet diet P = 0.19).

To evaluate differences in dilution spaces between both isotopes, for IRMS-based values it

was found that 2H dilution spaces significantly exceeded those for 18O by 3.0% on the average (dry

diet P = 0.004, wet diet P < 0.001). For LS-based values for the dry diet it was found that 2H dilution

spaces exceeded those for 18O by 1.1%, but this was not statistically significant (P = 0.09). In

contrast, for the wet diet it was found that 2H dilution spaces significantly exceeded those for 18O by

2.8% on the average (P < 0.001).

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Table 3.1: Individual-specific body masses (g), calculated amounts of body water based on IRMS,

and LS analysis (TBW IRMS, and TBW LS, respectively, g), and calculated water efflux rates based

on IRMS, and LS analysis (rH2O IRMS, and rH2O LS, respectively, g/d) assuming rG values of 0.25 and

0.5. The upper half of the table concerns the birds when fed a dry diet, the lower half when fed a

wet diet. SD is the standard deviation.

animal Mass (g) TBW IRMS (g) TBW LS (g) rH2O IRMS (g/d) rH2O LS (g/d)18O 2H 18O) 2H (rG = 0.25) (rG = 0.5) (rG = 0.25) (rG = 0.5)

1 204.5 123.3 127.5 124.0 127.9 31.2 31.6 29.6 30.12 248.7 151.0 157.2 152.1 155.2 23.3 23.6 22.7 23.13 240.7 148.0 153.0 148.7 153.3 41.9 42.6 40.1 40.74 260.9 141.4 148.3 141.9 146.4 31.8 32.3 32.8 33.35 322.6 205.1 209.4 204.3 206.2 47.9 48.6 48.0 48.86 292.9 138.2 135.3 138.5 133.2 21.7 22.1 21.0 21.37 277.4 143.1 147.5 143.4 145.4 47.1 47.8 46.4 47.28 269.1 155.0 158.0 155.9 156.0 38.1 38.7 34.6 35.19 236.2 139.2 139.9 138.6 138.1 29.9 30.4 29.6 30.1average 261.4 149.3 152.9 149.7 151.3 34.8 35.3 33.9 34.4SD 34.5 22.8 23.5 22.5 22.8 9.6 9.7 9.5 9.7

1 200.9 146.3 152.3 146.3 150.0 116.5 118.3 116.2 118.02 235.1 144.2 148.9 143.7 147.0 93.1 94.1 91.8 93.23 228.0 151.8 158.8 151.5 155.7 112.9 114.7 112.9 114.64 244.5 129.4 135.9 129.8 133.0 77.8 79.1 78.2 79.45 285.9 186.5 192.6 186.4 192.5 92.3 94.4 92.6 94.16 290.9 134.5 139.3 135.0 138.7 81.0 82.3 81.5 82.87 255.2 151.8 156.2 152.4 157.4 99.1 100.7 96.2 97.78 260.3 154.3 162.1 155.9 160.4 100.2 101.8 102.2 103.89 221.1 147.8 152.2 147.4 152.0 90.1 91.5 88.6 90.0average 246.9 149.6 155.4 149.8 154.1 95.9 97.4 95.6 97.1SD 29.5 16.1 16.4 16.1 16.9 13.0 13.2 12.9 13.1

3.4.4.2 Water efflux

As a result of the manipulation of the diet, we were able to significantly change the water

efflux by a factor of about 2.7 (P < 0.001, Table 3.1). For the dry diet, values based on IRMS

measurements were 34.8 and 35.3 g/d after assuming rG values of 0.25 and 0.50, respectively,

proving little sensitivity of the calculated water flux to assumptions concerning fractional evaporative

water loss. For the wet diet, corresponding values were 95.9, and 97.4 g/d, respectively. Only for the

dry diet, calculated values based on LS measurements were significantly lower than values based on

IRMS measurements (for rG = 0.25, P = 0.034, and for rG = 0.5, P = 0.040), but the difference was

less than 3%.

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3.4.4.3 CO2 production in relation to rG

Rates of CO2 production, as measured with respiration gas analysis, are listed in Table 3.2.

After employing a paired t-test, it was found that the values did not differ significantly for both diets

(t8 = 1.20, P = 0.27). To simplify the presentation of the results of the validation, rCO2-IRMs and rCO2-LS-

values are expressed as a relative deviation from the value obtained with the respiration gas

analysis.

Table 3.2: Relative errors in CO2 production rates of Japanese Quail as determined with the IRMS

and LS methods (IRMS error, and LS error, respectively, %), relative to the direct respiration gas

analysis (rCO2 IRGA, l/d). Values are given for presumed fractions of evaporative water loss (rG) of 0,

0.25 and 0.50. The upper half of the table lists the values when fed on the dry diet, while the lower

half lists the values when fed on the wet diet. The asterisks indicate that the average error of

calculated rate of CO2 production differs significantly from zero.

animal rCO2 IRGA (l/d) IRMS error (%) LS error (%)(rG = 0) (rG = 0.2

5)(rG = 0.5

0)(rG = 0) (rG = 0.2

5)(rG = 0.50)

1 9.2 -1.3 -4.0 -6.8 6.4 3.9 1.32 9.5 3.4 1.3 -0.7 14.7 12.7 10.73 11.6 8.3 5.3 2.4 6.8 4.0 1.14 11.8 15.8 13.6 11.4 3.7 1.4 -0.85 14.9 6.4 3.8 1.3 5.1 2.6 0.06 9.4 5.1 3.3 1.6 4.5 2.8 1.07 12.2 -1.8 -4.5 -7.2 -5.6 -8.3 -10.98 10.7 -8.7 -11.8 -14.8 -0.4 -3.1 -5.99 10.0 11.8 9.2 6.6 15.6 13.1 10.6average 11.0 4.3 1.8 -0.7 5.7 * 3.2 0.8SD 1.8 7.5 7.7 7.9 6.6 6.7 6.9

1 11.2 9.5 2.4 -4.7 9.8 2.7 -4.42 10.7 10.1 3.3 -3.6 11.3 4.5 -2.33 14.1 15.2 8.9 2.6 8.1 1.9 -4.44 11.5 9.1 3.7 -1.8 2.3 -3.2 -8.75 12.4 10.7 4.5 -1.7 6.6 0.4 -5.86 9.9 13.2 6.9 0.5 14.1 7.8 1.37 11.5 6.7 -0.3 -7.3 17.5 10.7 3.88 13.1 6.5 0.3 -5.8 -6.2 -12.5 -18.89 10.8 -4.2 -10.8 -17.5 9.1 2.6 -4.0average 11.7 8.5 * 2.1 -4.3 8.1 * 1.6 -4.8SD 1.3 5.5 5.6 5.8 6.9 6.6 6.4

For the dry diet, calculated rCO2 values tend to be rather insensitive to assumptions

concerning rG (Table 3.2). For example, in the absence of evaporative water loss (rG = 0), the

average relative error of rCO2-IRMS was 4.3%, whereas at rG = 0.5, the average relative error

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was –0.7%. For this diet, at the three different assumed rG levels, relative errors of the DLW method

were lowest at rG = 0.5 for rCO2-IRMS, and rCO2-LS, and amounted to –0.7%, and 0.8%, respectively,

and did not differ significantly from zero (P-values: 0.81, and 0.76, respectively). At these assumed

rG levels, the standard deviations for these two methods were 7.9%, and 6.9%, respectively,

indicating similar precision levels. In addition, it was found that when assuming rG = 0.25, average

errors did not differ significantly from zero for both methods (Table 3.2, P values: 0.53, and 0.21,

respectively). For rCO2-IRMS, and rCO2-LS, zero errors for the calculated mean rates of CO2 production

were obtained at rG levels of 0.43, and 0.58, respectively, to yield an average value of 0.51.

However, it has to be noted that because of the low sensitivity of rCO2 to assumptions concerning rG

for the dry diet, the application of a rG-value of 0.58 (as derived from rCO2-LS) for the calculation of

rCO2-IRMS does not lead to an error to be significantly different from zero.

For the wet diet, calculated rCO2 values are much more sensitive to assumptions concerning

rG. For example, at rG = 0, the relative error based on the IRMS-2-18 measurements was 8.5%,

while at rG = 0.5 its error was –4.3%. For this diet, lowest relative errors were observed at rG = 0.25

for the rCO2-IRMS, and rCO2-LS values, and the relative errors were on average 2.1%, and 1.6%,

respectively, but did not differ significantly from zero (P values: 0.32, and 0.51, respectively).

Standard deviations for both methods were 5.6%, and 6.6%, respectively, again suggesting that the

precision of both analytical tools is comparable. In addition, it was found that at an assumed rG level

of 0.5, there was a tendency that both methods underestimated the true rCO2 by 4.3% and 4.8% for

rCO2-IRMS, and rCO2-LS, respectively, but this was not significant (Table 3.2, P values: 0.067, and 0.068).

For these two estimates, zero error of calculated mean rCO2 was obtained at rG levels of 0.33, and

0.31, respectively, to yield an average value of 0.32.

To further statistically evaluate whether errors of rCO2 are attributable to random analytical

uncertainties, rCO2-IRMS, and rCO2-LS values were averaged for each animal and diet. Subsequently, for

each individual and diet, errors were calculated of these combined estimates relative to respiration

gas analysis. For the dry diet, it was found that for a rG value of 0.25, average error was 2.5%

(SD = 6.21), and for a value of 0.5 the average error was 0.1% (SD = 6.40). By comparison with the

SD values for the separate analytical methods for the dry diet (Table 3.2) it can be calculated that

the combined estimates are about 14% more precise. Similarly, for the wet diet it was found that the

precision of the combined estimates was on average 28% better, indicating the higher sensitivity of

the DLW method to analytical errors for the wet diet.

3.4.5 Discussion

By manipulating the water content of the diet, in the Japanese Quail, rH2O increased

significantly from 34.9 g/d when fed the dry diet (average value based on both analytical methods at

rG = 0.5) to 95.8 g/d for the wet diet (average value at rG = 0.25), i.e., an increase by 174%

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(Table 3.1). For the dry diet, the DLW method exhibited little sensitivity to assumptions concerning

rG, and for the three levels evaluated (rG = 0, 0.25 and 0.5) rG = 0.50 was found to be the most

appropriate. However, no significant error was made after assuming a rG-value of 0.25. For the wet

diet, the DLW method appeared to be more sensitive to assumptions concerning rG, a best fit being

found at a value of 0.25. The best fit of the DLW method for the dry and wet diets (i.e., over-all

error is zero) yielded rG-estimates of 0.51, and 0.32, respectively.

3.4.5.1 Comparison between observed water fluxes to lab- and field-

based allometric predictions.

A comprehensive review of literature data on water fluxes revealed that for free-living birds

levels tend to be higher by on average almost 60% than for birds under laboratory conditions (Nagy

1988). In some aquatic birds, however, such as shorebirds and ducks, water fluxes in the field tend

to be even more elevated. For example, in captive Tufted Ducks (Aythia fuligula) water fluxes were

on average 172 g/d, but they were on average 827 g/d under free-living conditions (De Leeuw and

Visser, unpublished). A similar range of values has been observed in the Red Knot (Visser 2000a).

To evaluate observed average rH2O levels for the Japanese Quail fed the dry, and wet diets,

they were compared to allometric predictions based on existing data for birds under laboratory

conditions (Nagy 1988). It was found that for the dry and wet diets, water fluxes were on

average16% below, and 140% above prediction, respectively. Similarly, based on field-based

predictions, it was found that for these diets water fluxes were on average 46% below, and 53%

above prediction, respectively. In conclusion, observed water fluxes for the dry diet were 16% lower

than allometrically predicted based on data for captive birds, and for the wet diet observed water

fluxes were on average 53% higher than allometrically predicted based on data for free-living birds.

Thus, observed water fluxes fall in the range as observed in captive and free-living birds.

3.4.5.2 Sensitivity of calculated rCO2 to assumptions concerning rG: a

recommendation for the application of the DLW method in comparative

studies

For the wet diet, rCO2 values exhibited a much greater sensitivity to assumptions concerning

rG than for the dry diet. For example, for the wet diet the relative error of rCO2-IRMS changed from

8.5% at an assumed rG-value of 0, to –4.3% for an assumed rG-value of 0.5% (Table 3.2). For the

dry diet, at both assumed rG levels the average errors were 4.3%, and –0.7%, respectively. A similar

pattern was observed for rCO2-LS values.

The uncertainty with respect to fractional evaporative water losses in free-living animals has

been subject to debate since many decades (Lifson 1966, Nagy 1980, Speakman 1997, Visser,

2000b). Presently, for the application of the DLW method in small animals, groups of scientists have

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favored three different assumptions: (1) fractionation due to evaporation does not occur (i.e., rG = 0,

Lifson 1966, Eq. 6; Nagy 1980), (2) rG = 0.25 (Speakman 1997, Equation 7.17), and (3) rG = 0.5

(Lifson 1966, Equation 35). This uncertainty strongly complicates the application of the DLW in

comparative studies as rCO2 is negatively correlated to rG. Given the large differences in water fluxes

between captive and free-living animals, it is questionable whether these issues can be adequately

resolved in lab-based validation studies.

As we have shown in Table 3.2, the sensitivity of rCO2 to assumptions concerning rG tends to

be a function of the animal’s water flux. More specifically, Visser and Schekkerman (1999) and

Visser, Boon, and Meijer (Visser 2000b) demonstrated that this sensitivity is a function of the

animal’s water flux per unit of CO2 production (i.e., the animal’s Water Economy Index, Nagy 1988).

At high water fluxes per unit of CO2 production (i.e., in animals fed the “wet” diet), there is relatively

little difference between 2H and 18O turnover rates, and any small change in the assumed rG will have

a significant impact on the calculated rCO2 value. Conversely, at low water fluxes per unit of CO2

production (i.e., in animals fed the “dry” diet), this sensitivity is much less. Given these uncertainties,

we have shown that the over-all error of the DLW method for the “dry” and “wet” diets are lowest at

an assumed rG-level of 0.25. Based on this finding, of the three rG-values currently used we propose

usage of rG = 0.25 for calculation of rCO2 in comparative studies (although in our specific study

rG = 0.33 was found to yield lowest over-all errors).

3.4.5.3 Perspectives: LS as an analytical tool for DLW studies

For DLW applications with stable isotopes, dual-inlet IRMS has traditionally been used as an

analytical tool to yield the highest over-all accuracy and precision of the method (Wong 1990). IRMS

measurement requires the conversion of the sample of the body water pool to gasses of small

molecules such as H2 and CO2. This conversion is not without problems, especially the reduction of

the water molecule to yield H2 gas, potentially affecting the precision and accuracy of the DLW

method. As we have shown above, this is especially the case in animals exhibiting high water fluxes

per unit of CO2 production. Therefore, there is a continuous need for improvement of the analytical

tools. In the framework of a larger research project (Kerstel 2001c), we now have evaluated the

novel LS method as an analytical tool. The analyses have revealed (Table 3.1 and Table 3.2), that

both accuracy and precision of LS is at about the same level as observed in traditional IRMS.

However, it has to be mentioned here that our current application of the IRMS as an analytical tool is

the product of a 45-year development, whereas this is our first application of the LS. In combination

with a higher sample throughput of LS compared to IRMS (Kerstel 2001c), we firmly believe that LS

analysis will eventually outclass IRMS analysis. Moreover, we are currently evaluating another

advantage of LS, its ability to measure 17O enrichments along with those 2H, and 18O, to yield a triply

labeled water method. This potentially has the advantage of calculating rCO2 based on 2H and 18O

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turnover rates, as well as on 2H, and 17O, a possibility that has not yet been explored in the

literature.

3.5 Conclusion

LS has proven to be a valuable tool in DLW studies. It already reaches at least the same

performance as the traditional IRMS systems and the sample throughput is higher. The fact that the

enrichment level of the reference standards is not accurately known is no limitation whatsoever to

this observation.

For more information about the assumptions, problems, pitfalls and possibilities of the DLW

method, the reader is referred to the excellent book on the DLW method by Speakman (1997)

4Glaciological application

Glaciological measurements

113

4. Glaciological applicationIsotope ratio measurements of water are widely being used in the study of the past climate. The

“proxy climate signal” that is contained in the isotope ratios is brought about by isotope fractionation

effects that occur in the meteoric water cycle. Since the magnitude of the effects is dependent on

climate indicators, especially on the local cloud temperature, isotope ratio measurements along depth

profiles of ice cores (natural precipitation archives) can be used to reconstruct Earth’s paleoclimate. By

now, several deep ice cores have been drilled both in Antarctica and on Greenland and from the

measurement results, much has been learned about the history of Earth’s climate. Here, we demonstrate

the first application of the new laser spectrometry (LS) method in ice core measurements and make a

first attempt to interpret the results. In this chapter an introduction to ice core research in general, the

measurements of ice core samples and the interpretations of the results will be presented. The latter

part is largely based on a paper submitted to “Annals of glaciology” (Van Trigt 2001b).

4.1 Introduction

4.1.1 Equilibrium and kinetic fractionation

As explained in Chapter 1, two kinds of isotope fractionation processes are distinguished:

Equilibrium and kinetic fractionation. Most often, in natural processes a combination of these two is

found, although some processes can be considered as being purely equilibrium (see also Chapter 1). The

fractionation ε for evaporation of water under equilibrium conditions is –9.71‰ and –78.4‰ at 20 ºC

for 18O and 2H, respectively (Majoube 1971). This implies that, after equilibrium is established, the

vapour is 9.71‰, respectively 78.4‰, depleted in the respective isotope abundances compared to the

water it is in equilibrium with. For kinetic fractionation it is much harder to measure accurate values,

since it is not easy to entirely separate the effect from its equilibrium counterpart. Moreover, it is often

difficult to accurately and quantitatively describe the physical processes leading to the kinetic fraction

under consideration. To give an indication of a kinetic process: diffusion of water vapour through dry air

has values for ε of about –27‰ for δ18O and –23‰ for δ2H (Merlivat 1978).

4.1.2 The Rayleigh process

The simplest model that can be used for the description of isotopic behaviour in the hydrological

cycle is the worldwide distribution of water vapour via the Rayleigh process, also known as Rayleigh

distillation. See Figure 4.1. In its simplest form, this model assumes that all water evaporates in tropical

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114

regions (Dansgaard 1964, Mook 2001). Average ocean water is defined as 0‰ (with respect to

VSMOW) and therefore in principle the composition of the vapour can be calculated, if a value for the

relative contributions of kinetic and equilibrium fractionation is assumed. This vapour will be isotopically

lighter than the ocean water. Subsequently, the water vapour is transported to higher latitudes. Due to

the prevailing lower temperatures, condensation will take place and rainfall will occur. During rainout,

fractionation will occur again (condensation is the opposite process from evaporation), thus further

depleting the remaining vapour in the heavier isotopomers. This process continues up to arrival at the

poles, where the last vapour freezes out as snow.

This very simple model already produces reasonable qualitative results in interpreting stable

isotope signals of precipitation and can be used to provide insight in the physical processes. Many

refinements to this very coarse model are possible and have indeed been made (e.g., Mook 2001).

Nowadays complicated atmospheric General Circulation Models (GCMs) are used to model the climate

system and to simulate isotope signals (Hofmann 2000). The transport, evaporation and condensation

phenomena in these GCMs are modelled in a much more reliable way; still they are in principle based on

Rayleigh processes.

Figure 4.1: Schematic representation of the Rayleigh process of the 18O depletion of water vapour when

flowing away from the Equator. For 17O and 2H similar plots can be drawn.

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115

4.1.3 Meteoric Water Line

For 2H and 18O (and 17O) the above described systematics of isotopic fractionation are very

similar. This implies a positive correlation between the 2H and 18O isotope concentrations, or abundance

ratios. Friedman (1953) was the first to report a relation between these isotopes for precipitation from

various parts of the world. Later it was quantified by Craig (1961a) as:

δ δ2 188 10H O= ⋅ + (4.1)

This relationship is known as the Meteoric Water Line (MWL). The MWL is a worldwide average

(therefore Global MWL or GMWL are also used). On a regional scale its slope and intercept may differ

from the standard values as found in Equation 4.1. Still the GMWL is useful as a starting point for further

interpretation of hydrological stable isotope data. Moreover it can help in understanding the different

processes that occur in the hydrological cycle.

The slope of 8 of the GMWL can be understood by first assuming equilibrium conditions in

evaporating and condensing water vapour. The ratio of the respective equilibrium fractionation factors of2H and 18O is slightly higher than 8; the slope decreases to the GMWL value of 8 because of a remaining

kinetic component in the evaporation process, which has nearly the same fractionation factor value for

both isotopes. It is believed and understandable that this kinetic influence appears most prominently

during the evaporation, where wind and humidity play important roles. In clouds, where condensation

takes place gradually, isotopic equilibrium is easily established. In the formation of snowflakes, however,

water vapour is deposited on smaller flakes and an additional kinetic effect is expected (Jouzel 1984,

Souchez 2000). For local MWLs slopes between 5 and 8 are being found.

The intercept of 10 in the GMWL is another consequence of the kinetic contribution in the

evaporation of (ocean) water (Kendall 1998). In local MWLs, the variations found in this intercept are

larger than in the slope.

4.1.4 Climate signal

From the model it follows that the degree of depletion compared to ocean water is dependent

on the temperature difference between the source region (in this coarse model the tropics) and the

precipitation region. Since summer-winter temperature differences tend to be larger at longer distances

from the equator, the yearly cycle shows a larger amplitude in higher altitude regions. As an example,

this seasonality is observed in the 18O and 2H isotope abundance ratios from precipitation in The

Netherlands. Figure 4.2 shows the monthly mean δ18O values of all rain and snow in three stations in

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The Netherlands between 1981 and 1995. The difference in summer and winter values is at most 3‰

for 18O, the yearly average is about –7‰. The same seasonality can be seen in precipitation in polar

regions, as an example data from Nord (Greenland) are shown (Figure 4.3). Here the summer-winter

variance is much larger, up to 15‰. The yearly average value is around –25‰. For 2H comparable

figures can be plotted. Nowadays the International Atomic Energy Agency (IAEA) and the World

Meteorological Organization (WMO) collaborate in collecting data on isotope ratios of precipitation in the

Global Network for Isotopes in Precipitation (GNIP). This database holds data from more than 500

stations world-wide, analysed by over 200 laboratories, going back to as far as 1961 (Araguas-Araguas

2000, IAEA 2001, http://isohis.iaea.org).

Figure 4.2: The average seasonal cycle of δ18O in precipitation in Groningen (53.14º N), Beek (50.54º N)

and Wieringerwerf (52.52º N) as analysed at the CIO. The plotted points are averages of the δ18O values

determined for the precipitation for that particular month over a range of 15 years (1981 – 1995). The

error bars indicate the deviations in the mean and are therewith a measure for the interannual

variability. The points have been fitted with a two-harmonics curve (CIO Scientific Report 1995-1997,

original data also available at the GNIP database).

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Figure 4.3: Average (1961 – 1972) 18O depletion in monthly precipitation in Nord (81.60º N), data taken

from the GNIP database, data analysed by University of Copenhagen, Copenhagen, Denmark.

The most obvious difference between Figure 4.2 and 4.3 is that average values are lower in

polar regions than in moderate climates (such as Groningen) due to continuing Rayleigh distillation

(Dansgaard 1964). This trend is reflecting lower average local temperatures and is referred to as the

latitude effect. Other effects that can be deduced from observations are the altitude effect (more

negative values at increasing surface elevation), the continent effect (more negative values and larger

seasonal signals further from the coast) and the precipitation effect (more negative values in periods

with more precipitation). All of these effects can be directly understood in a qualitative sense from the

Rayleigh distillation model. Like the latitude effect, the altitude effect is related to average local

atmospheric temperatures. The continent effect is caused by the gradual depletion of the atmospheric

water vapour during its journey over land. The precipitation effect is again, loosely, coupled to the local

temperature. The existence of these different influences on the isotope signal imply that the isotope

signal is certainly not a “perfect” climate measure, but rather a powerful “proxy” to climate (Lajtha

1994).

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4.1.5 Paleotemperatures (climate)

The same isotope information as in present day precipitation is in principle conserved in the

kilometers thick ice layers in the Arctic and Antarctic regions. After all, these layers can be regarded as

natural archives for (hundreds of) thousands of years of precipitation. The old ice can therefore provide

a proxy for past climate and climate changes. For recent times (the upper ice layers) we find the same

seasonality as in our local measurements on precipitation. See for example Figure 4.4. For deeper layers

with older ice, the resolution is not sufficient (due to compression of layers and to diffusion) to reveal

seasonality. Still, the average over one or more years provides us with valuable information about the

past climate, with still a better time resolution than can be obtained with other types of archives (e.g.,

pollen or ocean sediments). The typical resolution that can be obtained in e. 10,000 year old samples is

in the order of a few years, or better.

Figure 4.4: Part of the δ18O depth profile along the GISP ice core. The y-scale is from –25‰ to –35‰

with respect to VSMOW. Dark coloured peaks indicate summer periods. The age is centered around

1325 years AD. The seasonal cycles can be clearly observed [J. Glac. Vol. 20, No. 82, p 12, 1978].

With the above in mind, many studies have been done on ice cores (e.g., Dansgaard 1989,

Grootes 1993) drilled on selected locations in Greenland and Antarctica. For these locations it is

important that the layers are stacked in a well-organised way. The high pressure caused by the younger

snow makes the oldest (deepest) layers to be pressed to the sides of an ice area. The local ice dynamics

should thus be well-understood in order to be able to determine the age of the layer. After drilling a

deep core, the ice is stored for later measurements, among which the isotope ratio measurement in the

laboratory.

An interesting ice coring effort was made by a number of countries at Vostok station in

Antarctica in an attempt to construct a climate record up to 420.000 years ago (Petit 1999). This long

period allows scientists to study the past four glacial-interglacial cycles. Another example is a joint

European effort, the European Project for Ice Coring in Antarctica (EPICA). Again, the aim is to

reconstruct past changes in climate and atmospheric composition with high resolution. A linkage and

comparison with the Greenland Ice Core Project (GRIP) will be made to determine whether the changes

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observed in Greenland were global events or more regional ones (see also Mazaud 2000). Alternatively,

ice cores are being taken on smaller ice caps (Canadian Arctic, Spitsbergen) and in alpine regions

(Himalaya, Andes, Alpes) where the ice history is not going back so far. As an example, the exploration

of permanent glaciers for past hydrological and environmental parameters in the Alps can be mentioned

(Stichler, 2000).

A huge advantage of the use of these isotope records is the high resolution of the archives and

the high degree of certainty that the record is sequential and complete. Other natural archives often

suffer to a greater extent from diffusive effects. A complication of ice core archives is the uncertainty on

how to use the isotopic composition of ice sheets as a paleothermometer or, in other words, how to

relate the measured isotope ratios with past temperature. These questions have already been subject to

discussion for a long time (e.g., Mix 2001). In our present climate it is straightforward to calibrate the

isotope thermometer in many different regions on a local scale (depending on local altitude, latitude,

continental and precipitation effects), by measuring both atmospheric temperature and isotope ratios of

precipitation over a certain period (e.g., data from the GNIP database). The isotope ratios turn out to be

linearly dependent on atmospheric temperature and for the other climate parameters, relationships can

be found as well (Dansgaard 1964). This is also referred to as the spatial isotope/surface temperature

relationship. Dansgaard has already found a good correlation, valid for coastal and polar locations with

an average change in δ18O of 0.7‰ per degree Celsius (Dansgaard 1961) and this value was later

confirmed for Greenland (Johnsen 1989). For Antarctica, values of 9‰ per degree Celsius for 2H are

estimated (Salamatin 1998). If the assumption is made that these relations did not change in time, it is

possible to translate paleo isotope abundances into temperatures via the so-called transfer functions.

Indeed, for the past few hundred years in Greenland a significant correlation of stable isotope

concentrations and both local as well as more regional meteorological and climatic parameters exists

(White 1997): It can be concluded that under the present climate the assumptions on stability of the

influence of the parameters hold. Over longer time scales, however, many complicating factors exist that

have not yet been fully understood. These remain uncertainties in the input of the General Circulation

Models (GCMs) which are used to model the paleotemperatures from measurement results. Amongst the

uncertainties are changes in (1) surface altitude, (2) seasonal distribution of precipitation, and (3) the

evaporative origin of the moisture in time (Jouzel 1997, Werner 2000). It might well be too blunt an

approximation to just using one fixed number for relating isotope ratios with temperature. Indeed,

strong evidence exists that it is not correct to use the spatial relations over the entire time scale spanned

by the ice core. Direct temperature measurements in the ice core boreholes suggest that local surface

paleotemperatures were much lower than predicted by the results from ice core measurements. From

these borehole temperatures it was concluded that at the time the last glacial period reached its lowest

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temperatures (the so-called last glacial maximum, LGM) the average temperature in Greenland appears

to have been 22ºC colder than today (Johnsen 1995). This is almost double the difference derived on

the basis of older/initial analyses of ice core data. Current insights in the isotopic make-up of the ice

sheet using glacial circulation models are siding with the borehole derived temperature. A remaining

question and point of discussion is what both methods do really measure; ice cores are primarily

sensitive to temperatures in the atmosphere and clouds at the top of the inversion layer at the moment

the precipitation fell, while boreholes reflect more directly the average local surface temperatures. It is

believed that at the LGM the precipitation was more concentrated in the summer and that the

temperature inversion was stronger than at present day. Thus, the values do not necessarily contradict

each other.

Another problem in reconstructing climate history based on ice core measurements is to

determine the exact age of the deep ice. In modern interpretations a number of parameters is

simultaneously used for dating. In the upper layers one can measure seasonality in isotope ratio signals

and thus count layers, comparable to counting tree rings. In deeper, more compressed layers, however,

due to diffusion the signal has almost disappeared (and the yearly slices of ice become too thin due to

compression). The classical approach to dating is then calculating the age/depth relationship using ice

flow and ice-accumulation models (Lorius 1985). Although ice flow models have substantially been

refined in the course of years, several alternative techniques have also been presented. Among those

are radiocarbon dating of old atmospheric CO2 (Van der Wal 1994) and measuring the CH4 concentration

(Blunier 1998), both trapped in bubbles in the ice. Another method is the counting of layers using a

systematic combination of parameters, such as visual stratigraphy, electrical conductivity, laser-light

scattering from dust, volcanic signals (also dated by e.g., deep-sea isotope records), and major ion

chemistry signals. For example, a core with a length of over 3000 m has been dated in this manner up to

160.000 years BP (Meese 1997). Uncertainties are typically a few percent, but up to 20% for the deepest

layers. And yet another means is to fit the major features of the stable isotope signal to Milankovich

oscillations of the earth’s orbit which have a known frequency (Salamatin 1998).

The interpretation of ice core information is a continuing debate, but as our understanding

increases, more and more of the information about the past climate will be disclosed.

4.1.6 Deuterium excess

The so-called “deuterium excess” d was defined by Dansgaard (1964) as:

d H O= − ⋅δ δ2 188 (4.2)

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and it can be considered to be a measure of the difference in behaviour between 18O and 2H, or the

contribution of kinetic isotope fractionation effects to the formation of the precipitation. For the GMWL

(per definition) a value of 10‰ is found. Local MWLs often have slopes that differ in time (over the

year, but also over ages or millennia). The value of deuterium excess for local measurements is per

definition (4.2) calculated with a fixed regression of δ18O versus δ2H with a slope of 8, and thus, when

the true slope (ratio) for some reason changes in time, the calculated deuterium excess changes. As an

example, Figure 4.5 shows the trend for the deuterium excess for Groningen precipitation between 1964

and 1996.

Figure 4.5: Deuterium excess for Groningen precipitation. Its trend is compared with the NAO index for

the period 1964 – 1996. Although some long term correlation seems likely, evidence for interannual

variability correlation is lacking.

A comparison with the North Atlantic Oscillation (NAO, a quasi-periodic change in sea surface

temperature and atmospheric moisture in the North Atlantic) is made in this plot as well. It is likely that

some correlation exists between the two since most of the Groningen precipitation originates from the

Northern Atlantic ocean, but it is only observable in the long-term trend and not in the interannual

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variability. The average seasonal cycle in Groningen of the deuterium excess (detrended) is shown in

Figure 4.6. Here, a clear and significant pattern exists. Its interpretation, however, is not

straightforward.

Figure 4.6: Average seasonal cycle in the deuterium excess, the measured data were detrended and

averaged over the years. The points have been fitted to a two-harmonics curve. The error bars are a

measure for the interannual variability (CIO Scientific Report 1995-1997)

A changing regime of evaporation in the source area (caused by changing humidity, wind, or

waves or by a seasonal variation of the source region) will alter deuterium excess values, because the

relative kinetic contribution to the evaporation process will change. A change in the form of precipitation

(e.g., snow instead of rain) or other processes in the clouds can influence the kinetic contribution and

therewith the deuterium excess signal in a similar way (Ciais 1994). Calculations in which it was tried to

derive individual contributions of possible factors have been made (Jouzel 1982). Another example is the

Law Dome shallow ice core in Antarctica. Here, seasonal δ18O and δ2H cycles were found to reflect the

local temperature, but the deuterium excess signal is shifted four months backwards in phase (Delmotte,

2000). From this, the different sources of the precipitation in the different seasons were identified. A

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comparable lag is seen in high-altitude regions of the Greenland ice sheet and also in this case it was

possible to draw conclusions concerning the origin of the water vapour (Dansgaard 1989).

For polar regions it is now widely accepted that deuterium excess is above all affected by (1) the

temperature of the moisture source and (2) the absolute humidity in the source region of the

precipitation (Fisher 1991). Complex GCMs can nowadays predict these factors quite well for the present

day situation. However, relatively simple Rayleigh-type models can do this too, under most

circumstances (Armengaud 1998). Still, we have to keep in mind that these models all start with the

well-known present-day circumstances. The same models do not (yet) succeed in a reliable

reconstruction of the past climate using reverse-modelling of the paleo 2H and 18O isotope ratio signals,

let alone the deuterium excess. Furthermore, both the simple and the complicated models can only

simulate large scale effects, while measurements are always done on a local scale (Jouzel 1996).

Nowadays, in many studies deuterium excess values have been determined, providing

information additional to that of 18O or 2H values alone. The extra information that becomes gradually

available in this way has not been fully exploited yet.

4.1.7 Traditional ice core isotope measurements

Stable isotope ratio measurements are usually performed on dedicated isotope ratio mass

spectrometers (IRMS). For measuring the stable isotope abundance ratios of 18O/16O and 2H/1H in water,

extensive sample pre-treatments are necessary. Traditionally, off-line methods are used. In the case of

deuterium measurements, water is reduced to H2 gas over hot uranium (Bigeleisen 1952) or zinc

(Friedman 1953, Coleman 1982). In the case of 18O ratio measurements, the isotope signal in water is

often transferred to CO2 of known isotopic composition by equilibration, often referred to as the

Epstein/Mayeda technique (Epstein 1953). These techniques and some alternatives are described in

more detail in the introduction of this thesis (Chapter 1).

It requires an enormous effort to analyse an isotope depth profile over the entire length of a

typical ice core, since the traditional techniques are laborious and ice coring delivers many thousands of

samples. Therefore a number of techniques has been developed in order to automate the traditional off-

line techniques. For example, for δ18O, on-line automatic equilibration systems have been built (Johnsen

1997), and also for δ2H measurements the traditional method has been automated (Vaughn 1998). Both

methods are based on traditional techniques, but are optimised and automated to handle a larger

number of samples.

More recently, new on-line continuous-flow (CF) techniques have been developed that use

different approaches. For deuterium measurements, equilibration of hydrogen gas with water using a

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catalyst and alternative on–line reduction methods coupled to continuous flow IRMS (CF–IRMS) are used

(Meijer 1999 and references therein, Brand 1996). As a catalyst for the H2–H2O equilibrium reaction,

platinum is used (Horita 1988, Coplen 1991). Reducing materials reported in the literature include

chromium (Gehre 1996) uranium (Vaughn 1998, Hopple 1998) and zinc (Socki 1999). On–line pyrolysis

of many different sample types, water included, coupled with CF–IRMS is another promising

development (Begeley 1997). For 18O, however, the problems are smaller, and consequently less efforts

have been taken to improve the existing automated systems, based on traditional Epstein/Mayeda

processes. Still, some alternatives were published: again on–line pyrolysis (CO is formed) coupled with

CF–IRMS (Kornexl 1999, Wang 2000), or on–line isotopic exchange with CO2 bubbles in a long capillary

at elevated temperatures (Leuenberger 2001).

From all these new techniques the best results report precisions of about 0.05‰ for δ18O and

about 0.6‰ for δ2H and these are comparable to the best precisions attainable with traditional

methods. However, international interlaboratory comparisons in which selected laboratories perform

measurements in ring tests, show larger spreads than the mentioned values. When calculating

deuterium excess the situation gets even worse, because there is no correlation between the deviations

of the isotopes. In other words, a laboratory that gets somewhat lower than average results for one

isotope might give slightly higher values for the other. Therefore Meijer (1999) reports the spread of

deuterium excess in an interlaboratory comparison to be almost ±4‰ (2σ). Note that this is an

important observation for comparison of deuterium excess results of different laboratories, but not

necessarily for the observation of trends.

4.2 Groningen ice core measurements

The text in this paragraph is based on a paper published in “Annals of Glaciology” (Van Trigt 2001b).

4.2.1 Abstract

We report on the first application of a new technique in ice core research, based on direct

absorption infrared laser spectrometry (LS), for measuring 2H, 17O, and 18O isotope ratios. The data is

used to calculate the deuterium excess d (defined as δ2H - 8·δ18O) for a section of the Dye-3 deep ice

core around the Bølling transition (14,500 BP). The precision of LS is slightly better than that of most

traditional methods for deuterium, but not for the oxygen isotopes. The ability to measure δ17O is new

and is used here to improve the precision of the δ18O determination. Still, the final precision for δ18O

remains inferior to traditional isotope ratio mass spectrometer (IRMS). However, its accuracy may be

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better, as the LS measurements are not affected by sample contamination by, e.g., the drilling fluid.

Therefore, deuterium excess was calculated from a combination of the LS and IRMS isotope

determinations.

4.2.2 Introduction

Isotope ratio measurements of δ18O and δ2H of water have been and are being widely used in

the study of the past climate. The “proxy climate signal” that is contained in the isotope ratios is brought

about by isotope fractionation effects that occur in the meteoric water cycle. Since the magnitude of

these effects is dependent on climate indicators (especially the local cloud temperature at the time of

precipitation) isotope ratio measurements on ice cores can be used as a temperature proxy (Dansgaard

1964). By now, several deep ice cores have been drilled, both in Antarctica and on Greenland, and

analysed for a variety of parameters, such as electrical conductivity, dust, chemical constituency and

isotope concentrations. From these measurements, much has been learned about the paleoclimate.

However, in practically every single case only δ18O or δ2H has been measured; rarely both isotopes have

been measured simultaneously and then only in a small section of the core, basically due to the cost and

time–consuming nature of these measurements.

From 1979 to 1981 the deep ice core at a location named Dye–3 (South Greenland) was drilled

by a team of Danish, Swiss and American scientists. It was part of the well–known Greenland Ice Sheet

Program (GISP). The total length of the core amounted to 2037 m until bedrock (Dansgaard 1982).

From, among others, δ18O measurements on this core, the paleoclimate has been reconstructed (see

Figure 4.7). A most interesting event was found at a depth of 1786 m (Figure 4.8), where an abrupt shift

in δ18O (and nearly all other parameters studied) was located. Since then, this Younger Dryas/PreBoreal

(YD/PB) transition has been examined in great detail (Dansgaard 1989). The δ18O level in the core

between 1784 m and 1788 m shifted upwards by 5‰ within a 50 year period. Based on present day

spatial δ18O - temperature relations, Dansgaard and co–workers supposed that this indicates a 7°C

temperature rise. Later it was argued that this value should be as high as 15°C, based on bore hole

temperature calibration of the δ18O values in Central Greenland (Johnsen 1995, Cuffey 1995). The age of

the ice at 1786 m below surface was dated at 10,720 ± 150 year BP by counting annual layers in δ18O

and electrical conductivity of the core. A more precise date of 11,500 ± 70 years BP for this transition

has been obtained from the GRIP core by counting annual layers in several high resolution chemical and

isotope profiles (Johnsen 1992). This event defines the end of the last glacial period (Weichselian

glaciation) and was preceded by a complex structure of rapid climatic shifts. The YD/PB transition is the

last transition in a climate oscillation, named the Bølling/Allerød–Younger Dryas (B/A–YD) oscillation. The

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observed shift in δ18O at the onset of the Bølling period has equal magnitudes as the YD/PB transition,

thus indicating similar enormous climate changes on a short time scale.

Figure 4.7: Example of the δ18O analysis of the Camp Century (Greenland) ice core. A 120,000 period is

covered on the y-axis. Different periods are marked in the Figure (reproduced from Dansgaard, 1973).

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Figure 4.8: a: Radiocarbon dated δ18O profile along a 4 m long sediment core from the Gerzensee

(Switzerland); b: δ18O profile along 150 m of the deep Dye–3 ice core. The 1700 – 1850 m depth interval

spans the entire pleistocene to holocene transition, including the Bølling/Allerød–Younger Dryas

oscillation; c: Concentration of continental dust; d: Detailed δ 18O record through the

Younger–Dryas–Pre–Boreal transition, a strong shift in 50 years is observed; e: Deuterium excess of the

same period, the transition occurs in 20 years; f: Dust concentration, shows the same shift as deuterium

excess. Figure is reproduced from Dansgaard (1989).

Deuterium excess d, defined by Dansgaard (1964) as:

d H O = − ⋅δ δ2 188

can be considered a measure of the difference in behaviour between 18O and 2H, or the contribution of

non–equilibrium isotope fractionation effects to the entire hydrological cycle. A changing regime of

evaporation in the source area will alter deuterium excess values because the relative non–equilibrium

contribution to the evaporation process will change. For polar regions it is now widely accepted that

deuterium excess is above all affected by (1) the temperature of the moisture source and (2) the

absolute humidity in that region (Johnsen 1989, Fisher 1991, Armengaud 1998). For example, for Law

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Dome, Antarctica, seasonal δ18O and δ2H cycles were found that both reflect the local temperature, while

the deuterium excess signal is four months backwards shifted in phase (Delmotte 2000). From this the

most likely sources of the precipitation were identified, as well as their seasonal dependence. A

comparable phase–lag is seen in high–altitude regions of the Greenland ice sheet and here too

information concerning the origin of the water vapour is obtained (Johnsen 1989).

For the YD/PB transition in the Dye–3 ice core, the δ2H profile has been measured as well

(Dansgaard 1989). Having both isotope profiles for this section, the deuterium excess could be

calculated (see Figure 4.8). It showed a shift of about –5‰, starting at the same time as the δ18O–shift,

but reaching a new stable value about twice as fast as δ18O. The time–scales of the d and δ18O changes

were initially calibrated at 20 and 50 years, using dating work done by Hammer and co–workers (1986),

who claim a 2 cm annual layer thickness in the YD and a 3 cm thickness shortly after the YD/PB

transition. More recent insight is based on a comparison with the well–dated GRIP ice core, yielding

mean annual layer thicknesses of 1.7 cm for the early PB, 0.7 cm in the YD, 0.9 cm in the Allerød,

0.95 cm in the Bølling and 0.45 cm in the pre–Bølling period. We estimate the accuracy of these figures

to be close to 10%. They are in fair agreement with annual high resolution PIXE data from sections of

the Dye–3 core (Hansson 1993). This makes it necessary to revise the time scale of the YD/PB climate

shift upwards to 50 and 100 years, for the deuterium excess and δ18O transitions respectively. These are

still very fast climate changes. A possible explanation is that the sea–ice cover retreated rapidly due to

the return of the North Atlantic current, thus creating a vast area of initially cold surface water as an

additional source of moisture (Dansgaard 1989). The immediate cause is believed to be the return of the

North Atlantic Current to higher latitudes and an associated northward shift of the polar front (Bond

1995, Broecker 1995, Ruddiman 1981).

From all isotope (and other) evidence it can be concluded that the climate in the last glacial

period has shown abrupt and radical changes in ocean circulation, polar front position, storminess,

humidity, atmospheric temperature and evaporation conditions. The δ18O data from Dye–3 have been

confirmed and validated by measurements on other cores, such as GRIP (Dansgaard 1993) and GISP2

(Grootes 1993), but so far this is not true for the deuterium data in the last glacial period.

In the last years we have developed a new technique for measuring isotope ratios in our

Groningen laboratory (Kerstel 1999). The method is conceptually different from the existing methods

that are all based on IRMS. Instead, our apparatus uses an infrared laser to measure the direct

absorption spectrum of gaseous water in order to obtain its isotope ratios (δ2H and δ18O, as well as

δ17O). We have already shown its application in the biomedical field (Van Trigt 2001a, 2001c). This

technique, apart from being elegant, is potentially very fast and can easily be automated. Advantages of

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the new method over the traditional ones include the absence of sample preparation. In fact, even

volatile contaminants do in practically all cases not interfere with the measurement, due to the very high

selectivity obtained by high–resolution infrared spectroscopy. We directly obtain isotope ratios for

deuterium and both oxygen isotopes. The δ18O measurement is not (yet) as accurate as with

conventional techniques, but further progress is foreseen. However, for δ2H we already achieve a higher

precision than with traditional methods, while the measurement of δ17O is new. Although it is known that

for all natural, meteoric water samples a fixed relationship between 17O and 18O holds and thus, in

principle, no new information can be derived from the 17O signal (Meijer 1998), the 17O measurement

can be used together with this fixed relationship as a check on the δ18O data, and possibly to improve its

precision.

Here we demonstrate the application of the newly developed method to the measurement of the 18O/16O

and 2H/1H isotope abundances in water. As a real–world test on glaciological samples we have

performed a detailed investigation of the deuterium excess in the Bølling transition in the Dye–3 deep ice

core.

4.2.3 Methods

4.2.3.1 Measurements

The Laser Spectrometer (LS) technique is based on direct absorption spectrometry, using a small

section (~1.3 cm-1) in the 2.7 µm region of the infrared absorption spectrum of water. This section

contains rotational-vibrational transitions for all four isotopomers of interest (i.e., 1H16O1H, 1H17O1H,1H18O1H, and 2H16O1H). For water samples with natural isotope abundances the absorption strengths of

these transitions are of the same order of magnitude and, although the spectral features are close to

each other, they are well resolved. We can use the low-pressure, gas phase, infrared absorption

spectrum for isotope ratio determinations since the intensities of the transitions are a direct measure of

the abundances of the corresponding isotopomers.

To record an absorption spectrum we scan a tunable, single mode laser (a Color Centre Laser or

FCL, Burleigh) from 3664.05 cm-1 to 3662.70 cm-1 in about 5000 steps. For each step of the laser we

record the laser power before and after the passage through the gas cells using phase sensitive

detection. The spectra of the water samples in the four multiple-pass gas cells are thus recorded

simultaneously. A 10 µl liquid water sample is injected into the cells, assuring a final (partial) pressure of

the water vapor of about 13 mbar, well below the saturation vapor pressure. One of the four gas cells

always contains a working standard, while the others contain either reference water or an unknown

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sample. For each sample injection eight successive scans were recorded. A full measurement cycle,

including introduction of the sample, takes about 40 minutes. Since we have four gas cells, we measure

three samples (or standards) in one run together with the working standard. Where duplicate

measurements did not agree to within 3 times the mean standard deviation, an extra measurement was

made. This was needed for typically 10% of the samples. In the Bølling transition section of the core,

measurements were performed in fourfold. The error due to memory effects amounts typically to less

than 5% of the difference in δ-value between previous and current sample. In this study this error is

generally smaller than the analytical error. Special care had to be taken only after measuring VSMOW or

SLAP, because their isotope ratios differ significantly from that of the samples and the international

reference standard, GISP. In these cases the new samples were injected and removed once, before the

actual measurement commenced.

4.2.3.2 Standards

As in traditional IRMS, LS needs a working standard to compare the samples with, in order to

obtain reliable isotope ratio determinations. We chose a working standard as close as possible to the

expected sample values, namely a mixture of old “leftover” batches of Greenland Ice Sheet Precipitation

(GISP). Initially, the isotope ratio of this mixture was not known exactly, since fractionation might have

occurred during storage of the different bottles over the years. Still, we later found that its value was

close (just 2.5‰ higher for δ2H) to the values of fresh GISP.

As reference materials for the calibration of the system, we used fresh VSMOW, SLAP and GISP.

The use of primary calibration standards is defendable in this stage of the work, largely thanks to the

very small amounts of water that are used.

The ratio of measured standards to samples for this project was about 1:3, Table 4.1 shows the

numbers in more detail.

Table 4.1: Total number of single measurements made on the different waters for the entire Dye-3

measurement project. In all cases Old GISP mixture was used as the working standard. For all samples,

isotope ratios for all three isotopomers were acquired.

Old GISP mix GISP SLAP VSMOW Samples

Total # of

measurements178 57 53 69 807

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4.2.3.3. Samples

We measured 279 water samples of the Dye–3 ice core. Their age varies from 9200 year BP to

14,700 year BP, thus including both the YD/PB transition and the Bølling transition. As stated in the

introduction, these samples have been previously measured for δ18O over the entire core, but not for

δ2H. The YD/PB transition has been extensively studied for deuterium excess, as well as for other climate

indicating parameters (Dansgaard 1989). In those experiments all water was used up and we could

therefore not include this particular section in our current programme. The depth resolution of the

sampled ice–core section between the depths of 1730 m and 1812 m is 55 cm, 27.5 cm, 11 cm or 5 cm,

depending on the desired resolution for the specific period. In the Bølling transition the resolution is

5 cm, corresponding to roughly 7 years per sample.

4.2.3.4 Calibration

We apply a calibration procedure to scale our raw measurement results to the internationally

accepted values of the calibration materials VSMOW and SLAP, complying with the procedure

recommended by the IAEA (Gonfiantini, 1984). It should be noted that in IRMS several types of

corrections are necessary as well, but these are not as well understood and usually much bigger in

magnitude than those in LS.

The different gas cells exhibit different zero-point offsets. These turn out to be primarily

associated with the optical alignment of the instrument. Because the alignment is very stable we can

easily and reliably correct for these offsets. The values are around zero with a magnitude of the order of

one per mil.

The raw measurement results are also corrected for small differences in gas cell pressure

(amount of water) between the reference and sample cells. This linear correction is very well understood

and can be calculated from simulated absorption spectra (Kerstel 1999). Moreover, the magnitude of the

corrections is small (typically below 0.1‰) for all isotopes.

The gas cell and isotope dependent scale expansion factors lie in a range from 0.98 to 1.02,

which is much smaller than what is usually seen in IRMS. The calibration procedure is described in more

detail elsewhere (Kerstel 1999). Note that the above scale corrections constitute a VSMOW/SLAP scale

normalization as prescribed by the IAEA (Coplen 1988). After removal of obvious outliers, the final

results are averaged for each sample.

Chapter 4

132

4.2.4 Results and Discussion

4.2.4.1 Measurement precision

An indication of the precision of the LS measurements is the single measurement standard

deviation (SD) of repeated measurements on the same sample. As each sample was measured only two

to four times, one sample will not provide reliable statistical information. Therefore we take the mean of

all calculated SD’s as a measure. We then find the single measurement precision to be ~0.6‰ for δ2H,

~0.5‰ for δ18O and ~0.3‰ for δ17O. The statistical spread of the standard deviations (histogram) is

fairly well represented by a Gaussian curve. These results are comparable to those obtained in analyses

based on repeated measurements of the same water sample (in particular VSMOW), which were carried

out in the framework of previous studies (Kerstel, 1999; Van Trigt 2001a). The better performance of

the LS system in the case of δ17O is attributed to the higher signal-to-noise obtained on the H17OH

spectral feature, compared to the H18OH line.

The relationship between δ18O and δ17O for meteoric waters established by Meijer and Li (1998),

enabled us to calculate values for δ18O from the measured δ17O. In the case of a linear fit forced through

zero for the inferred δ18O against the measured δ18O, we find a slope of 1.0023(20). We conclude that to

good approximation these inferred (indirect) and measured (direct) δ18O values may be treated as

duplicate determinations. The δ17O measurements thus serve as a check on the δ18O measurements and

may even be used to improve the precision of the latter by doubling the number of independent δ18O

determinations. We averaged the δ18O measurement and the calculated δ18O value (inferred from the

δ17O measurement), using the squared errors as weighing factors, resulting in a precision of the

combined determination of ~0.4‰. The combined result (i.e., the weighted mean) does not differ

significantly from the direct δ18O measurement.

4.2.4.2 2H and 18O isotope records

The depth profiles of the δ2H and δ18O records determined by means of LS are shown in Figure 4.9. They

show qualitatively the same behavior and the major transitions are clearly visible in both. As mentioned

before, samples from the interval between 1784 and 1788 m (YD/PB transition) were no longer

available.

Glaciological measurements

133

Figure 4.9: δ2H (GrLS2) and δ18O (GrLS18) depth profiles as measured with the Groningen LS apparatus.

As explained in the text, the water samples around 1785 m (the YD/PB transition) were used-up in the

original measurements (Reyk18, given here for comparison purposes) by Dansgaard and co-workers,

thus leaving a gap in the Groningen records. The 1989 deuterium measurements of Dansgaard and co-

workers (Saclay2) fit well in the gap in the GrLS2 record.

The LS δ18O record (GrLS18) can be compared directly to the old Reykjavik IRMS data (Reyk18),

also shown in Figure 4.9. The median δ-values for the two curves amount to –30.83‰ (279 samples)

and –30.89‰ (281 samples) for the GrLS18 and Reyk18 records, a strong indication that the data have

been properly calibrated. Closer inspection of the two isotope records reveals just one small section of

the core at the end of the Bølling transition in which the two records deviate. Figure 4.10 shows this

Bølling transition region in detail.

-40

-35

-30

-25

-20

-15

-400

-360

-320

-280

-240

-200

1740 1760 1780 1800

GrLS18Reyk18

GrLS2Saclay2

δδδδ18O

(‰

) δδ δδ2H

(‰)

Depth (m)

Chapter 4

134

Figure 4.10: Depth profiles for δ18O around the Bølling transition (about 14,500 years BP). Groningen LS

and IRMS data are shown, as well as the old Reykjavik IRMS data. The deviation of the old and new

measurements for the samples between 1809 m and 1810 m is clearly visible.

Between 1809 m and 1810 m (24 data points) the GrLS18 and Reyk18 records have median

delta-values that differ by 1.1‰. Such a big difference cannot be explained by fractionation (e.g.,

accompanying evaporation) during storage, transportation, or sample preparation. Contamination, most

likely by fragmentation of the drilling fluid in the ion-source of the mass spectrometer, would have

resulted in a higher IRMS value, not a lower one, with respect to laser spectrometry. Moreover, the

Reykjavik IRMS system is equipped with special cold-traps to prevent such contamination from having an

effect on the measurements. Also, it is highly unlikely to have affected a small section of the core only.

In fact, ice-core analyses of our Copenhagen laboratory (that, as the Groningen laboratory, does not

take such elaborate measures against drilling fluid contamination) in general show a systematic and

more or less constant offset, up to about 0.5‰. In conclusion, we have no explanation for the observed

-34

-33

-32

-31

-30

-29

-28

-27

1806 1807 1808 1809 1810 1811 1812

GrLS18GrMS18Reyk18

δδδδ18O

(‰

)

Depth (m)

Glaciological measurements

135

local discrepancy between the GrLS18 and the Reyk18 records. For this reason, we re-measured 74

samples belonging to the Bølling transition (1806 to 1813 m) by means of mass spectrometry in

Groningen. This partial record we will refer to as GrMS18. Its median value is 0.43‰ higher than the

GrLS18 record in the same depth range, tentatively attributed to drilling fluid contamination of the ice-

core, as also observed in the past in our Copenhagen laboratory. The GrMS18 record shows a

substantially smaller scatter, reflecting the higher precision of IRMS with respect to δ18O measurements

by means of laser spectrometry. After shifting the GrMS18 record downwards by 0.43‰, a nearly

perfect agreement with the GrLS18 record is obtained, as can be seen in Figure 4.10.

The transition (increasing temperature with time; note that the time scale is from right to left)

occurs in about 1.5 m of ice. If we take the average annual layer thickness during the transition to be

equal to 0.7 cm, this corresponds to about two hundred years or twice as long as the YD/PB transition.

During the Bølling transition δ18O increases from about –33.5‰ to –28.5‰ and the δ2H signal increases

from about –260‰ to –220‰. This is similar to the YD/PB transition and suggests a similar

temperature rise of about 15°C. We also notice in the δ18O record 2‰-strong cold event at 1811.0 m

depth in the middle of the Bølling transition lasting only 25 years. This climatic event is not as clearly

depicted in other Greenland isotope records.

4.2.4.3 Deuterium Excess

For the samples analysed in this study, no deuterium depth profile was acquired previously, so there is

no data to compare to. Only for the YD/PB transition (the section between 1784.20 m and 1788.05 m)

δ2H has been measured (Dansgaard 1989). But, as explained before, this section is missing in our data-

set. However, as Figure 4.9 shows, the old δ2H record (Saclay2), measured previously in the stable-

isotope laboratory at Saclay, fits well in the “gap” in the present record (GrLS2), again demonstrating the

quality of the calibration of the data, as well as the integrity of the 2-decade old samples (δ2H in

particular is very sensitive to fractionation processes).

From other deuterium excess measurements on large numbers of ice core samples, as well as from

several laboratory ring-tests, we conclude that a typical deuterium excess precision, using conventional

IRMS techniques, amounts to about 1.8‰ (based on a precision of ~1‰ for δ2H and ~0.1‰ for δ18O).

Although some glaciological isotope laboratories claim a precision well below the figures used here,

these claims may prove to be exaggerated if it comes to the accuracy of the measurements, particularly

of the deuterium excess. Inter-laboratory comparisons carried out by the International Atomic Energy

Agency have demonstrated the difficulty of maintaining such high levels of accuracy across a number of

specialized isotope laboratories (Lippman 1999). This is especially reason for concern when two isotope

Chapter 4

136

measurements (δ2H and δ18O) are used to calculate the deuterium excess. We would therefore strongly

argue in favor of participation of the ice-core isotope community in similar ring-tests. The LS

measurements alone (precision of ~0.6‰ and ~0.4‰ for δ2H and δ18O, respectively) would yield a

precision for deuterium excess of about 3.8‰, which is of the same size as the expected natural

(climate) variations. We therefore calculate the deuterium excess during the Bølling transition using the

GrLS2 deuterium record together with the GrMS18 oxygen-18 record. The latter has been shifted

downwards by 0.43‰ in its entirety, in order to best overlap with the more accurate, well-calibrated,

GrLS18 record. As mentioned before, this procedure is justified by earlier observations of systematically

higher IRMS δ18O-values when no proper precautions are taken to prevent residual drilling fluid from

interfering with the measurements.

Figure 4.11 presents the deuterium excess depth profile in the range of 1806 m to 1813 m, around the

Bølling transition. The RMS deviation of the data points with respect to the smoothed curve amounts to

1.4‰. This equals the estimated uncertainty in the deuterium excess determination, based on the

measurement uncertainties in δ2H and δ18O (0.6‰ and 0.1‰, respectively). The curve indicates that

deuterium excess decreased by about 6‰ within a time span of about 70 years at the onset of the

warming. The residual structure on the curve, in particular the two small dips at 1811.5 m and

1809.5 m, fall within the measurement uncertainty and we hesitate to associate these with minor climate

events. The over all pattern is then rather similar to what has been observed previously for the YD/PB

transition (Dansgaard 1989) and we may indeed assume that the same mechanism that caused the

YD/PB transition was also operative during the Bølling transition. It would be interesting to compare the

Bølling isotope records to the other climate indicators, in particular dust. If indeed the rapid shift in

deuterium excess at the onset of the Bølling transition signals a northward shift of the polar front, in

response to a return of the North Atlantic current to higher latitudes, one would expect to see a

decrease in dust in parallel with the deuterium excess shift, indicative of a more humid, milder, and less

stormy climate.

The general picture emerging from these isotope and other studies is that the climate in the last

glacial period has shown abrupt and radical changes in ocean circulation, polar front position,

storminess, humidity, atmospheric temperature and evaporation conditions.

Glaciological measurements

137

Figure 4.11: Deuterium excess, d = δ2H - 8·δ18O, for the Bølling transition. The solid curve is obtained by

smoothing of the GrLS2/GrMS18 data and serves mainly to guide the eye. The RMS deviation of the data

with respect to the smooth curve is 1.4‰. The shift in deuterium excess at the Bølling transition is

about 6‰ as was found for the YD/PB transition 26 m higher up in the core (Dansgaard 1989).

4.2.5 Conclusions

Laser Spectrometry is a new and elegant way of measuring stable isotopes in ice core samples.

Its sample throughput is already quite high (50 sample/day) and can easily be increased further. The

single measurement precision obtained for δ2H measurements (~0.6‰) is very competitive with

traditional IRMS methods. For δ18O the precision (~0.5‰) is still almost one order of magnitude worse,

while the measurement of δ17O (~0.3‰) is new. The δ18O precision can be improved to ~0.4‰ when

δ18O and δ17O measurements are combined. When IRMS δ18O measurements and LS δ2H measurements

0

2

4

6

8

10

12

1806 1807 1808 1809 1810 1811 1812

GrLS2/GrMS18

d (

‰)

Depth (m)

Chapter 4

138

are combined, a precision of ~1.4‰ for deuterium excess measurements can be achieved, comparable

to IRMS-only. Where IRMS δ18O measurements can be severely affected by drilling fluid contamination, if

no proper precautions are taken, LS is virtually immune to such effects, thanks to its extremely high

molecular and isotopomer selectivity. If this contamination is present, the LS δ18O results are more

accurate (but not more precise) than those obtained by IRMS.

The YD/PB transition (11,500 BP) as measured by Dansgaard and co-workers is not the only sharp

transition at the end of the last glaciation. Some 3000 years earlier, the Bølling transition showed an

about equal temperature rise, in approximately two hundred years time. Deuterium excess shifted

similarly in about 70 years. Together, these observations indicate that the underlying mechanisms may

have been very similar during the onset of the Bølling interstadial and the YD/PB climate transition

5Unusual samples

Unusual samples

141

5. Certification of an unusual water sampleThe first two major applications of the Laser Spectrometer (LS) have been described in

Chapter 3 and 4. In this chapter, a more exotic application of the laser spectrometric technique is

described. This specific application can serve as an example of the more general application of the LS

method in certifying isotopically labelled species as sold by many suppliers. The stated enrichments can

then be checked.

5.1 Analysis of 17O content in Ontario Hydro heavy water

In this section, an experiment will be described in which the 17O content is measured on a water

sample with an extremely high deuterium content. The LS provides a manner to measure the 17O

abundance, after some modifications have taken place in the measurement procedure and the data

analysis, compared to the previously discussed settings. The text is based on the measurement report on

this experiment (Kerstel 2001a).

5.1.1 Introduction

The deuterated heavy water analysed here (99.92% D2O) is used as the detection medium in a

Canadian experiment designed to detect solar neutrinos (Waltham 1992). Because of the large neutron

capture cross–section of 17O, there is interest in knowing its abundance to a reasonable level of

accuracy. Previous measurements of the 17O abundance have resulted in two rather different values:

5.5·10–4 (already long ago determined by Atomic Energy Agency of Canada), and a more recent value

of 17·10–4 measured with the advanced electron cyclotron resonance ionisation source on the 88"

cyclotron at Berkeley (Simpson 2001). The natural abundance of 17O (see Chapter 1) equals 3.8·10-4.

Here we report on the measurement of the 17O abundance by means of the Stable Isotope Laser

Spectrometer (LS) at the Groningen Centre for Isotope Research. The spectrometer is based on direct

absorption of infrared radiation passing about 20 m through the gas phase water sample. The intensities

of selected isotopomer lines in the sample spectrum are compared to the corresponding intensities in the

spectrum of a reference material in order to calculate the isotope ratios of interest. For each heavy

isotopomer we scale the intensities of spectral features belonging to this isotopomer using the intensity

of an abundant H16OH spectral feature. Principally due to the very low abundance of the rare isotope,

the so–determined molecular isotope ratio [H17OH]/[H16OH] is for all practical purposes equal to the

atomic isotope ratio [17O]/[16O].

Chapter 5

142

5.1.2 Constants and definition of symbols

In Table 5.1 the constants are listed which are used in the calculations for the isotope

abundances.

Table 5.1: Constants used to calculate the isotope abundances.

parameter value uncertainty description Ref

mH 1.0078825 amu atomic mass 1H Verkerk (1986)

mD 2.014102 amu atomic mass 2H Verkerk (1986)

m16 15.99492 amu atomic mass 16O Verkerk (1986)

m17 16.99913 amu atomic mass 17O Verkerk (1986)

m18 17.99916 amu atomic mass 18O Verkerk (1986)17R0

1) 3.8·10–4 0.2·10–4? 17O isotope ratio of VSMOW

(=[17O]/[16O])

Li (1988)

18R0 2.0052·10–3 5·10–7 18O isotope ratio of VSMOW

(=[18O]/[16O])

Baertschi (1976)

δ17O(GS–23) -3.33‰ 0.3‰ 2) 17RGS-23/17RVSMOW-1

δ18O(GS–23) -6.29‰ 0.05‰ 18RGS-23/18RVSMOW-1

1) Li (1988) gives 17R0 as (3.799 ± 0.009)·10–4, (corresponding to 0.03790 atom%). Considering the

difficulties associated with its determination and the controversy in the literature concerning the best

value, we will base our error analysis on an assumed uncertainty of 0.2·10-4, more than one order of

magnitude larger than the one–sigma error in 18R0 as claimed by Baertschi (1976). As we will see, in this

case the error in 17R0 and our measurement error contribute about equally to the final error in the 17O

abundance of the heavy water sample.

2) Error based on laser–spectrometric measurement. Almost one order of a magnitude smaller when

calculated from δ18O in combination with the mass–dependent fractionation formula of Meijer and Li

(1998).

5.1.3 Procedure

The procedure for measuring this sample is different than for natural or DLW samples: Since it is

basically D2O (instead of H2O), all of our regular spectral features (Chapter 2) disappear. There are no

working standards available to compare the sample spectrum to, so we need to dilute the sample first

with water of known isotopic make-up and a natural 2HOH level.

Unusual samples

143

5.1.3.1 Dilution

The original sample was diluted with an isotopically well–characterised local standard, known as

GS–23 (δ2H = – 41.0‰, δ17O = – 3.36‰, and δ18O = – 6.29‰ on the VSMOW-SLAP scale). As

mentioned before, dilution is required to increase the initially extremely weak signal on the spectral

features of interest: H16OH and H17OH (and H18OH). In addition, the dilution factor should be high

enough to bring the intensities of nearby spectral features belonging to 2HOH down to a level where

they no longer interfere unacceptably with the spectral features belonging to H16OH and H17OH (and

H18OH). But the mixing ratio may not be so large as to wash out the H17OH signal. A compromise in

these demands was found using mixing ratios of sample : local standard water (GS-23) of about 1:30

and 1:75. The exact mixture rates (A and B) can be found in Table 5.2. The resulting 2HOH

concentrations thus become about 3% and 1.3%, respectively.

Table 5.2: Mixing parameters for the two diluted heavy water mixtures.

Mixture A (1:75) Mixture B (1:30)

Ms (mass D2O sample) (g) 1.0996 3.1751

Mb (mass GS–23 buffer) (g) 74.2682 93.9224

f := Ms/(Ms+Mb) 0.013151 0.029531

∆f/f (relative weighing error) 0.0005 0.0002

5.1.3.2 Isotope ratio measurement

The measurements were basically carried out as described in Chapter 2. However, the 17O line

present in the standard spectral region of our spectrometer (3662.7 cm-1 to 3664.0 cm-1) has weak 2HOH

absorptions present on each of its shoulders. This is not a major problem for natural abundance water

samples or enriched samples as encountered in biomedical applications. The highest enriched samples

we have measured so far (δ2H = 15000‰) contain about 0.2% of 2HOH. In the present case, the 2HOH

concentration is at least 6 times higher, and the resulting absorptions give rise to 2HOH lines that are

more intense than the 17O line itself. This feature is illustrated in Figure 5.1. In the case of Mixture B, the

intensity of the 2H lines (“162”) accompanying 17O (“171”) even saturate. These strong neighbouring

lines influence the 17O line in an unacceptable manner. We therefore located a nearby spectral region

with a more favourable set of lines for this specific goal. This region is from 3660.1 cm-1 to 3661.6 cm-1

Chapter 5

144

and encompasses the lines given in Table 5.3. Figure 5.2 presents typical spectra obtained in this region

for both the reference water (GS–23) and the 75–fold diluted D2O sample. Here, the only 2HOH line

present is much weaker than in the previous section (and not even visible for the natural abundance

spectrum of GS-23), while the H17OH and the H18OH have sufficient intensity for accurate

determinations.

Table 5.3: The transitions used in the determination of the 17O and 18O abundances.”161” is used to

indicate H16OH, “181” for H18OH, “171” for H17OH, and 162 for 2HOH.

isotope frequency (cm-1) intensity (cm/molec) temp. coeff (‰/K)

161 3660.376 6.1·10-23 -2.8

181 3660.844 2.3·10-23 -0.24

171 3661.373 2.2·10-23 -1.5

Figure 5.1: “Traditional” spectral region

0

1

2

3

4

5

6

3663 3663.2 3663.4 3663.6 3663.8

GS-23 :reference

Mixture B (1:75)

Wavenumber(cm- 1)

161171

162162

162

Unusual samples

145

Figure 5.2: New region for 17O and 18O.

Since no working standard of isotopic make-up comparable to the samples is available, we used

the local GS–23 as the working standard. The δ17O and δ18O values of GS-23 are close enough to the

expected sample values, but the δ2H value is very much different between sample and working standard.

A number of independent δ–measurements were carried out, each consisting of 10 or 20

individual laser scans with in one gas cell the measurement reference material and in the other gas cell

the diluted heavy water sample. The measurements are summarised in Table 5.4. Even after changing

the spectral region, the procedure used by Kerstel (1999) to calculate the δ–values proved too sensitive

for the overlap of the H17OH line at 3661.373 cm-1 with the (very) weak 2HOH absorption on its shoulder

(see Figure 5.2). It was therefore deemed necessary to write a new analysis routine that fits a

superposition of Voigt profiles with variable position, height and width to the experimental spectra. The

new procedure proofed slightly inferior to the old procedure when tested on “normal” water samples and

routine measurements, but far superior in the present case where the deuterium concentration differs so

dramatically between the working standard and sample water mixtures A and B.

0

1

2

3

4

5

3660.2 3660.4 3660.6 3660.8 3661 3661.2 3661.4 3661.6

cell 2: GS-23:reference

cell 3: Mixture A (1:75)

Wavenumber(cm- 1)

161

181

181

161

1 71

162

Chapter 5

146

The measured 17O and 18O δ–values show a strong positive correlation (see Figure 5.3). This

suggests that measurement–to–measurement variations are related to sample–handling problems, e.g.,

fractionation processes inside or outside the gas cell occurring during or after sample injection.

Figure 5.3: Corrected results for Mixture A (1:75) showing the correlation between the 17O and 18O

measurements. The horizontal lines represent the weighted means.

5.1.3.3 Analysis

Mixture A (1:75)

The four measurements (#1 to #4 in Table 5.4) on Mixture A (1:75) yield weighted averages of:

δ δ17 18and :O ‰ O ‰GS GS− −= ± = ±23 234 6 0 6 11 3 0 7 ( . . ) ( . . )

and referencing with respect to VSMOW:

δ δ δ17 17 17O O O

‰

VSMOWcorr

GScorr

VSMOWGS ( ) ( )

( . . )

= + ⋅ + −

= ±

−−1 1 1

1 16 1 0

2323

and:

2

3

4

5

6

7

2

4

6

8

10

12

1 2 3 4

δ17O δ18O

δδδδ17O

(‰

)δδ δδ

18O

(‰)

Measurement index

Unusual samples

147

δ δ δ18 18 18O O O

‰VSMOWcorr

GScorr

VSMOWGS ( ) ( )

( . . )

= + ⋅ + −= ±

−−1 1 1

3 85 1 123

23

The above two final values for δ17O and δ18O are consistent with atomic fractional abundance of

the heavy water sample of:17fs = (5.08 ± 0.63)·10–4 and: 18fs = 3.53·10–3

The error in 17fs was calculated assuming an error ∆(δ17O) = 1‰ for the measurement of δ17O

and a relative weighing error ∆f/f=0.000 5 for the measurement of the mixing ratio (relative

concentration) of sample and GS–23 buffer solution. The error in 17fs also includes a contribution from

the estimated uncertainty in the isotope ratio of VSMOW: 17R0 = 3.8.10-4 ± 0.2.10-4. If we assume that17R0 is known with zero uncertainty, the error in 17fs reduces to 0.29.10-4. In other words: the uncertainty

in the absolute 17O isotope concentration of the international calibration material VSMOW contributes for

more than 50% to the uncertainty in the 17O concentration of the heavy water sample.

(Note: 17f refers to the concentration [17O]/([16O]+[17O]+[18O]), while 17R refers to the isotope

ratio [17O]/[16O]. Thus 17f = 17R/(1+17R+18R) and 17R=17f/16f).

Table 5.4: Overview of the measurements. The δ–values are expressed with respect to GS–23 and have

been corrected for the zero–offset. The errors presents one standard deviation.

# date Serie # # scans sample quantity

(µl)

(δ17O)raw

(‰)

(δ18O)raw

(‰)

1 20010320 *3 10 Mixture A

1:75

10 5.6 ± 0.9 12.6 ± 1.1

2 20010321 *1 20 Mixture A

1:75

10 4.0 ± 1.2 9.3 ± 2.1

3 20010321 *2 20 Mixture A

1:75

5 4.5 ± 2.0 10.8 ± 1.2

4 20010323 *1-4 40 Mixture A

1:75

10 3.0 ± 1.3 9.7 ± 2.1

5 20010323 *5-8 40 Mixture B

1:30

10 8.0 ± 1.2 18.3 ± 1.3

Mixture B (1:30)

Chapter 5

148

Similarly, we obtain the following result for measurement #5 on Mixture B (1:30):

δ 17O ‰VSMOWcorr ( . . )= ±4 6 1 6 and: δ 18O ‰VSMOW

corr ( . . )= ±11 9 1 7

These values for δ17O and δ18O are consistent with atomic fractional abundance of the heavy

water sample of:17fs = (4.80 ± 0.46)·10-4 and: 18fs = 3.22·10-3

In this case, the error in 17fs was calculated assuming an error ∆(δ17O) = 1.6‰ for the

measurement of δ17O (more overlap with 2HOH line and thus greater uncertainty than when measuring

Mixture A and a relative weighing error ∆f/f=0.000 2 for the measurement of the mixing process. As

before, it includes a contribution from the very conservatively estimated uncertainty in the isotope ratio

of VSMOW: 17R0 = 3.8·10-4 ± 0.2·10-4. If we assume that 17R0 is known with zero uncertainty, the error

in 17fs reduces to: 0.21·10-4. And again the uncertainty in the absolute 17O concentration of the

international calibration material VSMOW contributes for more than 50% to the uncertainty in the 17O

concentration of the heavy water sample.

5.1.4 Concluding remarks

The values of the mixture A and B agree very well(with their errors excluding the contribution of

the uncertainty in 17R0): 5.08 ± 0.29 and 4.80 ± 0.21, respectively. The weighted average is

4.90 ± 0.16. However, if the uncertainty in 17R0 is taken into account, the measurements on the two

diluted mixtures may be combined into the final values:17fs = (4.9 ± 0.5)·10-4 and: 18fs = 3.4·10-3

The error given is an estimated value based on the two measurements presented above and

taking into account that these may be correlated through the determination of the zero offset.

6Future prospects

Future

151

6. FutureThe current LS set-up has proven to be a reliable and useful tool in different applications. Especially

for 2H, the accuracy and precision are competitive to the traditional IRMS method. For 18O, the

measurements in the natural abundance range are not yet precise enough. Its ability to measure 17O in

water is, to our best knowledge, the only method available for small amounts of water. In order to

achieve these performances, the system has been optimised in the last years. Therefore, from the

present set–up only small increases in performance can still be expected. For substantial improvements,

major changes in the set–up and method are needed. In this last chapter, such an improvement of the

LS set-up will be discussed. Further, some possible other applications of the technique will be touched

on.

6.1 Further development of LS

At this moment, the FCL is the only laser source in the 2.7 µm region that meets our demands.

As explained in Chapter 2, this is the most favourable section (fundamental vibration) to work in. Next to

this region of absorption, water has bands around 1.4 µm, 1.9 µm and between 5 µm and 7 µm. The

intensity of the absorption features in the last section is about equal to the ones around 2.7 µm and

would thus equally well be suited for absorption measurements. Its strongest disadvantage is that this

far in the infrared non-standard, expensive optics are needed. The 1.4 µm and 1.9 µm overtone regions,

on the other hand, show absorption strengths almost one order of a magnitude lower than in our

present region.

The replacement of the laser itself by a tunable diode laser (TDL) is expected to increase the

system’s performance drastically, both in terms of sample throughput and precision. The use of a TDL

has the distinct advantage that the frequency scan speed improves dramatically. Scanning can be

performed by simply changing the current of the laser and this can be done at very high frequencies, if

required. The speed at which the (absorption) data can be collected will in practice than become the

limiting factor. The high scan speed will eliminate the influence on the measurements of any processes

on longer time–scales. This includes slow exchange of (physi- or chemi-) sorbed water or environmental

(e.g., temperature) changes. Furthermore, within the short time–scales of one individual measurement,

a large number of laser scans can be made in order to reach a higher signal-to-noise ratio (S/N) than

possible with the FCL. Another distinct advantage of these lasers is that frequency modulation (FM) can

be employed. This will result in elimination of the base–line uncertainty, which is inherent to the

amplitude modulation (AM) technique as currently used. This will thus reduce the sample-to-sample

measurement uncertainty (increase precision) and thus improve the intrinsic precision of the system

Chapter 6

152

itself. Our attempts to use FM on the FCL (by modulating the cavity end mirror) did not succeed, since

the modulation that can be reached is not strong enough (too small modulation depth compared to the

absorption line widths). Other advantages of TDLs are their relatively low costs and thier ease of

operation. Together with their high scan speed (or short measurement time) they offer a potential

inprovement of LS based isotope ratio measurements.

Since the TDL offers so many advantages, we decided to perform a pilot experiment, using a

III–V semi–conductor TDL at 1.393 µm as the LS light source, in collaboration with the University of

Naples (Livio Gianfrani and Gianluca Gagliardi; see also Gagliardo 2000). In spite of the lower absorption

strengths in this spectral region, we hope to obtain a good indication as to what improvements a TDL in

the more favourable 2.7 µm region could lead.

- 1 0

- 5

0

5

1 0- 0 . 0 1

0

0 . 0 1

0 2 5 6 5 1 2 7 6 8 1 0 2 4

Sig

nal

(a

rb.

u.)

Resid

ual

Index

HOD HOD/H1 7OH H1 7OH H1 6OH H1 8OH

reference

sample (= reference)

Fig

ure 6.1: Small section of the water spectrum in the 1.393 µm region as obtained with LS with a TDL as

its light source, showing spectral features belonging to the four isotopomers of interest. The second

feature from the left is due to two nearby absorptions of 2HOH and H17OH and is not used in the

analysis. The spectra are not corrected for the increase in output power (from left to right) that

accompanies the tuning of the laser.

Future

153

A typical spectrum as obtained with LS with the current TDL is plotted in Figure 6.1. The two gas

cells were filled with the same water sample. The upper panel shows the result of the least squares

procedure in which the sample spectrum is fit in small sections around each spectral feature to the

reference spectrum. The sample-to-reference line ratios determined in this manner are used to evaluate

the isotope ratios of the sample (initially referenced to the GS–23 local reference material; Kerstel 1999).

We performed a series of measurements in which the same standard water was repeatedly

injected in both cells. In total about 200 measurements were done for δ2H and δ17O and 120 for δ18O.

The first two isotopomers can be measured simultaneously, while the latter needs a slightly different

spectral range in order to find a usable line. Each measurement is the average of 20 laser scans. This

resulted in values for the precision (standard deviation) for δ2H of 3.1‰, for δ17O of 1.1‰ and for δ18O

of 0.53‰ (Kerstel 2001c). The S/N level of the TDL suggests that a precision of better than 1‰ for all

isotopomers can be achieved. At the moment another laser, with better specifications, is being tested.

The combination of a high scan speed and the possibility to use FM in a TDL, can improve the

precision of LS. We believe that this can already be the case for the 1.393 µm TDL that is currently being

testing, but as soon as TDLs become available in the 2.7 µm region, dramatically further improvements

in precision may be expected.

6.2 Future possible applications

6.2.1 Stratospheric water

In the last decades, increasing attention has been paid to the understanding of the earth’s

atmosphere and climate and the parameters that influence it. It has been found, for example, that the

water in the troposphere always shows a fixed relationship (within the measurement accuracy) between18O and 17O (Meijer 1998). Compared to the moisture source, it is depleted in both oxygen isotopes

because of the slow fractionation caused by the freezing out of water during the cooling of the air while

the moisture ascended. Water in the stratosphere which is originating from the troposphere shows the

same relationship. However, water in the stratosphere which is produced by methane oxidation shows a

totally different relation between 18O and 17O. Other fractionation processes are then responsible for this

anomalous behaviour, which is referred to as mass independent fractionation (MIF). The underlying

processes that cause MIF are complex and only qualitatively understood (e.g., Mauersberger 1987). The

resulting anomaly in isotope behaviour can be used as a measure for the relative contributions of

tropospheric and stratospheric processes.

Chapter 6

154

LS is a good tool to study these anomaly effects due to its ability to measure 17O. When a

compact diode laser based apparatus will be available, it can become possible to perform the

measurements on stratospheric water in real-time.

6.2.2 Other molecules

So far, the application of LS to measurements on natural, enriched and uncommon water

samples has been focussed on. However, these applications do not yet show the full possibilities of the

LS technique. Some short comments on the measurement of other species than water will be given.

The LS method is not limited to water only, since nearly all of the hydrogen containing molecules

exhibit strong absorptions in the same spectral region (around 3 µm). These transitions are, as in water,

associated with fundamental X–H stretching vibrations (X = C, N, O, ...). It must be noted that for all

species that can be studied using LS, it is necessary to identify a section of the absorption spectrum in

which the isotopomers of interest do all absorb light with about the same (and high enough) intensity.

This will often be the limiting factor for the success of a specific application. The data analysis procedure

as applied in the routine water measurement can be adopted, or the alternative procedure as introduced

in Chapter 5.

In order to be able to measure the isotope abundance ratios in ethanol, for example, it is only

necessary to identify a suitable section of its absorption spectrum. The set-up does not need to be

adjusted, so measurements could start as soon as within a few weeks.

In the case of ethanol, LS can distinguish between the two different positions that are available

for the deuterium atom: The isotope abundance ratio of the easily exchangeable deuterium connected to

the O atom will carry different information than the abundance ratio of the deuterium atom connected to

a carbon atom. For bigger molecules (e.g., other alcohols or toluene), even more different positions exist

which can be discriminated between. Information about this so–called “site–selectivity” might yield

valuable information, especially in food authencity research. This in an important field from an economic

point of view (e.g., Krueger 1982, Martin 1996).

Routine measurement of CO2 in conventional samples using the LS seems not to be very likely,

since IRMS is nowadays able to determine it with high precision. Still, the LS could be used to determine

the 17O abundance in stratospheric CO2 with higher precision and accuracy than in IRMS, which suffers

from mass overlap with 13C. Further, it is imaginable that a LS apparatus can even be used in order to

monitor the CO2 isotope ratios in air, without having to extract it from its matrix first. This could be done

at the same time as the measurement of concentrations or isotope ratios of other gasses. The biggest

problem here is that the line width of the spectral features will be very high as a consequence of

pressure broadening, so overlap of lines can easily occur. Probably, measurements under reduced

Future

155

pressure conditions are necessary. A compromise between line width and line intensity (path length!)

must be found. Also for economical reasons a LS set–up (especially if based on a TDL), can be

advantageous over IRMS.

LS is in principle able to measure isotope ratios of many hydrogen containing species. The

principles are the same as for water measurements. It will be hard to beat IRMS in terms of precision,

but where it comes to site–selectivity, measurements on “raw” samples and cost, LS can be favourable.

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Abbreviations

169

Abbreviations

AM Amplitude ModulationAOM Acousto-Optic ModulatorAR/AR Anti-Reflective (on both sides)BM-# BioMedical standard (local Groningen standard for enriched samples)BP Before PresentCF-IRMS Continuous Flow Isotope Ratio Mass SpectrometryCIO Centrum voor Isotopen Onderzoek (Centre for Isotope Research)CW Continuous WaveDLW Doubly Labelled WaterDSP Digital Signal ProcessingEA Elemental AnalyserEPICA European Project for Ice Coring in AntarcticaFCL Farbe Centre Laser (Color Center Laser)FM Frequency ModulationFSR Free Spectral RangeFT-IR Fourier Transform InfraRedGISP Greenland Ice Sheet PrecipitationGCM General Circulation ModelGNIP Global Network for Isotopes in PrecipitationGRIP Greenland Ice Coring ProjectGS-## Groningen StandardHITRAN HIgh resolution optical Transmission spectrum of the Atmosphere and

iNdividual gasesIAEA International Atomic Energy AgencyICE Intra-Cavity EtalonIRMS Isotope Ratio Mass SpectrometryLGM Last Glacial MaximumLIA Lock-in AmplifierLS Laser SpectrometryMIF Mass independent fractionationMWL Meteoric Water LineNAO North Atlantic OscillationNDIR Non-Dispersive InfraRedNIST National Institute of Standards and TechnologyNMR Nuclear Magnetic ResonanceRES Relaxed Exited StateSLAP Standard Light Antarctic PrecipitationSMOW Standard Mean Ocean WaterSNIF-NMR Site-specific Natural Isotopic Fractionation studied by Nuclear Magnetic

ResonanceS/N Signal to Noise ratioSTIRLAS Stable Isotope Ratio Laser SpectrometryTBW Total Body WaterTLW Triply Labelled Water (also used for local Groningen standards)VSMOW Vienna Standard Mean Ocean Water

Summary

171

Summary

Accurate measurements of the relative isotope abundance of light elements are widely used as a

tool for studying many different physical, chemical and biological processes. One of the most often used

applications is the measurement of the stable isotopes in water (18O and 2H). These measurements are

applied in, amongst others, hydrology, biomedicine and paleoclimatology. This thesis describes the

development of a new technique for the measurement of stable isotope ratios in water and its

application to the last two of the mentioned fields.

In Chapter 1, first some general information on isotope measurements and their application is

given. The abundance of stable isotopes is expressed as a deviation of the absolute isotope abundance

ratio of the sample from that same ratio of a standard. Then, the processes which are responsible for

the change in the isotope abundance ratios caused by physical, chemical or biological processes,

referred to as isotope fractionation, are described. The importance of proper calibration and

normalization and the difference between accuracy and precision are emphasised, items that are very

important in the field of isotope physics. Further, the common techniques for isotope ratio

measurements, which are all based on mass spectrometry, and some new developments, both based on

mass spectrometry and on optical techniques, are being described.

Chapter 2 gives an extensive description of the newly developed method, which is based on

laser spectrometry. A tunable infrared laser source (2.73 µm) is scanned over a small part of the

ro–vibrational spectrum of water vapour. The samples are kept in gas cells. The power of the laser beam

both before (“power”) and after passing the gas cell (“signal”) are recorded on cooled InAs detectors

using amplitude modulation detection. The spectra obtained this way are compared to the same

spectrum of a reference water with known isotopic composition. Among the advantages of the new

techniques are the lack of laborious and potentially hazardous chemical sample preparations (as

necessary in traditional methods) the ability to automate the system and thus the high throughput

possible, the relatively low costs, and the possibility of measuring 17O, next to 18O and 2H. Much

attention is being paid to the corrections that have to be applied to the raw measured value of the

isotope ratio. Most of the corrections are quantitatively understood, and the remaining corrections are

smaller than the analogous correction in the mass spectrometric techniques. The laser technique is

shown to be applicable to both the measurement of isotope ratios in the natural range and in the

enriched range, as often applied in biomedical experiments. Thanks to proper calibration, the accuracy of

the new method is comparable to the mass spectrometer method. In the enriched regime, the

measurement precision for the new method is better for 2H and competitive for 18O. For the natural

Summary

172

abundance range, however, the 2H measurement precision is about the same as in traditional methods,

while for 18O it is still worse.

In Chapter 3, the successful application of the new technique in a biomedical experiment is

described: The doubly labelled water method. It is based on the administration of both 18O and 2H as

isotopically enriched water to an animal or human. After an equilibration period, an initial sample of the

body water pool, usually blood, is taken. Since 18O can leave the body water pool both as water and as

CO2 gas, while 2H can only leave as water, the difference between the two is a measure for the CO2

production. In order to apply the doubly labelled water method, one must be able to accurately measure

the isotope ratios of the highly enriched aqueous samples as these often occur in the analyses. The

problem of proper absolute calibration caused by the lack of knowledge about initial isotope

concentrations at these levels of enrichment is described. After some preliminary experiments, we have

validated the widely applied doubly labelled water method in Japanese Quails on diets with different

water content. This way, an important assumption of the method, the fraction of evaporative water loss,

was examined. It was found that an assumed fraction of 0.25 is in most cases appropriate.

Another successful application of the new technique is described in Chapter 4. Since the isotope

abundance ratio of precipitation is dependent on, amongst others, temperature, the isotope ratios along

a depth profile of stacked archives of ice are a proxy for past temperatures. These ice sheets are being

found around both poles and in mountainous regions, the most important ones on Greenland and

Antarctica. By combining the measurement results of 18O and 2H, the so-called deuterium excess is

obtained, providing possibly even more information such as the humidity and temperature in the source

region of the precipitation. The interpretation of the deuterium excess data, however, is not

straightforward and not quantitatively understood. Still, it is a parameter additional to the isotope

abundance ratio measurements of a single isotope. We have measured part of an ice core drilled in

central Greenland. This part originated from about 1800 m below surface and its age was between 9200

and 14700 years BP. This is around the transition from the last ice age to the current interglacial. The

sharp Bølling transition, around 14500 years BP, was studied in great detail. It was found from 18O

measurements that the temperature rise of about 7˚C occurred in only 200 years time, and that

deuterium excess signal, which is an indication for the duration of the process that caused the change,

shifted in only 70 years.

In Chapter 5, a more exotic and unique application of the new laser spectrometric technique is

described. Highly enriched deuteriumoxide as used in a solar neutrino experiment was checked on its 17O

content. To this end, it was necessary to dilute the deuteriumoxide with natural water. To circumvent

the problem of overlapping absorption features caused by the high 2H content, a slightly different part of

the water spectrum and an alternative technique for fitting the spectral features were used for

Summary

173

measurements. This resulted in a more accurate determination of the 17O content than would be possible

by any other method.

In Chapter 6, some expected future developments of the apparatus are described. The use of a

different light source, based on a diode laser, is the most promising. This way it is possible to employ

frequency modulation and a much higher scan speed. At this moment, however, diode lasers are only

available in the less favourable 1.4 µm region, but even in this case we expect at least comparable

performance as with the current laser source. When a 2.7 µm diode laser comes available, a major

improvement may be expected. Finally, some possible future applications of the laser spectrometric

technique are shortly described.

Samenvatting

175

Samenvatting

Laser spectrometry voor stabiele isotopen analyse aan water

Biomedische en paleoklimatologische toepassingen.

Dit proefschrift is één van de tastbare resultaten van mijn promotieonderzoek aan het Centrum

voor IsotopenOnderzoek (CIO) van de Rijksuniversiteit Groningen. In die periode is er een methode

ontwikkeld voor het meten van de zware isotopen in watermoleculen en is die methode gebruikt voor

enkele zeer interessante toepassingen.

Isotopen zijn de zwaardere varianten van normale atomen. Van alle chemische elementen

bestaan isotopen, sommige daarvan zijn radioactief, de andere stabiel. Uit nauwkeurige metingen van de

hoeveelheid isotopen in allerlei stoffen kan bijzonder veel informatie worden verkregen. Isotopen zijn

namelijk bijzonder goede tracers of volgstoffen: hun gedrag is bijna exact gelijk aan dat van de

“normale” atomen, maar toch net genoeg anders om verschil te kunnen zien. Zo kan worden bestudeerd

hoe stoffen zich verspreiden en gedragen in tal van fysische, chemische en biologische processen.

Binnen het CIO worden de isotopen van verschillende lichte atomen routinematig gemeten.

De bekendste toepassing van isotopenmetingen is ouderdomsbepaling met behulp van het

radioactieve koolstof-14. Vooral binnen de archeologie wordt deze methode veelvuldig toegepast. Een

andere, nieuwere, toepassing van isotopenmetingen is gelegen op het gebied van klimaatonderzoek. Het

CIO heeft daarvoor sinds kort een eigen 60 m hoge “snuffelpaal” aan de Waddenzee om lucht te

verzamelen en ook de (isotopen)samenstelling van lucht, opgestuurd in flessen vanuit diverse

vergelijkbare Europese meetstations, wordt in Groningen gemeten. In dit proefschrift wordt echter een

andere tak van isotopenonderzoek behandeld, namelijk het onderzoek aan water. De toepassingen die in

dit proefschrift worden beschreven liggen op het terrein van de biologie en het onderzoek van het

klimaat van het verleden.

Het meten van de stabiele isotopen, dus ook die van water, gebeurt normaal gesproken met

massaspectrometers. Voor gassen werkt die methode uitstekend, maar voor water is het niet mogelijk

om deze techniek rechtstreeks te gebruiken. Daarom wordt het watermonster altijd eerst omgezet in een

gas. Als we de aanwezige hoeveelheid zwaar waterstof, deuterium, willen meten betreft dat meestal een

omzetting naar waterstofgas, als we zwaar zuurstof willen meten, wordt het water meestal in chemisch

evenwicht gebracht met koolstofdioxidegas. Deze omzettingen berusten op chemische processen en zijn

Samenvatting

176

potentieel onnauwkeurig, tijdrovend, arbeidsintensief en soms zelfs gevaarlijk. De arbeidskracht die dit

vergt in combinatie met de specialistische apparatuur maakt de methode bovendien duur. In dit

proefschrift wordt de ontwikkeling van een hele nieuwe methode voor isotopenmetingen aan water

beschreven die deze nadelen niet kent en die ons toestaat direct aan watermonsters te meten. De

methode is gebaseerd op een in wezen eenvoudig principe, namelijk dat van de absorptiespectrometrie.

De apparatuur die is ontwikkeld bestaat in feite uit een lichtbron, een monsterhouder en een

detectiesysteem. De lichtbron is een infrarode laser met verstembare golflengte. De monsterhouder, een

gascel, is een glazen buis van ongeveer 50 cm lengte met spiegels aan beide kanten. Hierin wordt 10 µl

vloeibaar water geïnjecteerd hetgeen vervolgens volledig verdampt. De laserbundel wordt de gascel in

geschenen door een gat in één van de spiegels en vervolgens door middel van de spiegels vele malen

heen en weer gekaatst. Zo wordt een weglengte van ruim twintig meter bereikt. De absorptie van het

licht bij verschillende golflengten wordt met een detector gemeten. Uit het zo verkregen

absorptiespectrum kan de concentratie van ieder isotoop worden bepaald.

Omdat de veranderingen die wij willen meten bijzonder klein zijn, moet de meetnauwkeurigheid

erg groot zijn. Alle metingen worden, net als bij de traditionele methoden, gemeten relatief ten opzichte

van een standaard. Een van de gascellen bevat daarom altijd een van onze goed bekende standaarden.

Met de vier gascellen die momenteel zijn opgesteld kunnen we ongeveer vier monsters per uur meten en

dat is sneller dan momenteel mogelijk met de conventionele methodes zoals beschikbaar op het CIO. De

precisie waarmee we nu de waterstofisotopen kunnen meten is voor verrijkte monsters hoger dan die

van de traditionele massaspectrometermethode en voor natuurlijke monsters minstens gelijk. Voor de

zuurstofisotopen is de meetmethode nog niet vergelijkbaar nauwkeurig voor de natuurlijke monsters,

maar wel voor de verrijkte.

Twee nuttige en interessante toepassingen van de nieuwe methode worden in dit proefschrift

uitgebreid beschreven.

De eerste is op biologisch vlak. Als aan een dier een dosis van de zware isotopen van waterstof

en zuurstof wordt toegediend in de vorm van zogenaamd dubbel gelabeled water, vermengt zich dat

zeer snel met het lichaamsvocht. Daarna verdwijnen zowel de toegediende waterstof- als de

zuurstofisotopen weer langzaam uit het lichaam, via drie belangrijke wegen, namelijk met urine,

waterdamp in de adem en zweet. Voor het zuurstofisotoop is er echter nog een vierde manier om het

lichaam te verlaten, namelijk als koolstofdioxidegas bij de uitademing. Het verschil in de snelheid

waarmee de waterstof- en zuurstofisotopen worden uitgestoten is dus een maat voor de hoeveelheid

koolstofdioxidegas die het dier geproduceerd heeft gedurende de meetperiode. Als bovendien bekend is

welk voedsel het dier eet, is nu de energiehuishouding van het dier bekend. Vooral voor dieren die onder

Samenvatting

177

extreme klimatologische of fysiologische omstandigheden leven is dat een interessant gegeven. Zo is

bijvoorbeeld in het verleden met isotopenmetingen met massaspectrometers onderzocht hoe

keizerpinguïns kunnen broeden in de kou van Antarctica. In dit proefschrift staat beschreven hoe onze

nieuwe meetmethode kan bijdragen aan het verbeteren van de nauwkeurigheid van de dubbel gelabeld

watermethode. Met metingen aan kwartels hebben we aangetoond dat de methode minstens zo goed

werkt als de traditionele meetmethode, en bovendien sneller is. Het apparaat is verder nog gebruikt voor

het bestuderen van kanoeten, kleine zeevogels die in de Waddenzee komen bij-eten tijdens hun reis van

Alaska of Siberië naar Afrika. Ook deze dieren leven onder extreme omstandigheden door de grote

hoeveelheid zeewater die ze met hun voedsel binnenkrijgen. Het is zeer interessant om te bestuderen

hoe deze vogeltjes zich aan deze situatie hebben aangepast. Volgens planning zal de nieuwe

meetmethode met ingang van het volgende broedseizoen worden gebruikt voor de (commerciële)

routinebepalingen aan dubbel gelabeled water die door het CIO worden verricht. De traditionele

methode kan dan worden afgeschaft.

De tweede en heel andere toepassing die wordt beschreven ligt in het onderzoek naar het

klimaat van het verleden. Water dat als ijs op de poolkappen ligt opgeslagen kan heel oud zijn, tot ruim

vierhonderdduizend jaar. Uit de isotopensamenstelling van het oude ijs kan informatie worden verkregen

over de temperatuur in het gebied op het moment dat de neerslag viel. Het principe berust op het

verschijnsel dat water met daarin zwaardere isotopen iets moeilijker verdampt en iets makkelijker

condenseert dan “normaal” water. Dit verschijnsel is temperatuurafhankelijk en de isotopensamenstelling

van regen of sneeuw verandert dus met het klimaat. Door het combineren van de meetgegevens van

verschillende isotopen kan zelfs informatie worden afgeleid over de temperatuur in het brongebied van

de neerslag en de vochtigheid aldaar. Om bij het oude ijs te komen worden kernen geboord tot drie

kilometer diep. Wij hebben voor een demonstratie-experiment een ijskern gebruikt die twintig jaar

geleden op Groenland is geboord. Het door ons gemeten ijs is afkomstig van ongeveer 1800 m diepte en

uit de periode in de overgang tussen de laatste ijstijd en het huidige warme tijdperk, zo’n 10000 jaar

geleden. Met onze metingen hebben wij eerdere metingen bevestigd waaruit bleek dat de opwarming

van waarschijnlijk 7°C toentertijd zeer snel is gegaan, namelijk binnen een periode van decennia. Op dit

moment zijn de eerste metingen aan ijskernen afkomstig van Antarctica al begonnen. Met de nieuwe

techniek zal van ongeveer vijfduizend monsters de deuteriumconcentratie worden gemeten.

Er zijn nog veel meer toepassingen denkbaar van isotopenmetingen in water. Ook vandaag de

dag worden al zeer veel metingen aan water verricht, ondanks alle nadelen van de bestaande methoden.

De snelheid van de nieuwe methode is nu nog maar weinig hoger dan die van de traditionele

massaspectrometermethode, maar er zijn nog veel verbeteringen aan te brengen. Zo kan het inbrengen

van het water worden geautomatiseerd zodat de metingen vierentwintig uur per dag door kunnen gaan.

Samenvatting

178

De komst van een nieuw type lichtbron, een diode laser, maakt het apparaat nog veel eenvoudiger,

stabieler en sneller, en zal ook de precisie verder verbeteren. Wij zijn daarmee nu al aan het

experimenteren.

Concluderend kan worden gezegd dat de ontwikkelde methode een uitstekend alternatief biedt

voor de bestaande technieken. De nadelen van die technieken, voornamelijk het feit dat ze zeer

arbeidsintensief zijn en in sommige gevallen het gebruik van gevaarlijke stoffen vergen, zijn in onze

methode afwezig. De nauwkeurigheid waarmee de zware waterstofisotopen kunnen worden gemeten is

nu al groter dan bij de massaspectrometer, vooral bij de biologische toepassingen. Voor zuurstof is de

traditionele methode nu nog nauwkeuriger in de ijsmetingen, maar bij de verrijkte metingen voor

biologische doeleinden is de nauwkeurigheid al gelijk. Er zijn op korte termijn verdere verbeteringen te

verwachten. Daarom zal de techniek zich een eigen plaats verwerven binnen de isotopologie.

Dankwoord

179

Beste lezer,

Geweldig dat je zover gekomen bent met lezen. Hoewel de laatste loodjes altijd het zwaarst

wegen, schrijf ik dit hoofdstuk met veel plezier. Het is immers alleen maar leuk om de mensen te mogen

bedanken en op te hemelen die, ieder op hun eigen manier, hebben bijgedragen aan dit resultaat.

Mijn dankwoord kan ik niet anders beginnen dan bij Erik. Erik, jij was als mijn dagelijks

begeleider het meest direct betrokken bij het werk beschreven in dit boekje. Sterker nog, ik kwam

meedoen met jouw project. Overal waar “we” staat in dit proefschrift, gaat het ook over jou. Onze

samenwerking verliep altijd erg soepel en is alleen maar beter geworden dankzij onze vriendschap. Al

helemaal in het begin hadden we steun aan elkaar in het verre, eenzame Groningen en dat is alleen nog

maar gegroeid nadat de vriendinnen zich bij ons gevoegd hadden.

Ook Harro is een belangrijk onderdeel van “we”. Tijdens de vele experimenten liep hij dagelijks

het lab binnen voor de laatste resultaten en een praatje. Dankzij het beroemde Harro-effect waren de

eerste resultaten altijd grandioos. Harro, voor jou was het begeleiden van zo’n promovendus net zo

nieuw als promoveren voor mij, maar je hebt het er uitstekend vanaf gebracht.

Henk V., onze huisbioloog, was voornamelijk bij mijn werk betrokken in de tweede helft van het

onderzoek. Onze samenwerking verliep wat mij betreft naar volle tevredenheid. Ik heb je betrokkenheid

bij werk en mensen altijd enorm gewaardeerd en daar veel van geleerd. Voor mij ben jij echt het

prototype van een enthousiaste wetenschapper. Vergeet je niet af en toe ook aan jezelf te denken?

De taak van de leescommissie waardeer ik bijzonder. Professor Sigfus Jonhsen, thanks for

reading and commenting on the manuscript. Collaboration with you has been a great pleasure for me!

Professor Serge Daan en Professor Reinhard Morgenstern waren de andere leden van de commissie en

ook hen wil ik daarvoor graag bedanken.

Concluderend mag ik wel zeggen dat iedere promovendus mag hopen op de begeleiding en

begeleiders zoals ik die heb gehad.

Jaap heeft mij wegwijs gemaakt in het lab en in Groningen. Sinds hij de eerste dag wat

weerstandjes uit het magazijn ging ophalen was mijn interesse in Gronings en Groningers gewekt.

Hoewel ik langzamerhand een reputatie schijn te hebben opgebouwd niets te moeten hebben van dat

platte land en dat rare taaltje, heb ik het altijd best kunnen waarderen. Jaap, na jouw plotselinge

afscheid van het CIO heb ik je erg gemist in het lab, op het professionele vlak, maar zeker ook op het

sociale. Toen stond ik plotseling alleen in de deuropening: Erik, koffie!

Dan de rest van het CIO. Sociaal vormen jullie een prima groep om als eenzame, beginnende

OiO in terecht te komen. Iedereen heeft daar op zijn of haar manier bijgedragen. Maar ook voor wat

betreft de werkzaamheden verdienen jullie een bedankje. HJ voorop, die al die vakanties, weekeinden en

Dankwoord

180

feestdagen goed voor onze laser heeft gezorgd. Berthe, Trea, Janette en Bert die de

monstervoorbehandelingen voor hun rekening namen en Henk J. die de zaak dan weer moest meten. En

de technische ondersteuning van Erik Ku., Jan en Henk B. en de medewerkers van de werkplaatsen,

voornamelijk Koos en Ben, waren zeer waardevol. Maar ook het andere CIO personeel was er altijd voor

een grap of andere nuttige bijdrage: Anita, Henny, Fsaha, Luc en Luc, Martijn, Wim, Rolf, Marie-Hélène,

Hans en Hans, Renate, Stef, Dicky en Charlotte, bedankt. En ook de krypton laser wordt vriendelijk

bedankt voor het blijven functioneren tot mijn experimenten waren afgerond.

Dan natuurlijk nog een stukje over het leven naast het werk, waardoor het wonen in Groningen

nog aangenamer werd. Jaap en Wieke, dankzij jullie voelden Saskia en ik ons al heel snel thuis in

Groningen. We zijn niet voor niets zo dichtbij komen wonen en willen nu niet eens meer weg! Flo en

Erik, zijn eigenlijk de andere “buren”. Wat hebben wij vaak samen gekookt en genoeglijke dagen en

avonden doorgebracht. Samen met Jaap en Wieke waren jullie de eerste en meest robuuste sociale

peilers in deze verre stad.

Ook de mensen van het GAIOO zijn goed voor vele contacten. Het was een goede beslissing om

bij die club te gaan en zo ook promovendi met een hele andere achtergrond tegen het lijf te lopen. Geen

dag was hetzelfde dankzij al onze e-mails.

Dit stuk zou echt onleesbaar worden als ik al die mensen die verder hebben bijgedragen aan

werken en welzijn in Groningen apart zou noemen. Daarom bij deze voor al die mensen die nog niet

vermeld zijn: bedankt. Dankzij jullie heb ik het erg naar mijn zin in Groningen.

Broertje en zusje, het aantal momenten dat we elkaar “live” zagen is flink teruggelopen nadat ik

naar Groningen was vertrokken. Maar dankzij de e-mail en telefoon bleven we prima op de hoogte van

elkaars reilen en zeilen, zowel van de goede als de minder goede dingen. Dat contact is voor mij altijd

erg waardevol en daar laten wij nooit iets tussen komen.

Henk en Ria, dankzij jullie stimulering en steun heb ik de kansen kunnen grijpen die ik kreeg.

Toen ik naar Groningen ging was dat even schrikken voor jullie en voor mij, maar ondanks de afstand

zijn we altijd dichtbij elkaar gebleven. Jullie constante betrokkenheid heeft enorm bijgedragen aan het

succesvol afronden van deze periode.

Oma, wat jammer dat jij er niet meer bij kan zijn op je eigen verjaardag. Iedereen had nog wel

zo op je gerekend. Ik ben er erg trots op dat ik jouw kleinzoon ben.

Lieve Saskia, dankzij jouw offers, aandacht en liefde werd het veel leuker in Groningen. Ik ben

ontzettend blij dat jij hier nu ook zo goed aardt.

Radboud van Trigt, oktober 2001

List of publications

181

Publications

Kerstel, E.R.Th., Van Trigt, R., Dam, N., Reuss, J., Meijer, H.A.J., Simultaneous Determination of the2H/1H, 17O/16O, and 18O/16O Isotope Abundance Ratios in Water by Means of Laser Spectrometry, 1999,Anal. Chem., 71, 5297

Kerstel, E.R.Th., Van Trigt, R., Meijer, H.A.J., Laser Spectrometry Applied to the SimultaneousMeasurement of the δ2H, δ17O and δ18O Isotope Abundances in Water, 1999, TecDoc IAEA AdvisoryGroup Meeting on GC/IRMS and Laser Spectroscopy, Vienna 1999

Van Trigt, R., Kerstel, E.R.Th., Visser, G.H., Meijer, H.A.J., Stable Isotope Ratio Measurements on HighlyEnriched Water Samples by Means of Laser Spectrometry, 2001, Anal. Chem., 73, 2445

Van Trigt, R. Meijer, H.A.J., Sveinbjornsdottir, A.E., Johnsen, S.J., , Kerstel, E.R.Th., Measuring StableIsotopes of Hydrogen and Oxygen in Ice: The Bølling Transition in the Dye–3 Ice Core, 2001, Ann.Glaciol., in press

Kerstel, E.R.Th., Van Trigt, R., Meijer, H.A.J., Visser, G.H., Johnsen, S.J., Applications of the InfraredSpectrometric, Simultaneous Measurement of the 2H/1H, 17O/16O, and 18O/16O Isotope Ratios in Water,2001, Proc. 1st Int. Symp. On Isotopomers, Yokohama, Japan, July 23-26

Van Trigt, R., Kerstel, E.R.Th., Neubert, R.E.M., Meijer, H.A.J., McLean, M., Visser, G.H., Validation of theDoubly Labeled Water Method in Japanese Quail at Different Water Fluxes, 2001, sumitted to J. Appl.Physiol.

Kerstel, E.R.Th., Gagliardi, G., Gianfrani, L., Meijer, H.A.J., Van Trigt, R., Ramaker, R., Determination ofthe 2H/1H, 17O/16O, and 18O/16O Isotope Ratios in Water by Means of Tunable Diode Laser Spectroscopyat 1.39 µm, 2001, submitted to Spectrochim. Acta

Curriculum vitae

183

Curriculum vitae

Radboud van Trigt werd op 1 juni 1972 geboren in Delft. Na het afronden van het Gymnasium Felisenum

te Velsen-Zuid in 1990 ging ik scheikunde studeren aan de Vrije Universiteit in Amsterdam. Als bijvak

heb ik daar organometaal chemie gedaan in de groep van professor Frits Bickelhaupt. Als hoofdvak koos

ik analytische chemie in de groep van professor Nel Velthorst en onder leiding van Arjan Mank. Daar heb

ik onderzoek gedaan aan de on-line labelling van vetperoxiden met leuco-methyleenblauw. Gedurende

dit onderzoek heb ik mijn eerste ervaring opgedaan met spectroscopie. Mijn scriptie over “single

molecule detection” is binnen dezelfde groep geschreven onder leiding van professor Cees Gooijer. Het

laatste jaar van mijn studie stond grotendeels in het teken van mijn lidmaatschap van de

universiteitsraad. In augustus 1996 behaalde ik de bul. Na een uitstapje van een paar maanden naar de

lerarenopleiding scheikunde accepteerde ik het aanbod om als oio in Groningen te beginnen. In dienst

van de stichting Fundamenteel Onderzoek der Materie (FOM) werkte ik aan het Centrum voor

IsotopenOnderzoek (CIO) van de Rijksuniversiteit Groningen (RuG) aan een nieuwe methode voor het

meten van isotopen ratios van de stabiele isotopen in water. Het onderzoek werd uitgevoerd in de groep

van professor Harro Meijer en stond onder de dagelijkse leiding van Erik Kerstel. Dit proefschrift is het

resultaat van dat onderzoek. Sinds november 2001 werk ik bij Pharma Bio-research in Assen als study

director.