Jeroen Ongenae

SIFT-MS and LowoxMSTrace impurity analysis in hydrocarbon matrices with

Academiejaar 2011-2012Faculteit Ingenieurswetenschappen en ArchitectuurVoorzitter: prof. dr. ir. Guy MarinVakgroep Chemische Proceskunde en Technische Chemie

Master in de ingenieurswetenschappen: chemische technologieMasterproef ingediend tot het behalen van de academische graad van

Begeleider: prof. dr. ir. Kevin Van GeemPromotor: prof. dr. ir. Kevin Van Geem

Deze pagina is niet beschikbaar omdat ze persoonsgegevens bevat.Universiteitsbibliotheek Gent, 2021.

This page is not available because it contains personal information.Ghent University, Library, 2021.

Preface

This work has come to a good end thanks to a number of people. Via this way I would like to express

my gratitude explicitly towards those people.

First of all I’d like to thank prof. dr. ir. Guy Marin for giving me the opportunity to perform the

research described in this work at the department of Chemical Engineering and Technical Chemistry.

Also, I would like to express my gratitude towards my promotor and coach prof. dr. ir. Kevin Van

Geem for his guidance and the hours of work that he put into this work helping me and correcting it.

Without his guidance this work would never have come to a successful ending.

I would like to express my gratefulness towards Interscience Belgium, with whom was collaborated

for this master thesis, for providing all the equipment necessary for this research. Especially dr. Joeri

Vercammen for his theoretical and practical guidance. Without him, this work would never have

succeeded. I would also like to thank the technical personal at Interscience Belgium, who helped me

whenever I had a question or a technical issue, in particular Jean-Louis, Matthias and Gunther.

I also want to thank the technical personnel working at the laboratory who were always ready and

willing to give me a hand when I needed technical assistance. In particular Michaël Lottin, who put in

significant amounts of time in assisting and helping me. Thanks also to the PhD students at the

department of chemical engineering and technical chemistry for their guidance when necessary, ir.

Thomas Dijkmans and ir. Marko Djokic in particular.

A word of thanks goes to my fellow master students for the great atmosphere at the office and

during the coffee breaks.

Last but certainly not least I would like to express my gratitude towards my parents and my brother

for their support and the opportunity for to go to the university and complete my studies. A special

thanks to my girlfriend for her support and patience during this whole period.

To all of you, thank you!

Trace Impurity analysis in hydrocarbon matrices with SIFT-MS and LowoxMS

Jeroen Ongenae

Promotor: prof. dr. ir. Kevin Van Geem Begeleider: prof. Dr. ir. Kevin Van Geem

Masterproef ingediend tot het behalen van de academische graad van Master in de ingenieurswetenschappen: chemische technologie.

Voorzitter: prof. dr. ir. Guy Marin Vakgroep Chemische Proceskunde en Technische Chemie Faculteit Ingenieurswetenschappen en Architectuur Academiejaar 2011-2012

Abstract

This master’s dissertation reports the investigation of whether SIFT-MS is capable of detecting trace

impurities, such as arsine, phosphine and certain oxygenates, in hydrocarbon matrices. The obtained

results are compared to current state-of-the-art analytical methods, e.g. LowoxMS. The latter is

capable of separating a complex mixture of low-boiling oxygenates diluted in a hydrocarbon matrix,

i.e. n-hexane. This is described in Chapter 3.

SIFT-MS doesn’t provide an alternative in the detection of arsine and phosphine in a hydrocarbon

matrix. SIFT-MS is only capable of detecting arsine in polymer-grade propylene, while a GC-MS set-up

equipped with a PoraBond QTM analytical column is capable of detecting both.

SIFT-MS is also capable of detecting acetone and formaldehyde in a very low concentration range, i.e.

low ppb level, in complex hydrocarbon matrices. Therefore, SIFT-MS could provide a quick

alternative for the current state-of-the-art detection methods. However, further investigation on this

topic is necessary.

Keywords: LowoxMS, SIFT-MS, PoraBond QTM, oxygenates, hydrocarbon matrix

Trace impurity analysis in hydrocarbon matrices

with SIFT-MS and LowoxMS

Jeroen Ongenae, Supervisor(s): Kevin M. Van Geem

Abstract During this master thesis an investigation is carried

out on whether the SIFT-MS can be used as an alternative for

the current state-of-the-art techniques for the detection and

quantification of trace impurities in hydrocarbon matrices.

Therefore, a comparison between the existing gas

chromatographic applications and SIFT-MS is performed. The

obtained results show that the SIFT-MS has a lot of potential

applications. An example could be to use it as an early indication

of problems arising in the production process, thus losing a

minimal amount of time and money. The SIFT-MS is also

capable of detecting and quantifying trace level concentrations,

low ppb level, of formaldehyde in a complex hydrocarbon

mixture.

Keywords LowoxMS, SIFT-MS, PoraBond QTM, oxygenates,

hydrocarbon matrix

I. INTRODUCTION

It is well-known that some trace impurities can affect the

activity of catalysts. This may or may not be irreversible.

Therefore, the industry sets very high requirements on the

present concentration of these impurities in hydrocarbons.

Some examples are given in Table 1.

Table 1. Typical specifications for some impurities [1].

Component Typical specification

arsine Less than 20 ppb

fosfine Less than 20 ppb

ammonia Less than 100 ppb

hydrogen sulfide Less than 20 ppb

carbonyl sulfide Less than 20 ppb

nitrogen dioxide Less than 50 ppb

HCN Less than 100 ppb

HCl, HF Less than 200 ppb

phosgene Less than 50 ppb

sulfur dioxide Less than 50 ppb

chlorine Less than 30 ppb

methyl mercaptan Less than 20 ppb

The industy demands analytical methods, which can

measure the impurities very fast in these very low

concentration levels.

In this work, an investigation on the fact whether the

Selected Ion Flow Tube – Mass Spectrometer, SIFT-MS,

could offer an alternative is investigated. The SIFT-MS has

already proven in previous research that it can analyze

complex samples very fast. An example is the analysis of

volatile organic compounds, VOCs, in olive oil [2].

II. ULTRATRACE QUANTITATIVE ANALYSIS OF OXYGENATES

USING LOWOXMS

Firsts the current state-of-the-art technology in the analysis

of oxygenates in hydrocarbon matrices, the LowoxMS, is

used. This is a GC-MS based technology, which uses a

multicolumn set-up. The analytical column applied in this

instrument is the CP-Lowox, which was developed by

Chrompack/Varian (now: Agilent) especially for this

application.

A mixture of 21 low-boiling oxygenates, e.g. acetaldehyde,

ethanol and iso-propanol, diluted in n-hexane, is injected on to

the LowoxMS. By applying cryogenics, the GC-MS is

capable of separating most of the components very well,

despite their small difference in boiling point. An example of

a chromatogram, obtained during these analyses is given in

Figure 1.

Figure 1. Example of a chromatogram obtained during the analysis of

a mixture of oxygenates in n-hexane (identification: see text).

The different operating modes of the MS are also evaluated,

i.e. Full Scan (FS), Extracted Ion Chromatography (EIC) and

Single Ion Monitoring (SIM). The preferred operating mode

turns out to be EIC, because this method retains the possibility

to identify the components and, at the same time, it shows a

significant gain in sensitivity.

III. LOWOXMS ANALYSIS FOR ARSINE AND PHOSPHINE

IMPURITIES IN POLYMER-GRADE PROPYLENE

The LowoxMS has also been tested to verify whether it can

detect arsine and phosphine. The analysis of the dilutions,

made in Tedlar® bags, show that the LowoxMS is only

capable of detecting and quantifying arsine. This is probably

caused by the fact that phosphine doesn’t have a retention on

the CP-Lowox column. Moreover, phosphine has a very low

molecular weight, 34 g/mole, which makes it harder to

distinguish from other impurities such as oxygen and CO2.

Arsine isn’t a problem for the LowoxMS, given that the lower

limit of the mass range, in which the MS scans, can be

elevated, thus excluding the impurities from being detected

and thus interfering the signal. The limit of detection is

estimated at 845 ppt using a rule of thumb.

Since the LowoxMS isn’t capable of detecting phosphine,

an alternative is searched. This is found in the form of another

analytical column, the PoraBond QTM

, which is the ideal

solution for these analyses according to Gras et al. [3]. A GC-

MS set-up equipped with this column is tested. For these

analyses, an isothermal FS/SIM method is applied. The

findings of Gras et al. are confirmed [3]. The detection limit is

again estimated using the rule of thumb. The detection limit

for arsine and phosphine is estimated at 1,364 ppb and 1,538

respectively. Figure 2 shows a chromatogram obtained during

the injection of arsine and phosphine in propylene.

Figure 2. Chromatogram obtained during the injection of phosphine

(middle) and arsine (lower) in propylene.

A clear separation between the target components and the

matrix, propylene (RT: 8,16 min), can be seen on this figure.

However, subsequent injections showed a significant

underestimation of the present concentration. This is most

likely due to the fact that propylene remains on the column for

too long. A temperature program could offer a solution.

IV. SIFT-MS ANALYSIS OF TRACE IMPURITIES IN

HYDROCARBON MATRICES

Results of experiments conducted with arsine and

phosphine diluted in nitrogen, display the ability of the SIFT-

MS to detect and quantify arsine and phosphine. Based on

some blank nitrogen injections, the detection limits are

estimated at 5,61 ppb for phosphine and 12,58 ppb for arsine.

This is slightly higher to those obtained with the LowoxMS,

but still well below the required specifications (Table 1).

When injecting dilutions in propylene, it quickly becomes

clear that the SIFT-MS has troubles coping with this matrix.

This is probably caused by the fact that propylene has a

double bond, which makes it easy to ionize en thus a lot of the

reagent ions used by the SIFT-MS are consumed by the

matrix. The SIFT-MS can only quantify arsine, which

indicates that the SIFT-MS is capable of detecting this

component in propylene in these very low concentrations (ppb

level). Phosphine is no longer detectable by the SIFT-MS.

The second part of the experiments with SIFT-MS involves

an investigation into the capabilities of the instrument of

detecting and quantifying acetone and formaldehyde in some

hydrocarbon matrices (e.g. methane, ethane, ethylene and

propylene). This is done by constructing calibration curves for

these components in each of the hydrocarbon matrices and

check their linearity.

Calibration curves for acetone are composed diluted in

nitrogen, methane, ethane, ethylene and propylene. The results

are given in Figure 3.

Figure 3. Matrix influence on the signal of acetone detected with

SIFT-MS.

High correlation coefficients are obtained by performing

linear regression on the data, indicating the fact that SIFT-MS

is very well capable of detecting acetone in each of these

matrices at the required concentration level. Also, injections

of acetone diluted in a complex matrix, normally used to

calibrate gas chromatographs, are done in order to simulate

some sort of steam cracker effluent. This also delivers a

lineair calibration curve. Then it is tried to recreate these

results by averaging the results obtained in pure hydrocarbon

matrices, based on the composition of the calibration mixture.

The results are promising, but further investigation is required

in order to confirm these.

Finally, formaldehyde, diluted in several hydrocarbon

matrices, is also injected in very low concentrations. Mainly

because of the fact the current chemical analytical methods

have a lot of difficulties doing this. Therefore, formaldehyde

is diluted in nitrogen, methane, propylene and the calibration

mixture and injected on to the SIFT-MS. The results show

good promise of the application of SIFT-MS for these

analyses. Even in the calibration mixture, a linear calibration

curve is obtained down to a concentration of 20 ppb.

V. CONCLUSIONS

The results obtained in this work show that The SIFT-MS

has a lot of potential and possible applications. For instance,

all the obtained detection limits are in the low parts-per-billion

level as desired by the petrochemical industry. A possible

application for the SIFT-MS could be to use it as an

instrument which gives an early warning of problems arising

in the production process. However, all the conclusions

formulated in this work need to backed up with further

experimental results. As for providing an alternative for the

current state-of-the-art detection methods, the SIFT-MS has

some potential for certain specific applications, for example

the analysis of formaldehyde in hydrocarbon matrices.

VI. REFERENCES

1. Thind, S.S., Analysis of Impurities in Polymer-grade Ethylene,

Propylene and 1,3-butadiene. Petro Industry News, 2003(June/July

2003): p. 1-2.

2. Davis, B.M., Volatile Organic Compounds and Antioxidants in Olive

Oil: Their analysis by Selected Ion Flow Tube Mass Spectrometry.

2007, University of Canterbury: Christchurch. p. 294. 3. Gras, R., et al., Analysis of part-per-billion level of arsine and

phosphine in light hydrocarbons by capillary flow technology and

dielectric barrier discharge detector. Journal of Chromatography A, 2010. 1217(3): p. 348-352.

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0 20 40 60 80

Sig

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Matrix influence on acetone signal (77)

nitrogen

methane

ethane

ethylene

propylene

Trace impurity analysis in hydrocarbon matrices

with SIFT-MS and LowoxMS

Jeroen Ongenae, Supervisor(s): Kevin M. Van Geem

Abstract Het onderwerp van deze thesis is het onderzoek naar

het feit of de SIFT-MS een alternatief kan vormen voor de

huidige chemische analytische technieken voor de kwantificatie

van spooronzuiverheden in koolwaterstoffen. Daarom wordt een

vergelijkende studie tussen gas chromatografische technieken en

de SIFT-MS uitgevoerd. Uit de resultaten blijkt dat de SIFT-MS

eventueel gebruikt kan worden om problemen die zich voordoen

in het productieproces snel op te lossen met een minimaal verlies

van tijd en geld. Ook blijkt de SIFT-MS goed in staat om

spoorconcentraties, laag parts-per-billion niveau, formaldehyde

en aceton te detecteren en te kwanitificeren in een complex

koolwaterstof mengsel.

Keywords LowoxMS, SIFT-MS, PoraBond QTM, oxygenates,

koolwaterstofmatrix

I. INLEIDING

Spooronzuiverheden in koolwaterstoffen kunnen de

activiteit van katalysatoren beïnvloeden. Dit is al dan niet

irreversibel. Daarom stelt de industrie zeer hoge eisen aan de

aanwezige concentratie van deze componenten. Een aantal

voorbeelden zijn gegeven in Tabel 1.

Tabel 1. Typische specificaties voor een aantal onzuiverheden [1].

Component Typische specificatie

arsine Minder dan 20 ppb

fosfine Minder dan 20 ppb

ammoniak Minder dan 100 ppb

waterstof sulfide Minder dan 20 ppb

carbonyl sulfide Minder dan 20 ppb

stiktstof dioxide Minder dan 50 ppb

HCN Minder dan 100 ppb

HCl, HF Minder dan 200 ppb

fosgene Minder dan 50 ppb

zwaveldioxide Minder dan 50 ppb

chloor Minder dan 30 ppb

methyl mercaptaan Minder dan 20 ppb

De industrie vraagt dan ook naar analysemethoden welke

deze componenten zeer snel in deze zeer lage concentraties

kunnen meten.

Een onderzoek naar het feit of de Selected Ion flow Tube –

Mass Spectrometer, SIFT-MS, een alternatief kan bieden

wordt in dit werk uitgevoerd. De SIFT-MS is immers een

toestel dat reeds bewezen heeft heel snel complexe stalen te

kunnen analyseren. Een voorbeeld hiervan is de analyse van

vluchtige organische componenten in olijfolie [2].

II. KWANTITATIEVE ANALYSE VAN

ULTRASPOORONZUIVERHEDEN MET BEHULP VAN LOWOXMS

Eerst wordt de huidige state-of-the-art technologie in de

detectie en kwantificatie van oxygenates in koolwaterstoffen,

de LowoxMS gebruikt. Dit is een GC-MS gebaseerde

technologie, waarbij gebruik gemaakt wordt van een multi-

kolom technologie. De analytische kolom is de CP-Lowox,

welke speciaal voor deze toepassing ontwikkeld werd door

Chrompack/Varian (Nu: Agilent).

Een mengsel van 21 zuurstof houdende componenten met

een laag kookpunt, bv. acetaldehyde, ethanol en propanol,

opgelost in n-hexaan wordt geïnjecteerd op de LowoxMS.

Doordat er gebruik gemaakt wordt van een ovenprogramma

waarbij cryogene technologie gebruikt wordt, is het toestel in

staat om zo goed als alle componenten goed te scheiden,

ondanks hun klein verschil in kookpunt. Een voorbeeld van

een chromatogram bekomen tijdens deze analyses wordt

gegeven in Figuur 1.

Figuur 1. Voorbeeld van een chromatogram bekomen bij de analyses

van een mengsel oxygenates in n-hexaan (identificatie: zie tekst).

Tevens worden de verschillende gebruiksmodes van de MS

geëvalueerd, nl. Full Scan (FS), Extracted Ion

Chromatography (EIC) en Single Ion Monitoring (SIM). De

voorkeur gaat uit naar EIC, omdat deze de

identificatiemogelijkheden van FS behoudt, maar toch een

significante winst in detectiegevoeligheid vertoont.

III. ANALYSE VAN ARSINE EN FOSFINE IN PROPEEN MET

BEHULP VAN LOWOXMS

De LowoxMS wordt eerst getest op het feit of deze arsine

en fosfine zou kunnen detecteren. De analyses op de

verdunningen, gemaakt met behulp van Tedlar® bags, tonen

dat de LowoxMS enkel in staat is om arsine te detecteren en te

kwantificeren. Dit wordt waarschijnlijk veroorzaakt doordat

fosfine geen retentie vertoont op de CP-Lowox kolom.

Daarenboven heeft fosfine een zeer laag moleculair gewicht,

34 g/mol, wat het moeilijker te onderscheiden maakt van

andere onzuiverheden zoals bv. zuurstof en CO2. Arsine is

geen probleem voor de LowoxMS, aangezien de onderlimiet

van het massabereik waarin de MS scant kan worden

verhoogd zodat deze onzuiverheden niet meer gedetecteerd

kunnen worden. De detectielimiet van arsine wordt, met

behulp van een vuistregel, geschat op 845 ppt.

Aangezien de LowoxMS niet in staat is fosfine te detecteren

wordt gezocht naar een alternatief. Dit alternatief wordt

gevonden in de vorm van een andere analytische kolom, de

PoraBond QTM

, welke volgens Gras et al. de ideale oplossing

is voor dit probleem [3]. Een GC-MS uitgerust met deze

analytische kolom wordt getest. Hierbij wordt een isotherme,

FS/SIM methode toegepast. De bevindingen van Gras et al.

worden bevestigd [3]. Opnieuw wordt de detectielimiet

geschat via dezelfde vuistregel. Voor arsine en fosfine wordt

respectievelijk een detectielimiet van 1,364 ppb en 1,538 ppb

gevonden. Figuur 2 geeft een chromatogram weer bekomen

tijdens injectie van arsine en fosfine in propeen.

Figuur 2. Chromatogram bekomen met injectie van fosfine (midden)

en arsine (onder) in propeen.

Hier is een duidelijke scheiding van de doelcomponenten

van de matrix, propeen (RT: 8,16 min.), zichtbaar. Bij latere

injecties werd echter een significante onderschatting van de

concentratie waargenomen. Dit is waarschijnlijk omdat

propeen te lang op de kolom blijft hangen. Een

temperatuursprogramma zou hier soelaas kunnen bieden.

IV. ANALYSE VAN SPOORONZUIVERHEDEN IN

KOOLWATERSTOFFEN MET BEHULP VAN SIFT-MS

Uit de experimenten uitgevoerd op verdunningen van arsine

en fosfine met behulp van SIFT-MS blijkt dat de SIFT-MS in

staat is zowel arsine als fosfine te detecteren, weliswaar in een

stikstof matrix. Op basis van een aantal blanco injecties wordt

de detectielimiet geschat op 5,61 ppb voor fosfine en 12,58

ppb voor arsine. Dit is hoger dan wat bekomen werd voor de

LowoxMS, maar het is nog steeds onder de gestelde

specificaties (Tabel 1).

Wanneer verdunningen met propeen worden geïnjecteerd

blijkt snel dat de SIFT-MS moeilijkheden heeft met deze

matrix. Dit wordt waarschijnlijk veroorzaakt door het feit dat

propeen een dubbele binding bezit, waardoor het gemakkelijk

geïoniseerd wordt en dus veel van de precursorionen, gebruikt

door de SIFT-MS, consumeert. Voor arsine kan in propeen

nog steeds een kalibratiecurve opgesteld worden, wat wijst op

het feit dat de SIFT-MS in staat is deze component te

detecteren in propeen bij deze zeer lage concentraties (ppb-

niveau). Fosfine is in propeen echter niet meer detecteerbaar.

Een tweede luik van de experimenten met de SIFT-MS

omvat een onderzoek naar de capaciteiten van de SIFT-MS

om aceton en formaldehyde te detecteren en kwantificeren in

enkele koolwaterstoffen (bv. methaan, ethaan, etheen en

propeen). Dit wordt nagegaan door kalibratiecurves op te

stellen voor elke component in elke matrix.

Voor aceton worden kalibratiecurves opgesteld in stikstof,

methaan, ethaan, etheen en propeen. De resultaten van deze

analyses wordt weergegeven in Figuur 3.

Figuur 3. Invloed van de matrix op het signaal van aceton

gedetecteerd met SIFT-MS.

De hoge correlatiecoëfficiënt bekomen met lineaire

regressie op de datapunten voor de kalibratiecurves in alle

matrices wijzen op het feit dat de SIFT-MS in staat is aceton

te detecteren in elke van deze matrices, op het gewenste

concentratieniveau. Er worden ook injecties gedaan van

aceton verdund in een complex mengsel, normaal gebruikt om

GC’s te calibreren, om een stoomkraker effluent te simuleren.

Ook hier wordt een mooi lineaire kalibratiecurve verkregen.

Verder wordt er ook getracht de resultaten van dit laatste

experiment na te bootsen door de resultaten, bekomen in de

zuivere koolwaterstoffen, uit te middelen op basis van de

samenstelling van het kalibratiemengsel. De bekomen

resultaten zijn veelbelovend, maar verder onderzoek is nodig

om deze te bevestigen.

Tenslotte wordt ook formaldehyde, verdund in

koolwaterstofmatrices, geanalyseerd in zeer lage

concentraties.. Dit voornamelijk omdat de huidige methoden

veel moeite hebben met deze toepassing. Formaldehyde wordt

verdund in stikstof, methaan, propeen en het

kalibratiemengsel geïnjecteerd op de SIFT-MS. De bekomen

resultaten zijn veelbelovend. Zelfs in het complexe

kalibratiemengsel is de kalibratiecurve lineair tot een

concentratie van 20 ppb.

V. CONCLUSIES

De resultaten bekomen met de SIFT-MS zijn veelbelovend.

Bijvoorbeeld alle detectielimieten liggen in het lage ppb

niveau, zoals gewenst door de petrochemische industrie. Een

mogelijke toepassing van de SIFT-MS zou kunnen zijn deze

te gebruiken als een instrument dat snel een waarschuwing

kan geven van mogelijke problemen. De besluiten

geformuleerd in dit werk dienen wel nog ondersteund te

worden met verdere experimentele resultaten. De SIFT-MS

heeft potentieel om een alternatief te vormen voor bepaalde

van de huidige state-of-the-art detectiemethoden, bv. bij de

analyse van formaldehyde in koolwaterstoffen.

VI. REFERENTIES

1. Thind, S.S., Analysis of Impurities in Polymer-grade Ethylene, Propylene and 1,3-butadiene. Petro Industry News, 2003(June/July

2003): p. 1-2.

2. Davis, B.M., Volatile Organic Compounds and Antioxidants in Olive Oil: Their analysis by Selected Ion Flow Tube Mass Spectrometry.

2007, University of Canterbury: Christchurch. p. 294.

3. Gras, R., et al., Analysis of part-per-billion level of arsine and phosphine in light hydrocarbons by capillary flow technology and

dielectric barrier discharge detector. Journal of Chromatography A,

2010. 1217(3): p. 348-352.

0,00

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800,00

1000,00

1200,00

0 20 40 60 80

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Matrix influence on acetone signal (77)

nitrogen

methane

ethane

ethylene

propylene

Table of Contents

Notation i

1. List of Abbreviations i

2. List of Symbols ii

Chapter 1: General Introduction 1

1. Catalyst deactivation 1

2. Outline 3

3. References 4

Chapter 2: Literature Survey – Analytical methods for the detection of trace impurities in

hydrocarbon matrices 5

1. Introduction 5

2. Dry colorimetry 7

2.1. Principle 7

2.2. Applications 8

3. Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) 9

3.1. Principle 10

3.2. Applications 12

4. Lowox analytical column 14

4.1. Properties 14

4.2. Typical set-up with the CP-Lowox analytical column 15

4.3. Applications 16

5. Dielectric Barrier Discharge Detector (DBD) 18

5.1. Principle 18

5.2. Application 20

6. Selected Ion Flow Tube – Mass Spectrometry (SIFT-MS) 21

6.1. Principle 21

6.2. Applications 23

7. Conclusion 24

8. References 25

Chapter 3: Ultratrace Quantitative analysis of oxygenates using LowoxMS 27

1. Introduction 27

2. Experimental 28

2.1. Standards 28

2.2. Gas Chromatography 28

2.3. Mass Spectrometry 29

3. Results and Discussion 30

3.1. System Set-up 30

3.2. System Suitability 32

4. Applications 35

4.1. Naphtha Feed 35

5. Conclusions 36

6. References 36

Chapter 4: LowoxMS analysis for arsine and phosphine impurities in polymer-grade propylene 37

1. Introduction 37

2. Experimental 37

2.1. Standards 37

2.2. LowoxMS 38

2.3. GC-MS - PoraBondQTM 38

2.3.1. GC-MS Settings 39

3. Results and discussion 39

3.1. LowoxMS 39

3.1.1. Phosphine 39

3.1.2. Arsine 40

3.2. GC-MS equipped with the PoraBondQTM column 42

3.2.1. Arsine and phosphine calibration curves in nitrogen 42

3.2.2. Arsine and phosphine diluted in propylene injections 44

4. Conclusions 46

5. Future Work 47

6. References 49

Chapter 5: SIFT-MS analysis of trace impurities in complex hydrocarbon matrices 50

1. Introduction 50

2. Experimental 50

2.1. Standards 50

2.2. Selected Ion Flow Tube – Mass Spectrometer 52

2.2.1. Implementing a new method on the Voice 200 53

3. Results 56

3.1. Phosphine and arsine detection and quantification 56

3.1.1. SIFT-MS method 56

3.1.2. Calibration curves in nitrogen 57

3.1.3. Repeatability of the dilution method 59

3.1.4. Calibration curve in polymer-grade propylene 59

3.2. Oxygenates in hydrocarbon matrices 61

3.2.1. Acetone 62

3.2.1.1. Repeatability of propylene calibration curves 62

3.2.1.2. Matrix influence 64

3.2.1.3. Matrix additivity check 66

3.2.2. Formaldehyde 68

3.2.2.1. Matrix influence 68

3.2.2.2. Calibration mixture results 69

4. Conclusions 71

5. Future Work 73

6. References 75

Chapter 6: Conclusions and future work 76

Annex A: Overview of performed experiments 79

i

Notation

1. List of Abbreviations

ASTM American Society for Testing and Materials

AAS Atomic Absorption Spectrometry

AED Atomic Emission Detector

amu atomic mass unit

BEC Background Equivalent Concentration

BTEX Benzene, Toluene, Ethyl benzenes and Xylenes

COS Carbonyl Sulfide

CI Chemical Ionization

CCT Collision Cell Technology

DBD Dielectric Barrier Discharge

DNPH Dinitrophenylhydrazine

ETBE Ethyl Tertiary Butyl Ether

EIC Extracted Ion Chromatography

FID Flame Ionization Detector

FCC Fluid Catalytic Cracking

FS Full Scan

FS/SIM Full Scan/Single Ion Monitoring

GC Gas Chromatography

GC-AED Gas Chromatography – Atomic Emission Detector

GC-MS Gas Chromatography – Mass Spectrometry

HPLC-UV High Performance Liquid Chromatography - Ultraviolet spectrometry

ICP-MS Inductively Coupled Plasma – Mass Spectrometer

ICP-OES Inductively Coupled Plasma – Optical Emission Spectrometry

LCT Laboratorium of Chemical Technology

LOD Limit of Detection

LOQ Limit of Quantification

MS Mass spectrometer

MAOT Maximum Allowed Operating Temperature

MTBE Methyl Tertiary Butyl Ether

µGC Micro Gas Chromatography

MM Molecular Mass

NIOSH National Institute for Occupational Safety and Health

NIST National Institute of Standards and Technology

ORS Octopole Reaction System

ppb parts-per-billion

ppm parts-per-million

ppt parts-per-trillion

%RSD Percentage Relative Standard Deviation

PEG PolyEthylene Glycol

PLOT Porous Layer Open Tubular

ii

SIFT-MS Selected Ion Flow Tube – Mass Spectrometer

SCR Selective Catalytic Reduction

S/N Signal-to-Noise ratio

SIM Single Ion Monitoring

S.D. Standard Deviation

TCEP tris-(2-carboxyethyl)-phosphine

VOC Volatile Organic Chemicals

2. List of Symbols

xbl Average signal of the blanks (counts/s)

c(M) Concentration of analyte M (molecules/cm³)

LoD Limit of Detection (counts/s) or (ppb)

Ip Number of counts per second of product ion detected (counts/s)

Ii Number of counts per second of reagent ion detected (counts/s)

P1 Permeation rate at operating temperature T1 (ng/min)

P0 Permeation rate at reference temperature T0 (ng/min)

k Rate coefficient for the reaction between analyte and reagent ion (cm³/molecule s)

sbl Standard deviation of the blanks (counts/s)

T Temperature (°C)

α Temperature coefficient (0,034 °C-1if P0 < 150 ng/min, else 0,03 °C-1)

tr Time available for the reaction between analyte and reagent ion (s)

1

Chapter 1: General Introduction

1. Catalyst deactivation

A good catalyst is characterized by specific parameters such as a high selectivity, durability and

activity. A high selectivity for the desired product is probably the most important characteristic of a

catalyst, because this means that less by-products are formed. The latter results in the fact that a

lower amount of feed has to be used in order to form the desired amount of product. The durability

of a catalyst refers to the fact that the catalyst should retain its activity and selectivity for a certain

amount of time. Ideally, these parameters should remain a constant and the catalyst has an eternal

live, which in practice is impossible to achieve. The loss of catalytic activity with the on-stream time is

one of the biggest concerns for the industry nowadays [1, 2].

Catalyst deactivation is a process both of chemical and physical nature that occurs simultaneously

with the main reaction. Though catalyst deactivation is inevitable, a lot of research is focused on

preventing deactivation as much as possible and/or to slow it down [1]. Depending on the type of

process, a time-scale of deactivation exists. An overview of the typical time-scale of deactivation for

some reactor types is given in Table 1-1 [3].

Table 1-1. Time-scale of deactivation of the applied catalysts for some reactor types.

Time-scale of deactivation Typical reactor type

Years Fixed bed reactor, usually no regeneration

Months Fixed bed reactor, regeneration while the reactor is off-line

Weeks Fixed bed reactors in swing mode, moving bed reactor

Minutes –days Fluidized bed reactor, slurry reactor; continuous regeneration

Seconds Entrained flow reactor (riser) with continuous regeneration.

2

Based on this table the lifetime of the catalysts for some industrial processes can be estimated. This

is given in Figure 1-1 [2]. Notice that this life time varies from seconds (e.g. Fluid Catalytic Cracking or

FCC) to one or several years (e.g. Hydrocracking or Selective Catalytic Reduction (SCR)) depending on

the process which is applied [2].

Figure 1-1. The time-scale of deactivation for some industrial processes[2].

Several mechanisms of deactivation can be identified, both chemical and physical in nature. These

are often divided into four main categories, i.e. poisoning, coking or fouling, sintering and mechanical

deactivation through damage [1-3].

Coking or fouling covers all phenomena where a surface is covered, for instance the deposition of

residues like ashes onto the catalytic surface. Sintering is a physical process where a loss of active

surface is observed due to structural modification of the catalyst caused by thermal degradation.

Mechanical deactivation can be caused by transporting the catalyst or expansion and contraction

during heating and cooling of the reactor. These processes can damage the catalyst which may lead

to catalytic deactivation [1, 2].

Poisoning of catalysts is defined as “the loss of activity due to strong chemisorption of impurities

present in the feed stream on the active sites of the catalyst” [1]. Several mechanisms of poisoning

are possible. The poison can simply act by blocking an active site or it can alter the adsorption of

other species by an electronic effect. It can also modify the chemical nature of the active sites of the

catalyst or result in the formation of new compounds which results in a definitively alteration of the

catalyst performance [1, 2].

It should be noticed that the poison can be adsorbed temporarily or permanently. In the first case

the poison isn’t adsorbed too strongly and catalyst regeneration can be carried out simply by

3

removing the poison, e.g. by heating up the catalyst. Poisons bonded permanently on the catalyst

aren’t removed that easy. Note that the distinction between both forms isn’t always that clear:

certain components that act as strong poisons at low temperatures are harmless in high-temperature

processes [1, 2].

For an impurity to be able to affect the activity of a catalyst significantly it suffices that this impurity

is present in a very low amount, i.e. low parts-per-billion level. Therefore the industry demands

technologies that can detect and quantify these kinds of concentration very fast, preferably online.

2. Outline

This thesis evaluates the ability of Selected Ion Flow Tube – Mass Spectrometry , SIFT-MS, for

detection of trace impurities, i.e. low parts-per-billion level, in hydrocarbon matrices.

Chapter 2 gives an overview of a literature survey performed on some of the latest technologies in

detecting and quantifying trace level impurities in light hydrocarbon matrices. These include dry

colorimetry, inductively coupled plasma – mass spectrometry (ICP-MS), the Lowox analytical column

designed especially for the analysis of oxygenated compounds in hydrocarbon matrices and the

dielectric barrier discharge (DBD) detector. Also a research on the current applications of the SIFT-MS

in the detection of trace level components in non-hydrocarbon matrices (e.g. air, breath, etc.) is

given in this chapter. For each of these techniques the principle and some results found in the

literature are discussed briefly.

Chapter 3 discusses the results of analyses performed with the current state-of-the-art for the

detection of trace level impurities, specifically light oxygenates, in hydrocarbon matrices, the

LowoxMS. This is a multicolumn chemical analytical technique which uses a mass spectrometer

instead of a flame ionization detector in order to obtain a higher sensitivity but also to be able to

identify unknown components. More specifically, analyses of certain common oxygenates dissolved

in n-hexane are discussed in this chapter.

Next, the detection and quantification of some other impurities, i.e. arsine and phosphine, with the

LowoxMS are discussed in Chapter 4. These are well-known impurities which can affect the catalytic

activity of some catalysts significantly. Therefore the detection of these components is important.

Dilutions of these components are made in Tedlar® sampling bags. The LowoxMS is also compared to

a gas chromatograph – mass spectrometry technique which uses the PoraBond QTM analytical

column. The latter was found in literature to be ideally suited for this application [4].

4

Chapter 5 five deals with the experiments performed with SIFT-MS. The first part is about

experiments performed on the detection and quantification of arsine and phosphine in polymer-

grade propylene. These results are then compared to those obtained in Chapter 4, in order to get an

idea on how both techniques compare. The second part of Chapter 5 discusses the detection of

certain oxygenates, i.e. acetone and formaldehyde, in various hydrocarbon matrices using SIFT-MS.

These matrices include nitrogen, methane, ethane, ethylene and propylene. The dilutions of the

oxygenates are made using the Micro-Processor Controlled Calibration System MK5, which uses

permeation tubes in which the oxygenates are contained. After executing experiments with pure

hydrocarbon matrices, analyses of the oxygenates in a relatively complex mixture are performed, in

order to simulate a steam cracker effluent.

At the end of Chapters 4 and 5, some suggestions for future work are also made. These suggestions

are based on the findings resulting from the experiments conducted in these experiments. These

include suggestions for a new GC-MS set-up and some possible solutions of how to remove the

matrix from the effluent stream of a steam cracker.

Finally, Chapter 6 gives an overview of the general conclusions and suggestions formulated during

this work.

3. References

1. Sivaramakrishnan, R., J.V. Michael, A.F. Wagner, R. Dawes, A.W. Jasper, L.B. Harding, Y. Georgievskii, and S.J. Klippenstein, Roaming radicals in the thermal decomposition of dimethyl ether: Experiment and theory. Combustion and Flame, 2011. 158(4): p. 618-632.

2. Zhao, Z., M. Chaos, A. Kazakov, and F.L. Dryer, Thermal decomposition reaction and a comprehensive kinetic model of dimethyl ether. International Journal of Chemical Kinetics, 2008. 40(1): p. 1-18.

3. Fischer, S.L., F.L. Dryer, and H.J. Curran, The reaction kinetics of dimethyl ether. I: High-temperature pyrolysis and oxidation in flow reactors. International Journal of Chemical Kinetics, 2000. 32(12): p. 713-740.

4. Gras, R., J. Luong, M. Hawryluk, and M. Monagle, Analysis of part-per-billion level of arsine and phosphine in light hydrocarbons by capillary flow technology and dielectric barrier discharge detector. Journal of Chromatography A, 2010. 1217(3): p. 348-352.

5

Chapter 2: Literature Survey

Analytical methods for the detection of trace impurities in

hydrocarbon matrices

1. Introduction

Several methods for analyzing impurities in hydrocarbon matrices such as gasoline and diesel, are

well established and in many cases they have even been standardized. These methods are either

based on gas chromatography (e.g.: ASTM-D-6730, D-5580, D-3738, D-4815), mass spectrometry

(e.g.: ASTMD-2789), infrared spectroscopy (e.g.: ASTM-D-5986), supercritical fluid chromatography

(e.g.: ASTM-5186) or fluorescent indicator adsorption techniques (e.g.: ASTM-D-1319) [1, 2]. A

detailed description of these ASTM methods can be found on the website of the American Society for

Testing and Materials [2].

However, these methods can only be used to detect high level impurities, i.e. 100 parts-per-million

(ppm) order or even higher concentrations. They are not fit to analyze trace level impurities in

hydrocarbon streams such as polymer-grade hydrocarbons, where the typical specifications of

impurities lie in the parts-per-billion (ppb) level. Typical specifications of some of these impurities in

polymer-grade hydrocarbon streams are given in Table 2-1 [3, 4].

Table 2-1. Typical specifications of some impurities in polymer-grade hydrocarbon streams [3, 4].

Impurity Typical Specification

Arsine Less than 20 ppb

Phosphine Less than 20 ppb

Ammonia Less than 100 ppb

Hydrogen Sulfide Less than 20 ppb

Carbonyl Sulfide Less than 20 ppb

Nitrogen Dioxide Less than 50 ppb

HCN Less than 100 ppb

HCl, HF Less than 200 ppb

Phosgene Less than 50 ppb

Sulfur Dioxide Less than 50 ppb

Chlorine Less than 30 ppb

Methyl mercaptan Less than 20 ppb

6

The specifications for polymerization applications of these impurities are so strict because even at

these low amounts these compounds can have a great effect on the performance of the catalyst used

in the polymerization process (e.g. arsine can adversely affect certain catalyst properties and it can

also lead to polymer contamination). Therefore the industry demands techniques that can analyze

samples at low ppb levels with high levels of accuracy and in principle in real-time [3, 4].

For naphtha samples generally these specifications are not as strict as those reported in Table 2-1.

The results of a survey performed on data from the ethylene manufacturers in the United States is

given in Table 2-2, which gives the maximum allowed concentration that these manufacturers apply

for some specific impurities in naphthas [5].

Table 2-2. Maximum allowed level for some impurities in naphtha samples [5].

Contaminate Maximum allowed concentration

Methanol 2-2000 ppm

Carbon dioxide 175-15000 ppm

Mercury 1-10 ppb

Fluorides 1-5 ppm

Chlorides 5 ppm

Sulfur 5-2000 ppm

Radon 800-2000 CT

NOX 0,1 ppb

Sodium 0,5-5 ppm

Water 5-300 ppm

Arsine 0,01-7 ppm

Metal 2 ppm

Oxygenates 100 ppm

Oxygen 2-1000 ppm

Lead 25-100 ppb

Carbon monoxide 4000 ppm

Vanadium 25 ppb

Olefins 20 ppb

This literature survey offers an overview of different techniques to analyze trace amounts of nitrogen

components, sulfur components, oxygenates, arsine, phosphine and hydrides in hydrocarbon

matrices.

7

2. Dry colorimetry

This technique was developed as a reaction to Gas Chromatography-Atomic Emission Detector (GC-

AED), which requires a highly skilled operator because of the complexity of operating the system and

interpreting the results. Dry colorimetry allows to easily analyze a sample for one particular impurity

with limited effort [3].

2.1. Principle

Conventional colorimetric methods utilize an impinger to collect gas in a liquid medium. Dry

colorimetry however, uses a tape, which is a non-toxic, proprietary chemical reagent system. This

tape reacts with a specific component present in a gaseous stream. When the stream contains the

specified impurity, the tape will change color in proportion to the amount of impurity present in the

gas: the higher the concentration, the darker the formed stain will be. This stain is then read by a

photo-optical system and compared to a standard response curve in the instrument’s data system [3,

6]. Figure 2-1 displays a possible set-up for a modern dry colorimetric system [7].

Figure 2-1. Possible set-up for performing dry colorimetry [7].

When dry colorimetry is used as an analytical system the tape is exposed to a metered sample

stream. The discoloration of the tape, due to impurities present in the stream, is measured by

reflecting a beam of light off the exposed part where the stain has formed during exposure. The

reflected beam is measured and compared to a standard response curve in the system in order to

determine the exact concentration. In combination with microprocessor control systems, this

technique provides an excellent accuracy, repeatability and detectability of very low concentrations.

8

Moreover, this is a technique ideal for direct, online analyses of impurities in hydrocarbon streams,

which also allows for the detection of multiple impurities in multiple streams with just one analyzer

system [3, 6, 8].

Key benefits of dry colorimetry are [3, 8]:

The detection tape reacts instantly with the target components which makes fast analysis

possible.

The detection is very sensitive. Detection limits lie in the low ppb-region.

The detection tape is very specific to the analyte that it is designed to measure. Reaction can

only occur due to the presence of that specific component.

Possible downsides to this technique are the fact that for measuring multiple impurities, multiple

detection tapes have to be available for those specific components and that an analyzer system can

only contain up to 8 tapes at a time, so it can only detect 8 different impurities at a time [3].

2.2. Applications

Dry colorimetry has multiple applications for detecting impurities in light hydrocarbon streams.

Literature describes the use of dry colorimetry for the detection of arsine, hydrogen fluoride (HF),

NOX and sulfur components in hydrocarbon streams, such as polymer-grade hydrocarbons (e.g.

ethylene and propylene), gases and fuels [3, 7, 8] .

Results of comparative analyses performed using dry colorimetry as well as NIOSH standard

detection methods are given for the detection of arsine and hydrogen fluoride in hydrocarbon

streams in Table 2-3 and for the detection of NOX in hydrocarbon streams in Table 2-4. These results

support the statement that dry colorimetry is as accurate as the more conventional, standardized

methods [3, 8].

9

Table 2-3. Dry colorimetry results as compared to analyses performed with NIOSH standard methods for detecting arsine in hydrocarbon streams[3].

Gas

Concentration as determined

by standard NIOSH methods

Analyzer reading (ppb)

AsH3 23 22

49 47

99 100

HF 5,0 4,0

10,1 9,7

19,5 19,7

24,5 26,0

Table 2-4. Dry colorimetry results as compared to analyses performed with NIOSH standard methods for detecting NO and NO2 in hydrocarbon streams[8].

Gas

Concentration as determined

by standard NIOSH methods

Analyzer reading (ppb)

NO2 10 14

45 47

100 104

NO + NO2 200 202

100 97

10 6

3. Inductively Coupled Plasma – Mass Spectrometry (ICP-MS)

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) is an elemental analysis tool which was

originally designed and developed to replace Atomic Absorption Spectrometry (AAS) and ICP-Optical

Emission Spectrometry (ICP-OES) for the analysis of metals in aqueous and organic matrices [9]. ICP-

MS can detect nearly every element in the periodic chart with a very high sensitivity. If necessary, it

can also be connected to a GC in order to have a separation of the components before sending them

to the detector. However, ICP-MS cannot analyze the elements that are highly abundant in the

atmosphere on a satisfactory level (e.g. nitrogen, oxygen, and argon), because the ICP-MS is an open

system [9, 10].

10

3.1. Principle

Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) is the combination of two well-known

techniques, i.e. ICP and MS. A schematic overview of the interface between a Gas Chromatograph

(GC), the ICP feed system (i.e. nebulizer and spray chamber) and plasma torch is shown in Figure 2-2

[11]. This particular configuration allows for a simultaneous introduction of both liquid and gas

samples via the nebulizer/spray chamber and the GC-interface [11].

Figure 2-2. Schematic overview of GC-ICP interface [11].

A schematic overview of the ICP torch is given in Figure 2-3. Argon is introduced tangentially into the

torch through the outer chamber, where it spirals upwards towards the radio frequency induction

coil (RF Load Coil). There, a spark is generated in order to form free electrons in the argon, which are

accelerated towards the argon atoms and collide with them. This causes the argon atoms to be

partially ionized, which forms the plasma. The outer tube also acts as an isolator to prevent a

potential short circuit, caused by the plasma in the coil. The sample, which is an aerosol or vapor in

dissolved in argon, is introduced into the plasma through the inner tube. An auxiliary gas flow, also

argon, is sent between the two tubes in order to control the position of the plasma relative to the

front end of the torch [12-14].

The plasma, which mainly consists of argon ions, has enough energy to vaporize, excite and ionize

the analyte molecules within this sample. The interface between the ICP apparatus and the MS is a

critical part in this system, and therefore has to be very well tuned. Its function is to transport the

ions from the plasma, which is at atmospheric pressure, to the mass spectrometer, which is at a

vacuum pressure [12, 13].

11

Figure 2-3. A schematic overview of the ICP torch [12].

In this interface, the sample is sent through a series of electrostatic lenses, which focus the ion beam

and separates the analyte ions from unwanted neutral particles, before they are sent into the ICP

collision cell (Figure 2-4 [12]). This cell uses a quadrupole, hexapole or octopole to guide the ions

through a stream of collision gas (e.g. He), which collides with the larger, polyatomic interferences.

The latter lose much more kinetic energy than the smaller analyte ions. A barrier at the exit uses

energy discrimination to prevent the interferences from entering the mass spectrometer, and hence

obtain higher sensitivities. Notice that the collision cell isn’t a necessary part of this system, but it

helps to achieve lower detection limits [12-14].

Figure 2-4. Schematic overview of the focusing lenses and the collision cell in an ICP-MS detector [12].

After interferences are removed, the sample is introduced into a mass spectrometer. Usually this is a

single quad type spectrometer but other types can also be used [12-14].

12

3.2. Applications

The ICP-MS system is an open system and therefore it can’t provide a satisfactory replacement for

other techniques (e.g. AED) in the measurement of elements that are highly abundant in the

atmosphere, such as nitrogen, oxygen, carbon and argon. Fluorine can’t be measured at all, due to

its ionization potential [9].

Geiger et al. [11] described the use of ICP-MS as the detector for sulfur and metal hydride impurities

in hydrocarbon matrices. They used the ICP-MS with a hexapole collision cell. In the determination of

sulfur species in gasoline and Fluid Catalytic Cracking (FCC) naphtha they obtained limits of detection

(LOD) in the 5-10 ppb range. They also performed analyses on some samples with sulfur species

present in the low ppm range, which were all easily quantified using this technique. A study on the

determination of carbonyl sulfide (COS) and arsine (AsH3) in polymer grade propylene was also

performed. These low level impurities required a chromatographic separation before sending the

mixture towards the ICP-MS. The results of these analyses are shown in Figure 2-5 [11].

Figure 2-5. Results of ICP-MS analysis on propylene - propane mixtures [11].

The upper chromatogram in Figure 2-5 shows the results for the determination of carbonyl sulfide.

The determined concentration is 40 ppb. A small amount of signal suppression from the matrix peak

is noticeable, which is said to be normal. The bottom chromatogram shows the detection of 2,5 ppb

arsine in a propane/propylene mixture. In this case the matrix interference is negligible. The achieved

limits of detection were around 200 parts-per-trillion (ppt)[11].

13

Thermo Electron Corporation [15] utilized the ICP-MS with collision cell technology (CCT) to

determine trace elements in gasoline samples. Table 2-5 shows the estimated 3-sigma detection

limits for 10 replicate measurements. It has to be notified that these samples contained considerable

amounts of contamination, which complicated the analyses even more. The detection limits range

from 10 ppt for molybdenum (Mo) to 1 ppb for boron (B) and aluminum (Al). They also claimed an

excellent stability of their results, which means the ICP-MS can work for a relatively long period

without the need for a calibration [15].

Table 2-5. Detection limits for certain analytes in gasoline samples using ICP-MS [15].

Analyte Detection Limit (ppb) 10B 1

24Mg 0,2 27Al 1 40Ca 0,6 48Ti 0,3 51V 0,06

52Cr 0,2 55Mn 0,2 56Fe 0,8 60Ni 0,2 63Cu 0,6 68Zn 0,4

98Mo 0,01 107Ag 0,02 111Cd 0,04 138Ba 0,04 208Pb 0,3

Woods et al. [16] used an ICP-MS coupled with an Octopole Reaction System (ORS), which is a

collision cell equipped with an octopole, to perform elemental analyses on trace analytes in biodiesel

and kerosene. Both the detection limits (LOD) and the background equivalent concentrations (BEC1

[17]) were in the ppb to ppt range, except for sulfur which was determined in ppm levels. The results

are given for 30 elements in Table 2-6 [16]. The precision and stability of this technique again were

claimed to be very good. The precision varied between 1,3% for arsenic (As) and 6,4% for boron (B)

during a 10-hour stability test [16, 17].

1 The background equivalent concentration (BEC) is defined as the concentration at which the S/N ratio for that

component is exactly 1 (signal = background).

14

Table 2-6. Detection Limits (LOD) and Background Equivalent Concentrations (BEC) for certain elemental analytes in kerosene and biodiesel using ICP-MS coupled with and Octopole Reaction System (ORS) (in ppb, except for sulfur in ppm).

Kerosene Biodiesel Kerosene Biodiesel

Element LOD BEC LOD BEC Element LOD BEC LOD BEC

Be 0,0153 0,0234 0,0109 0,0423 Ni 0,025 0,0367 0,126 0,165

B 0,344 4,63 6,57 30,1 Cu (MM=63) 0,0525 0,681 0,0264 0,832

Na 2,99 18,8 1,19 62,5 Cu (MM=65) 0,0723 0,675 0,101 0,834

Mg 1,37 8,9 8,65 9,77 Zn 0,0393 0,0685 0,211 0,963

Si 5,17 53,3 7,44 48,7 As 0,0896 0,192 0,066 0,547

P 10 52 22,7 811 Sr 0,0335 0,121 0,0631 0,215

S 0,124 0,569 0,0293 0,98 Mo 0,5 0,411 0,371 0,332

K 0,649 1,34 2,1 7,25 Ag 0,0374 0,0832 0,149 0,155

Ca 0,568 2,95 6,4 9,21 Cd 0,121 0,138 0,108 0,0913

Ti 0,125 0,0396 0,706 0,68 Sn 0,0173 0,0901 0,411 84,8

V 0,0198 0,0435 0,0409 0,134 Sb 0,0261 0,0827 0,0395 0,0895

Cr 0,0935 0,0772 0,0224 0,402 Ba 0,0472 0,145 0,099 1,87

Mn 0,0249 0,0527 0,0563 0,101 W 0,0111 0,0231 0,0177 0,0261

Fe 0,0447 0,129 0,0869 4,21 Hg 0,0147 0,107 0,123 0,403

Co 0,0113 0,0245 0,0337 0,04 Pb 0,00724 0,0595 0,0226 0,0666

4. Lowox analytical column

The CP-Lowox analytical column is a chromatographic column designed specifically for the analysis of

oxygenated compounds. It has a unique selectivity for alcohols, aldehydes and ketones, which allows

for a very selective separation of these analytes from hydrocarbon matrices [18].

4.1. Properties

The CP-Lowox analytical column is a Porous Layer Open Tubular (PLOT) column. The stationary phase

is a proprietary salt deactivated adsorbent with a high chromatographic selectivity for low molecular

weight oxygenated hydrocarbons [19]. On the one hand, hydrocarbons elute very fast through this

column because there is virtually no interaction with the polar stationary phase. On the other hand,

oxygenated compounds have a high retention index (e.g. retention index of methanol is higher than

1400 at temperatures >200°C [20]) because these polar compounds have high interactions with the

stationary phase. This means that light oxygenates elute long after heavy hydrocarbons, e.g.

methanol elutes later than n-tetradecane. Moreover, because of this high selectivity for oxygenated

hydrocarbons, this column is able to separate the most oxygenated compounds from each other,

despite of their low difference in boiling temperature [19-21].

15

The column also possesses a high inertness, which results in symmetric peaks in the chromatogram,

and a high temperature stability (i.e. applications are described with temperatures up to 350°C with

virtually no bleed). These characteristics make this column ideal to use in situations where very low

amounts of oxygenates have to be determined in hydrocarbon matrices such as ethylene, propylene,

butane and C5-streams [19-21]. A comparison of the most important characteristics of the Lowox

column to other columns with a polar stationary phase is given in Table 2-7 [20].

Table 2-7. Comparison of some characteristics of polar phases used for oxygenates analysis [20].

Column PEG 100% Cyano CP-TCEP CP-Lowox

Polarity Medium High High Very high

Selectivity High Low Medium Very high

Tmax (°C) 250 2245 140 350

Bleed at Tmax (pA)2 10 10 15 1

Trapping effect Low Low Low Very high

0,53-mm columns Yes No No Yes

The CP-Lowox analytical column is only available in certain dimensions (I.D.: 0,53 mm, length: 10 m,

film thickness: 10 µm [22]). Because of this relatively wide bore, when used in combination with a

mass spectrometer, a restrictor column has to be inserted between the Lowox column and the MS in

order to prevent the vacuum of the MS of protruding in the chromatographic system.

4.2. Typical set-up with the CP-Lowox analytical column

The CP-Lowox column is mostly used in multicolumn applications for the detection of trace level

oxygenates in different matrices (e.g. air and hydrocarbons)[23]. The first column usually is a

nonpolar pre-column, which isolates the oxygenated, polar components and low molecular weight

hydrocarbons from the heavier hydrocarbons (typically C12 and up). Only these light components

advance to the Lowox column. The light hydrocarbons elute fast through this column due to their

nonpolar nature. The oxygenated components are separated from each other due to the high

selectivity of the Lowox column for oxygenates. Meanwhile, the heavy hydrocarbons, which remain

on the first column, are backflushed over the first column to a vent or sent to another column,

parallel with the CP-Lowox. This is done so that the analytical column isn’t overloaded [23].

2 Resulted detector signal in pico-Ampere due to column bleeding.

16

4.3. Applications

De Zeeuw et al. [20] used a single column system to analyze trace level oxygenates in a light

hydrocarbon matrix. They did this using a technique called stack injection which requires successive

injections of the same sample using a sampling valve while the oven is kept at a constant

temperature. The hydrocarbons elute through the column, but the sum of all the oxygenates from

the different injections are trapped and refocused as a tight narrow band at the front of the CP-

Lowox column. Figure 2-6 shows a chromatogram obtained using this technique where it is applied

for the determination of oxygenates in n-pentane. A stack of 10 injections, which means a sensitivity

enhancement with a factor 10, was performed resulting in a limit of detection of 35 ppb for

methanol in pentane [20].

Figure 2-6. Stack injection technique applied for oxygenates in pentane using a CP-Lowox column. (1) acetaldehyde, (2) propanal, (3) methanol, (4) acetone [20].

De Zeeuw et al. [20] also applied a multidimensional GC-FID with a nonpolar pre-column combined

with the Lowox column to analyze oxygenate impurities in C1-C12 stream. The present oxygenated

components eluted together in the same range as the C1-C6 hydrocarbon fraction. The heavier

components were backflushed over the pre-column and sent to a vent. A chromatogram of the C1-C6

fraction with the oxygenates is given in Figure 2-7. The components that elute the first in this

chromatogram are the hydrocarbons. The numbered peaks are the oxygenates, which elute later due

to their polarity. Note the good separation of these oxygenates due to the very high selectivity of the

Lowox column. The authors also claim that by injecting a large sample onto the pre-column, it would

be possible to determine low ppb levels of the oxygenates in naphtha samples using this set-up [20].

17

Figure 2-7. Example of oxygenated impurities in naphtha samples analyzed with a multidimensional GC-FID system using the CP-Lowox column as analytical column [20].

Bruker [23] developed a similar system to the multidimensional GC-FID technique of de Zeeuw et al.

[20]. They performed analysis on certain oxygenates (ETBE, MTBE, methanol, acetone, MEK, ethanol

and 1-propanol) in a light hydrocarbon matrix. They reported a limit of detection of 100 ppb, a

system linearity of 100 ppb to 500 ppm, which is a relatively high concentration range. The typical

repeatability3 they obtained was <2% for gaseous streams and <2,5% for liquid streams for MTBE,

methanol and ethanol [23].

3 Expressed through the percentage relative standard deviation (%RSD)

18

5. Dielectric Barrier Discharge detector (DBD)

The Dielectric Barrier Discharge (DBD) detector was invented by Monagle [24] and has recently been

commercialized by Advanced Industrial Chemistry Corporation as a sensitive and selective detector

for GC analysis [25].

5.1. Principle

A schematic overview of the Dielectric Barrier Discharge (DBD) detector is given in Figure 2-8 [26]. It

consists of an alternating current generator, two electrodes (i.e. a high voltage electrode and a

ground electrode), a dielectric (indicated as streamer material in Figure 2-8) and a discharge gap

where the plasma is formed [26].

Figure 2-8. A schematic overview of the Dielectric Barrier Discharge detector [26].

The DBD detector is based on the use of a dielectric barrier discharge, which is a plasma discharge

obtained through a high voltage alternating current applied to a dielectric material like glass or

Pyrex®. The application of a high voltage to a gas results in a breakdown of the gas, followed by a

discharge from one electrode to the other [27]. This discharge creates meta-stable species and high

energy photons, which are used to ionize the analytes of interest in the sample [25]. The amount of

current funneled into any one discharge is limited by the fact that the dielectric barrier behaves as a

capacitor in the localized region of the discharge. As a consequence, each discharge is self-

terminating, which means that the discharge puts a substantial amount of energy into each discrete

discharge to create highly excited state molecules and atoms, such as helium and argon [25, 27].

The DBD uses a countercurrent flow configuration. Reaction gas enters the detector from the top

while carrier gas enters via the bottom. A mixture of both gases leaves the detector via the side arm

of the detector. this detector has a stable baseline because of the fact that this configuration

prevents the interaction between the solute gas and the plasma. When introduced into the detector,

the analyte molecules interact with the reaction gas and become ionized. These are then detected by

collecting them on the electrodes. One of the electrodes is held at ground potential, while the other

19

is connected to a positively biased direct current electrometer, inducing an electric field. This causes

the charged particles to drift from one electrode to the other, which provokes a current flow [25, 27].

Advantages of the DBD detectors are first of all the fact that the discharge is self-terminating and

non-thermal, which means that the electrode wear is relatively low. The latter implies that, in

contrary to the more traditional, arc-type discharge systems, this DBD doesn’t have to be operated

with some sort of cycling system which implements a cooling step of the electrodes. Since the

discharge is also non-thermal, it is possible to use pure reaction gases, such as argon, instead of the

blends, which have to be used in the arc-type discharge systems in order to prolong the durability of

the electrodes. Another advantage using the DBD is the fact that the power sources can be of the

relatively simple alternating current kind, which can be readily obtained from the commercial

market. The fact that the detector works at slightly elevated pressures makes the need for a vacuum

pumping system unnecessary, which is an advantage in comparison to many other GC-detectors.

Finally, the plasma is a virtually continuous light source because of a large number of discrete

discharges at a high rate, which means that the plasma is very stable and an ideal photoionization

source for this detector [25, 27].

There are two possible electrode configurations possible for this type of detector. The first consists of

a concentric electrode configuration in which the inner electrode is grounded and the outer is

connected to an electrometer. This design was developed for working with large diameter columns.

A second design was subsequently designed to reduce the internal volume of the detector for use

with capillary columns. It was named “The Mini” and it uses an over-and-under electrode lay-out.

Here, the upper electrode is grounded and the lower one is connected to the electrometer [25]. The

DBD detector can be used in two different modes, ( i.e. argon ionization and helium ionization

mode)depending on the application at which it is used [25, 27, 28].

Applications of the DBD detector in helium ionization detection mode [27]:

The measurement of gas impurities in other high purity gases

The measurement of freons

The measurement of impurities in ethylene

The measurement of CO and CO2

20

Possible applications of the DBD detector in argon ionization detection mode [27]:

The measurement of BTEX compounds at very low levels,

The measurement of phosphine or arsine,

The measurement of ethylene oxide,

The measurement of formaldehyde in formalin4

The measurement of ammonia.

5.2. Application

Gras et al. [28] have used the DBD detector, operating in argon mode, to detect trace amounts of

arsine and phosphine in light hydrocarbon matrices, such as propylene. In order to achieve the very

low detection limits, which were required with these analyses, they fitted the GC with a large volume

gas injection system and a heart-cutting system with capillary flow technology. The results showed

that the phosphine and arsine were well separated from the propylene matrix, which eluted later

than the analytes, as shown in Figure 2-9 [28]. Therefore, the authors suggest to backflush the

propylene matrix once the analytes of interest are transferred onto the second dimension in order

not to overload the analytical column [28].

Other impurities include hydrogen sulfide, which could also be separated if present in the sample,

and carbonyl sulfide (COS). The latter could not be detected as it co-elutes with propylene. The

detection limits of arsine and phosphine were both calculated at 5 ppb [28].

Figure 2-9. Chromatogram which shows the separation of arsine and phosphine from the propylene matrix as obtained by Gras et al [28].

4 Commercial name for solutions of formaldehyde in water

21

6. Selected Ion Flow Tube – Mass Spectrometry (SIFT-MS)

Selected Ion Flow Tube (SIFT) instruments were first constructed in the late 1970’s. Its initial use was

to model reactions between gas phase ions and neutral species under atmospheric and interstellar

conditions. Later, due to its extreme sensitivity, it was used as a detector to analyze certain analyte

concentrations in air. This technique is known as Selected Ion Flow Tube – Mass Spectrometry (SIFT-

MS). It proved to be an ideal technique to detect and quantify components in trace concentrations,

i.e. low ppb and even ppt level. Its main applications up to now are the analysis of air samples, the

headspace of certain liquid samples (e.g. urine), human breath samples, exhaust gases, etc. [29, 30]

6.1. Principle

Each SIFT-MS instrument can be divided into 4 main regions, as displayed in Figure 2-10. These are

the ion creation region, the ion selection region, the reaction region and the ion detection region.

Figure 2-10. Schematic overview of the Selected Ion Flow Tube - Mass Spectrometer (SIFT-MS)

working principle.

22

SIFT-MS is based on the soft chemical ionization (CI) of analytes using certain reagent ions (i.e. H3O+,

NO+ and O2+), coupled with fast flow tube technology and mass spectrometry. The reagent ions are

formed in the ion creation region by exposing moist air to high intensity microwave energy. Next, the

wanted reagent ions are selected in the ion selection region using a quadrupole mass filter. This

quadrupole is maintained at vacuum pressure in its own chamber (approximately 4,7 10-3 Pa). A lens

arrangement focusses the ions into the flow tube [29, 30].

Before entering the flow tube of the reaction region, a carrier gas mixture, He and Ar, is added to the

reagent ions. This carries the reagent ions through the flow tube. Argon is added to reduce the loss

of ions by diffusion to the walls. The ratio of He to Ar is typically 2:3 at a pressure of about 0.5 Torr

(66,66 Pa). The mixture is introduced in the flow tube via a Venturi orifice. In the reaction region the

reagent ions react with the analyte ions, which are introduced via a heated sampling inlet line [31].

In the flow tube chemical ionization (CI) takes place during a specific time period. This is the

ionization of analytes by ion-molecule interactions. In this particular case it is soft chemical

ionization, which means that the analytes aren’t fragmented as much in comparison to normal

chemical ionization processes [32]. There are 4 main reaction types that take place in the reaction

section, i.e. proton transfer, hydride ion and hydroxide ion transfer, ternary association and charge

and dissociative charge transfer. The proton transfer only occurs with H3O+, hydride ion and

hydroxide ion transfer and ternary association only occur with NO+ and the charge transfer and

dissociative charge transfer occur with NO+ as well as with O2+ [29].

After passing through the reaction section, the gas mixture, containing the analyte ions, is sent to the

mass spectrometer (MS) via a set of focusing lenses. The latter focus the ions into the quadrupole of

the MS, which is an ordinary single quad MS [30]. The calculation of the analyte concentrations is

done based on the kinetics of the reaction between the reagent ions and the analyte ions. The

applied equation to perform these calculations is given in equation 2.1 [30]:

( )

(2.1)

With:

c(M) the concentration of analyte M (molecules/cm³)

Ip the number of counts per second of product ion detected

tr the time available for the reaction between analyte and reagent ion

k the reaction rate coefficient between analyte and reagent ion (cm³/molecule s)

Ii the number of counts per second of reagent ion detected

23

This is the equation used for one reagent ion and one product ion. In a more realistic situation this

would be expanded with extra terms because there are multiple reagent ions with different

reactivities and more than one product ion is produced by the analytes [30].

6.2. Applications

No proof of concept for trace analysis of oxygenates exist, however, Milligan et al. [31] used SIFT-MS

to detect and quantify trace amounts of phosphine in nitrogen. The phosphine was prepared by

adding water to calcium phosphide. This pure phosphine was then diluted in nitrogen in Tedlar®

bags5. To obtain the very low concentrations, a so-called parent sample of about 5 ppm was

prepared. From this parent sample, measured amounts were extracted and injected into a new

Tedlar® bag filled with nitrogen. Because the reaction kinetics were known for phosphine, a

concentration could be calculated. The limit of detection for a ten second scan was calculated at

about 190 parts-per-trillion (ppt) and the limit of quantification at 940 ppt [31].

Prince et al. [33] performed real-time atmospheric monitoring of some volatile organic chemicals

(VOC) in air, i.e. ethylene, ethanol, 1,3-butadiene, benzene and toluene. Again, the kinetics of the

reactions between the reagent ions and the studied analytes were known, which made it possible to

accurately calculate the concentration. The limits of detection (LOD) for the analytes are given in

Table 2-8 [33].

Table 2-8. Limits of detection (LOD) for the selected analytes measured in air samples [33].

Analyte LOD (ppt)

Toluene 35

1,3-butadiene 6,4

Benzene 72

Ethanol 210

Ethane 180

Calibration lines for each of these components were also made in order to check their linearity. The

lowest concentration measurements were obtained for 1,3-butadiene in air. For this component

Prince et al. measured a concentration of 9 ppt (±4 ppt). For a 1 second measurement the LOD of 1,3-

butadiene was calculated at 50 ppt [33].

5 Small plastic sampling bags

24

7. Conclusion

This literature survey gives an overview of some of the current techniques in the detection of trace

impurities in hydrocarbon matrices.

Despite most described applications are capable of measuring a part of the possible impurities

present in a hydrocarbon matrix, none of them is capable of measuring them all at once. This means

that companies have to invest in multiple techniques and instruments to be able to do this. This

means a big investment has to be done as most of these technologies don’t come cheap. The

technology which is most promising to be able to detect and quantify all target components very

quick and very precise is dry colorimetry. Moreover, this is one of the cheapest technologies

described in this literature survey. However, downsides to dry colorimetry are that for each impurity

one wants to measure, a different detection tape has to be available. Moreover, the required

equipment could become very big if one wants to be able to measure a wide range of impurities,

because for each impurity a separate stream needs to be send to a detection tape.

SIFT-MS has the potential to be an analytical technique which is capable of analyzing very complex

samples very precise, e.g. measurement of trace impurities in air samples by Prince et al. [33]. One of

the unknowns however, is how SIFT-MS can cope with the hydrocarbon matrix, as there are little or

no studies known on this subject. This will be investigated during this master thesis, and will be

reported in Chapter 5.

25

8. References

1. Bansal, V., G.J. Krishna, A.P. Singh, A.K. Gupta, and A.S. Sarpal, Determination of hydrocarbons types and oxygenates in motor gasoline: A comparative study by different analytical techniques. Energy & Fuels, 2008. 22(1): p. 410-415.

2. American Society for Testing and Materials, A. ASTM standards. 2011 [cited 2011 20 November]; Available from: http://www.astm.org/Standard/index.shtml.

3. Thind, S.S., Analysis of Impurities in Polymer-grade Ethylene, Propylene and 1,3-butadiene. Petro Industry News, 2003(June/July 2003): p. 1-2.

4. Air Liquide America Specialty Gases, L., Feedstock Purity Calibration Solution, A. Liquide, Editor. 2011: Plusteadville, PA. p. 1-4.

5. Graham, M.A., Selected Ethylene Feedstock Impurities: Survey Data, E.P. Commitee, Editor. 1992: Houston, TX. p. 82-118.

6. Thind, S.S., The Detection of Total Organic Chloride and/or Hydrogen Chloride: Utilizing A Dry Colorimetric Detector. p. 6-7.

7. Thind, S.S., Evaluation of Sulphur Measurement Analytical Techniques for Sulphur in Gases and Fuel. Petro Industry News, 2004(June/July 2004): p. 8-10.

8. Thind, S.S., The On-Line Detection of Oxides of Nitrogen in Light Hydrocarbon Streams by Modified Chemiluscence Detection and/or by Dry Colorimetric Detection. Petro Industry News, 2005(April/May 2005): p. 1-2.

9. Geiger, W.M. and M.W. Raynor, ICP-MS: A Universally Sensitive a GC Detection Method for Specialty and Electronic Gas Analysis. Spectroscopy, 2008. 23(11): p. 34-+.

10. Rodgers, R.P. and A.M. McKenna, Petroleum Analysis. Analytical Chemistry, 2011. 83(12): p. 4665-4687.

11. Geiger, W.M., S. McSheehy, and M.J. Nash, Application of ICP-MS as a multi-element detector for sulfur and metal hydride impurities in hydrocarbon matrices. Journal of Chromatographic Science, 2007. 45(10): p. 677-682.

12. Agilent Technologies, I., ICP-MS, Inductive Coupled Plasma-Mass Spectrometry: A primer 2005. 84.

13. Thomas, R., A Beginner's Guide to ICP-MS. Spectroscopy, 2001. 16(4): p. 38-42. 14. Abro Pharmaceuticals, L. ICP MS LAB: principle. 2009 [cited 2011 15 November]; Available

from: http://icp-ms-lab.com/principle/. 15. Thermo Electron, C., Direct Analysis of Trace Elements in Gasoline: XseriesII ICP-MS with 3rd

generation CCT 2005, Thermo Electron Corporation. p. 1-2. 16. Woods, G. and F. Fryer, Direct elemental analysis of biodiesel by inductively coupled plasma-

mass spectrometry. Analytical and Bioanalytical Chemistry, 2007. 389: p. 753-761. 17. Goldstone, L. and O. Hirsch, Introduction to Atomic Emission Spectrometry. ICP Optical

Emission Spectroscopy, 2011. Technical Note 12: p. 1-6. 18. Dettmer, K., U. Felix, W. Engewald, and M. Mohnke, Application of a unique selective PLOT

capillary column for the analysis of oxygenated compounds in ambient air. Chromatographia, 2000. 51: p. S221-S227.

19. Vickers, A., GS-OxyPLOT: a PLOT Column for the GC analysis of Oxygenated Hydrocarbons, I. Agilent Technologies, Editor. 2007. p. 1-2.

20. de Zeeuw, J., C. Duvekot, J. Peene, P. Dijkwel, and P. Heijnsdijk, GC: A Review of the State-of-the-Art Column Technologies for the Determination of ppm to ppb Levels of Oxygenated, Sulfur, and Hydrocarbon Impurities in C1-C5 Hydrocarbon Streams. Journal of Chromatographic Science, 2003. 41(10): p. 535-544.

21. de Zeeuw, J. and J. Luong, Developments in stationary phase technology for chromatography. Trends in analytical chemistry, 2002. 21(9+10): p. 594-607.

26

22. Agilent Technologies, I. GS-OxyPLOT Column Information 2000 - 2012 [cited 2012 7 February]; Available from: http://www.chem.agilent.com/en-US/Products/columns-supplies/gc-gc-mscolumns/jwgs-oxyplot/pages/gp54615.aspx.

23. Bruker, Low Level Oxygenates Analyzer, Bruker, Editor. 2010. p. 1-6. 24. Monagle, M.J.P., NW., Albuquerque, NM, 87114), Trace constituent detection in inert gases.

1999: United States. 25. Gras, R., J. Luong, M. Monagle, and B. Winniford, Gas Chromatographic Applications with the

Dielectric Barrier Discharge Detector. Journal of Chromatographic Science, 2006. 44(2): p. 101-107.

26. Streamer Technologies, I. The Dielectric Barrier Discharge Detector. 2010 [cited 2012 11 February]; Available from: http://www.streamer-tech.com/index.php?p=2_6_Environmental-Plasma-processes.

27. Advanced Industrial Chemistry, C. White paper on the DBD. 2011 [cited 2012 9 February]; Available from: http://www.gcsrus.com/pdf/whiteppr4.pdf.

28. Gras, R., J. Luong, M. Hawryluk, and M. Monagle, Analysis of part-per-billion level of arsine and phosphine in light hydrocarbons by capillary flow technology and dielectric barrier discharge detector. Journal of Chromatography A, 2010. 1217(3): p. 348-352.

29. SIFT-MS: Selected Ion Flow Tube Mass Spectrometry. 2009 [cited 2012 9 February]; Available from: http://www.sift-ms.com/.

30. Davis, B.M., Volatile Organic Compounds and Antioxidants in Olive Oil: Their analysis by Selected Ion Flow Tube Mass Spectrometry. 2007, University of Canterbury: Christchurch. p. 294.

31. Milligan, D.B., G.J. Francis, B.J. Prince, and M.J. McEwan, Demonstration of selected ion flow tube MS detection in the parts per trillion range. Analytical Chemistry, 2007. 79(6): p. 2537-2540.

32. Prest, H.F., Ionization Methods in Gas Phase Mass Spectrometry; Operating Modes of the 5973Network Series MSDs, I. Agilent Technologies, Editor. 1999: California.

33. Prince, B.J., D.B. Milligan, and M.J. McEwan, Application of selected ion flow tube mass spectrometry to real-time atmospheric monitoring. Rapid Communications in Mass Spectrometry, 2010. 24(12): p. 1763-1769.

27

Chapter 3: Ultratrace Quantitative analysis of oxygenates using

LowoxMS

1. Introduction

The use of high yield metallocene catalysts has dramatically increased both efficiency and selectivity

of polymerization processes [1]. Unfortunately, these catalysts are extremely prone to poisoning by

feedstock impurities, such as arsine (AsH3), phosphine (PH3), oxygenates (e.g. dimethyl ether) and

sulphur containing compounds (mercaptanes, sulfides, etc.) [2,3]. Minute amounts of these

compounds suffice to impose undesirable effects and induce immediate loss of catalytic activity and

reaction yield. At the same time, trace contaminants at the part-per-billion (ppb) concentration levels

can end-up in the polymers and alter subsequent polymer properties and characteristics.

For decades, process chemical and petrochemical analysts used to address their analytical challenges

mainly by relying on superior chromatography and smart tools such as valve switching, backflush and

Dean’s heart-cut. In combination with relatively cheap, robust and selective detectors, they were

capable of providing all information necessary to control and tweak petrochemical processes.

Organic catalyst poisoners are usually determined using dedicated chromatographic analyzers. These

systems are, typically, equipped with a dual capillary column configuration with backflush and fitted

with an FID. Under these conditions, limits-of-detection are usually situated around 100 ppb,

depending on the compound investigated and the complexity of the matrix that is introduced [4].

Unfortunately, this is far from sufficient to protect the latest catalysts, which start to deteriorate

already when fed with low ppb amounts [5,6].

Table 3-1. Typical specifications for catalyst poisoners in polymer grade hydrocarbons [6].

Impurity Typical Specification

Arsine Less than 20 ppb

Phosphine Less than 20 ppb

Ammonia Less than 100 ppb

Hydrogen sulfide Less than 20 ppb

Carbonyl sulfide Less than 20 ppb

Nitrogen dioxide Less than 50 ppb

HCN Less than 100 ppb

HCl, HF Less than 200 ppb

Phosgene Less than 50 ppb

Sulfur dioxide Less than 50 ppb

Chlorine Less than 30 ppb

28

Mass spectrometry (MS) is hardly used in petrochemical QC laboratories, which is primarily due to its

apparent complexity and higher cost-of-ownership. Nonetheless, MS detection has several distinct

advantages over classic analogue detectors. In full scan acquisition mode, for example, it allows to

track and identify unknown components using spectral deconvolution and subsequent library

matching. In selective ion monitoring (SIM) mode, on the contrary, MS permits trace and ultratrace

quantification of target analytes often superior to classic selective detectors. Moreover, MS permits

the use of mass labeled internal standards that behave identical to their native analogues, which has

a positive effect on overall method precision and accuracy. No wonder instrument manufacturers

have invested substantially in solutions aiming at reducing overall MS complexity total cost-of-

ownership, the last couple of years. Easy tune and calibration functionalities, increased sensitivity

and speed, new acquisition modes and elegant solutions that eliminate downtime, e.g. vacuum lock

technology, have largely contributed in this respect.

In the present Chapter an overview is given of the main characteristics and performance of a new

GC/MS analyzer we have recently developed. The system combines the chromatographic separation

power and backflush/Dean’s heart-cut capabilities of a classic oxygenate analyzer with the

orthogonal separation power, sensitivity, selectivity and overall robustness of the latest generation

single quadrupole mass spectrometers.

2. Experimental

2.1. Standards

Standard oxygenate reference mixture from Spectrum (Sugarland, TX, US) at 10 ppm in hexane. For

more details with respect to the composition of the test mix, please consult Table 3-2. Calibration

standards were prepared by gradual dilution in hexane at 0,01 ppm, 0,05 ppm, 1 ppm and 5 ppm.

2.2. Gas Chromatography

The GC analyzer consisted of a Thermo TraceGC (Austin, TX, US) refurbished by Global Analyzer

Solutions (GAS, Breda, The Netherlands). The system was fitted with a gas sampling valve (GSV), a

liquid sampling valve (LSV), a vaporizer, a standard split/splitless injector and an FID. Inside the GC

oven, a universal pressure balanced Deans assembly was installed to carry out heart-cut and/or

backflush. Auxiliary pressure for balancing was provided and controlled by a separate Trace GC DCC

unit.

29

The first dimension column was a Restek Rtx-1 (Bellefonte, PA, US) with the following dimensions: 15

m L x 530 µm I.D., 5 µm df. The second dimension column was an Agilent CP-Lowox (Middelburg,

The Netherlands) with the following dimensions: 10 m L x 530 µm I.D., 10 µm df. Restrictions were

250 µm I.D. uncoated Siltek-deactived fused silica capillary tubing (Restek), cut to the appropriate

length. All connections were made using SGE Analytical Science micro-siltite unions (Victoria,

Australia). Other relevant parameters are summarized in Table 3-2.

Table 3-2. Overview of the GC settings.

Oven Setting Remarks

Initial temp., °C 50

Initial time, min 5

Final temp., °C 240

Final time, min. 10

Rate, °C/min 5 Slow heating to maintain resolution

Inlet

Type Direct

Mode Splitless

Temp., °C 200

Carrier

Gas Helium

Mode Constant pressure

Setting, kPa 50

Detector

Type FID

Temp., °C 200

2.3. Mass Spectrometry

The GC analyzer was hyphenated to a Thermo ISQ single quadrupole mass spectrometer. The system

was applied in both full scan, SIM as full scan/SIM mode. All relevant MS settings are summarized in

Table 3-3.

All data were acquired using Thermo QuanLab Forms. The MS was applied after running a full EI tune.

System performance was verified using daily tune check.

30

Table 3-3. Peak identification and typical SIM ions.

tR (min) Name SIM ions

24,52 Diethyl ether 59, 74

25,05 Acetaldehyde 44

26,48 ETBE 59, 87

26,79 MTBE 57, 73

26,92 Di-isopropyl ether (DIPE) 59, 87

27,93 Propanal 57, 58

28,92 t-Amyl methyl ether (TAME) 73, 87

29,42 Propyl ether 73, 102

30,50 iso-Butanal 72

31,78 Butyraldehyde 57, 72

32,82 Methanol 29, 31

33,45 Acetone 58

35,26 Valeraldehyde 57, 58

36,13 MEK 57, 72

36,50 Ethanol 31, 45

39,28 iso-Propanol 45

39,45 Propanol 59, 60

40,21 Allyl alcohol 57, 58

41,54 iso-Butanol 41, 74

41,64 t-Butanol 57, 59

42,51 n-Butanol 55, 56

3. Results and Discussion

3.1. System Set-up

The capillary column set comprises the true core of any classic catalyst poisoner analyzer. Particularly

the second dimension column is of utmost importance. Ultimately, it is here that separation of the

analytes, from each other as well as from the aliphatic matrix in which they reside, occurs. Extremely

powerful and widely accepted for this purpose is the CP-Lowox column from Agilent (formerly

Chrompack/Varian). This column, which is based on a multilayer PLOT concept, is very polar and

characterized by a high MAOT with virtually no bleed at temperatures as high as 350°C (7). In

combination with a backflushed apolar-coated capillary column in the first dimension, matrix

separations up until C12 hydrocarbons are well within range.

31

Unfortunately, the CP-Lowox column is not available in MS-friendly, i.e. narrow bore, dimensions. In

order to avoid the MS vacuum from protruding the system, it is necessary to incorporate an

adequate restriction at the back of the column. A schematic representation of system set-up for

Lowox/MS applications is depicted in Figure 3-1.

Figure 3-1. Schematic representation of the GC/MS analyzer. BF = backflush; HC = hydrocarbons.

True backflush, as well as Dean’s heart-cut, is achieved by increasing the auxiliary pressure at the

column joint just above the first dimension residual head pressure at a certain moment in time.

Debalancing induces full flow reversal, whilst maintaining a small flow over the Lowox column for

chromatography. It is crucial to know the exact pressure at the column joint, in order to be

successful. When set too low, standard flow direction will be maintained and the first column is not

backflushed. When set too high, on the contrary, none of the target analytes will be able to reach the

second dimension column and the detector. The easiest way to determine the pressure at the

column joint involves setting the head pressure as regular and subsequently reading the residual

pressure at the auxiliary DCC, which is kept off at this stage. Five kPa differences suffice to induce

flow reversal. Although less straightforward due to the vacuum conditions, a similar approach is

applied in combination with MS. It also permits to determine the minimal length of the restriction

capillary (see Figure 3-1).

32

3.2. System Suitability

System suitability was evaluated by direct injection of the 10 ppm oxygenate standard. In order to

compare with a classic analyzer set-up, experiments were carried out using both FID as well as MS as

detector. The MS was applied in both full scan and SIM modes. A typical chromatogram with the MS

in full scan mode is depicted in Figure 3-2. For peak identification is referred to Table 3-2.

Figure 3-2. Chromatogram of the oxygenates standard at 10 ppm. The MS was applied in full scan mode. For peak identification is referred to Table 3-2.

A comparative overview of the results is given in Table 3-4. For each peak, the signal-to-noise ratio

was calculated (RMS) in full scan, extracted ion and SIM mode. These results were subsequently

expressed relative to the S/N ratio with FID detection.

33

Table 3-4. Average sensitivity gains in different MS detection modes. FS = full scan; EIC = extracted ion chromatogram; SIM = selected ion monitoring. %RSD at the 10 ppm level.

Name MS (FS), S/N MS (EIC), S/N MS (SIM), S/N %RSD

Diethyl ether 1,2 2,6 81 9,27

Acetaldehyde 0,2 4,1 10 18,9

ETBE 0,6 9,9 66 7,56

MTBE 0,6 3,1 53 6,84

Diisopropylether 0,7 6,5 70 10,1

Propanal 0,3 6,6 27 11,9

t-Amyl ether 1,1 4,4 64 4,60

Propyl ether 1,3 17 70 6,56

iso-Butanal 0,4 5,0 38 5,35

Butyraldehyde 0,8 4,4 23 2,31

Methanol 1,5 1,8 6,5 12,2

Acetone 0,8 18 100 7,21

Valeraldehyde 1,5 8,4 177 5,09

MEK 1,3 2,0 4,4 10,9

Ethanol 0,3 1,2 3,7 13,4

iso-Propanol 0,6 2,9 5,6 18,7

Propanol 0,5 1,6 5,5 12,4

Allyl alcohol 0,3 1,1 14 11,0

iso-Butanol 1,1 8,1 45 11,7

t-Butanol 0,9 2,1 49 9,34

n-Butanol 0,7 1,9 25 11,7

The results in Table 3-4 clearly illustrate the significant gains in sensitivity that can be reached when

using MS compared to FID. Minimal gain is 3,7 for ethanol. Unsurprisingly, sensitivity gains are

particularly significant when the MS was used in SIM mode. Straight full scan mode proved to be less

appropriate for target analysis, which is predominately due to the low molecular weight of the target

compounds, thus having to include highly interfering masses such as m/z 18 (water), 28 (nitrogen)

and 32 (oxygen) into the scan range. Most convenient, in this respect, is the combined full scan/SIM

mode, which is available on all major instrument brands, nowadays.

Afterwards, calibration curves were constructed for each of the oxygenates in the standard mixture

from 0,05 ppm to 5 ppm. Correlation coefficients were ≥ 0,995. Some typical SIM traces at the 0,10

ppm level are depicted in Figure 3-3.

Method repeatability was determined as well. Results at the 10 ppm level are included in Table 3-4

(six consecutive analyses).

34

Figure 3-3. SIM traces at 0,01 ppm. Upper: acetaldehyde, Middle: ethanol, Lower: Propyl ether.

35

4. Applications

4.1. Naphtha Feed

Naphtha is a complex mixture of hydrocarbons (C5-C12) in petroleum boiling between 30°C and

200°C. Oxygenates are routinely determined in these samples according to reference procedures

such as ASTM D7423 (4) because their cracking product cause problems in the downstream

separation processes (8). Naphthas are very complex and fully require the chromatographic

separation power of the Lowox column. A typical chromatogram of a naphtha sample in SIM mode is

depicted in Figure 3-4. Individual samples were introduced using the LSV of the GC/MS analyzer.

Insert shows the methanol trace (ion 29, 0.18 ppm); concentrations of acetaldehyde and TAME were

8.3 and 4.9 ppm, respectively.

When idle, the GC oven was kept at 200°C with the backflush activated. This was necessary to

prevent the accumulation of siloxane bleed from the pre-column.

Figure 3-4. Typical SIM trace of a naphtha feed.

36

5. Conclusions

A GC/MS analyzer is described that substantially expands the workable application range of a classic

catalyst contaminants analyzer. The use of mass spectrometry in FS/SIM mode permits identification

of unknown contaminants in combination with reliable quantification at trace and ultratrace

amounts.

6. References

1. Resconi, L., L. Cavallo, A. Fait, and F. Piemontesi. F. Chem. Rev., 100, 1253-1345 (2000). 2. Almatis AC Inc., Application for Selective Adsorbents in Polymer Production Processes,

Technical Bulletin USA/6040-R00/0504. 3. Graham, M.A. Selected Ethylene Feedstock Impurities: Survey Data. Ethylene Producers

Conference, Houston, TX, USA (1993). 4. Speight, J.G., Handbook of Petroleum Product Analysis, John Wiley & Sons (2002). 5. Biela, B., R. Moore, R. Benesch, B. Talbert, and T. Jacksier. Gulf Coast Conference, Galveston,

TX, USA (2003). 6. Thind, S.S., Petro Industry News, 1-2, June/July, (2003). 7. de Zeeuw, J. and J. Luong. Trends Anal. Chem., 594-607, 21 (2002) 8. Pyl, S.P., C.M. Schietekat, M.-F. Reyniers, R. Abhari, G.B. Marin, and K.M. Van Geem, Chem.

Eng. J., 178-187, 176-177 (2011).

37

Chapter 4: LowoxMS analysis for arsine and phosphine impurities in

polymer-grade propylene

1. Introduction

The goal of this chapter is to investigate the possibility of applying the LowoxMS system for the

detection and quantification of phosphine (PH3) and arsine (AsH3) diluted in a propylene matrix.

These impurities are well known catalyst poisoners with concentration specifications set at very low

limits, i.e. lower than 20 parts-per-billion (cfr. Chapter 2 - Table 2-1). Therefore it is important to find

a chemical analytical technique that can do this efficient and reliable. During this chapter the

capabilities of the LowoxMS for this application will be evaluated, as well as a GC-MS equipped with a

PoraBond QTM column.

2. Experimental

2.1. Standards

The standard mixtures for arsine and phosphine are both mixtures of 250 ppb of the respective

component dissolved in nitrogen. Both mixtures were provided by Air Liquide and have been used

without further purification. Dilutions were made in Tedlar® bags with nitrogen and propylene. The

Tedlar® bags used for these experiments were provided by Sigma Aldrich. The specifications are

listed in Table 4-1. The method of making the dilution was simply to fill the bag with the required

amount of sample, and afterwards add the diluents to make the desired dilution. An example of how

the dilutions using Tedlar® bags were made is given in Figure 4-1. one ml of the sample was injected

on the GC with a gastight syringe.

Table 4-1. Tedlar® bag specifications [1].

Material Tedlar®

Valve Push Lock Valve

Maximum volume 1 litre

Size 0.1778 m x 0.2286 m

The reliability of this procedure has been evaluated as many variables can influence the

concentration [2]. Therefore a check of the repeatability of the dilutions was done with SIFT-MS

because this is less time consuming. These results will be discussed in Chapter 5.

38

Figure 4-1. Applied set-up to make the dilutions of phosphine and arsine in Tedlar® bags.

2.2. LowoxMS

The reader is referred to Chapter 3 for an extensive description of the setup. The settings are also the

same as those used in Chapter 3 – Table 3-2, except for the oven temperature program which started

in this case at -20°C instead of 50°C in an attempt to create some retention for arsine and phosphine.

2.3. GC-MS – PoraBond QTM

Because of information found in literature it was decided also to test another analytical column to

separate arsine and phosphine from each other and from the matrix. Gras et al. did research on this

topic and found that the PoraBond QTM is ideally suited for this application. They also said that the

CP-Lowox column, which is implemented in the LowoxMS system isn’t suited for this purpose at all

[3].

Therefore a Gas Chromatographic system was equipped with this PoraBond QTM column. The latter

was then connected to a mass spectrometer which had the same tuning as the LowoxMS system, i.e.

higher sensitivity for the lower mass ions, in order to try to compare results. The dimensions of the

column were 50 m L x 0,32 mm I.D., 10 µm df. The inner diameter of this column is narrow enough to

ensure that the vacuum of the MS doesn’t protrude into the chromatographic system. This means

that there’s no need for a restriction column, as is the case with the LowoxMS system.

39

2.3.1. GC-MS settings

The settings used for the GC-MS equipped with the PoraBond QTM column are given in Table 4-2.

Table 4-2. Settings used for the GC-MS system equipped with the PoraBond QTM analytical column.

Injection 10µl

Carrier gas He, 2,80 ml/min

Analytical column PoraBond QTM (50m x 0,32mm)

Oven Temperature 80°C - isothermal

Detector ISQ-MS, 29-150amu (0,01989s)

ISQ-MS, 34amu (0,2s) ISQ-MS, 78amu (0,2s)

Note that the mass spectrometer uses a Full Scan/Single Ion Monitoring method. This method

consists of a full scan, followed by two SIM scans on the mass ions of arsine and phosphine in the

same scan cycle. This is because of the fact that the application of only a full scan method with a

scanning range of 29-250 amu wasn’t able to detect arsine and phosphine. However, when applying

SIM, the MS did prove to be able to detect these components. The FS/SIM method was then chosen

in function of later injections with the components diluted in propylene to be sure that the baseline

had recovered before injecting new samples.

3. Results and discussion

3.1. LowoxMS

3.1.1. Phosphine

LowoxMS experiments with arsine and phosphine in nitrogen were carried out in order to verify

whether it indeed wasn’t capable of detecting these components, as Gras et al. stated [3]. Even in a

nitrogen matrix, it proved impossible to detect phosphine because active phase of the CP-Lowox

analytical column doesn’t interact with the component. This causes the component to elute from the

column directly after the dead volume together with other impurities. Moreover, the molecular mass

of phosphine proved to be too low for EIC or SIM to detect the components. Therefore, the detection

of this component with LowoxMS was not possible.

40

3.1.2. Arsine

Arsine, as phosphine, proved to have virtually no retention with the CP-lowox analytical column.

Therefore, the scanning range of the MS was adapted to 50 – 80amu. The latter caused arsine to be

detected in full scan mode (Figure 4-2, RT: 2,27 min.), because the impurities that could interfere

(e.g. carbon dioxide and oxygen) were eliminated from being detected by using this scan range.

Figure 4-2. Detection of arsine with the LowoxMS (50ppb).

After having proved that detection of arsine is possible with the LowoxMS, a calibration curve was

constructed for arsine in a nitrogen matrix. Also the detection limits were estimated based on the

S/N ratios. The calibration curve obtained with the full scan method is given in Figure 4-3. The trend

line obtained by linear regression together with the correlation coefficient of the regression are also

given in this graph.

The fit of this linear trend line through the data points is indicated by the correlation coefficient,

which is 99,96%. This means that arsine can be detected very well, even in these low concentrations ,

i.e. low ppb level. These injection also prove to be very repeatable, indicated by the %RSD value and

the small error bars in Figure 4-3. These are more indications of the good capabilities of the detector

to detect arsine at low ppb levels.

41

Figure 4-3. Calibration curve of arsine in nitrogen obtained with the LowoxMS in full scan mode.

The detection limit can be estimated by extrapolating the signal-to-noise ratio (S/N) at the lowest

measured concentrations, in this case 5 ppb. A rule of thumb says that the detection limit

corresponds to a S/N of three. The data of the experiments are given in Table 4-3. Using this rule of

thumb, a detection limit of 845 parts-per-trillion (ppt) is obtained, which is much lower than the

specifications set by the industry, i.e. a concentration lower than 20 ppb. However, it has to be

stated that these data were obtained with the components diluted in nitrogen. A hydrocarbon matrix

could have a significant effect, but this couldn’t be tested given the short amount of time available

with the LowoxMS to perform these experiments.

Table 4-3. Data for the calibration curve of arsine in nitrogen with the LowoxMS in full scan mode.

Concentration (ppb) Average peak surface (-) Standard deviation (-) %RSD Average S/N (RMS)

5 453928 72125 15,89 17,75

20 3081242 162690 5,28 86,25

50 6448260 184640 2,86 187,50

125 16026504 582730, 3,64 423,50

250 31795516 1990867 6,26 715,25

y = 126659x + 161810 R² = 0,9996

0,00E+00

5,00E+06

1,00E+07

1,50E+07

2,00E+07

2,50E+07

3,00E+07

3,50E+07

4,00E+07

0 50 100 150 200 250 300

Pe

ak S

urf

ace

(-)

Concentration (ppb)

Calibration curve arsine in nitrogen - LowoxMS

42

3.2. GC-MS equipped with the PoraBond QTM column

Because of the fact that phosphine couldn’t be detected with the LowoxMS and that Gras et al.

showed that the PoraBond QTM analytical column is capable of giving sufficient retention for the

detection of arsine and phosphine in a hydrocarbon matrix, a GC-MS set-up with this analytical

column was also prepared and tested [3]. Arsine and phosphine were first injected in a nitrogen

matrix to determine whether this was indeed the case and to set-up calibration curves. Later, the

components were diluted in a propylene matrix.

For the detection of arsine and phosphine an isothermal, Full Scan/SIM (FS/SIM) method at 80°C was

applied. During each scan cycle a scan is performed over the full mass range, i.e. 29-250 amu,

followed by two SIM scans, 34 amu for phosphine and 78 amu for arsine. A chromatogram of this

FS/SIM method is given in Figure 4-4. The upper chromatogram is the one obtained with the full

scan, the middle one is the SIM scan for phosphine and the lower one is the SIM scan for arsine. Note

that performing EIC on the full scan chromatogram also didn’t help to detect arsine or phosphine. An

isothermal oven temperature was used because of the limited effect of the temperature on the

retention times of both components. This allowed to carry out more injections in a shorter time,

which was necessary given the short amount of time available to perform the experiments.

Figure 4-4. Chromatogram of 125 ppb arsine and 125 ppb phosphine in nitrogen with FS/SIM method. Upper full scan, middle SIM (34 amu), lower SIM (78 amu).

3.2.1. Arsine and phosphine calibration curves in nitrogen

Based on the resulting peak surfaces, calibration curves for both components were constructed.

These curves are given in Figure 4-5 and Figure 4-6, together with the error bars, the trend line

resulting from the linear regression and its correlation coefficient.

43

Figure 4-5. Calibration curve for arsine in nitrogen obtained with GC-MS with the Porabond QTM column in SIM mode.

Figure 4-6. Calibration curve for phosphine in nitrogen obtained with GC-MS with the PoraBond QTM column in SIM mode.

The correlation coefficients both are high, i.e. 99,95% and 99,97%, indicating the excellent

capabilities of this set-up to detect arsine and phosphine in very low concentrations, i.e. low ppb

level, with an excellent repeatability. This is indicated by the small error bars, which represent the

standard deviation of the measurements.

y = 2921,8x + 2950,5 R² = 0,9995

0,00E+00

5,00E+04

1,00E+05

1,50E+05

2,00E+05

2,50E+05

3,00E+05

3,50E+05

4,00E+05

4,50E+05

0 20 40 60 80 100 120 140

Pe

ak S

urf

ace

(-)

Concentration (ppb)

Calibration curve arsine - PoraBond QTM

y = 948,03x + 6174,8 R² = 0,9997

0,00E+00

2,00E+04

4,00E+04

6,00E+04

8,00E+04

1,00E+05

1,20E+05

1,40E+05

0 20 40 60 80 100 120 140

Pe

ak S

urf

ace

(-)

Concentration (ppb)

Calibration curve phosphine - PoraBond QTM

44

Again an estimation of the detection limits for both components is made by extrapolating the S/N

ratios. The rule of thumb, i.e. detection limit corresponds with a S/N equal to three, is also applied on

the data given in Table 4-4 for arsine and Table 4-5 for phosphine. These calculations result in a

detection limit of 1,364 ppb for arsine and 1,538 ppb for phosphine.

As with the calibration curves and detection limits obtained with the LowoxMS system, it has to be

stated that these are detection limits obtained with nitrogen diluted samples. The addition of a

hydrocarbon matrix could have a great effect on these results.

Table 4-4. Data for the calibration curve of arsine in nitrogen with the GC-MS with the PoraBond QTM column in SIM.

Concentration (ppb) Average peak surface (-) Standard deviation (-) %RSD S/N

5 13919 547 3,93 11,00

20 62724 4910 7,83 40,75

50 152990 5282 3,45 95,75

125 366524 29198 7,97 187,00

Table 4-5. Data for the calibration curve of phosphine in nitrogen with the GC-MS with the PoraBond QTM column in SIM.

Concentration (ppb) Average Peak Surface (-) Standard deviation (-) %RSD S/N

5 11880 643 5,41 9,75

20 24159 1638 6,78 19,00

50 53396 673 1,26 49,75

125 124867 3047 2,44 77,50

3.2.2. Arsine and phosphine diluted in propylene injections

After the calibration curves were constructed, injections of arsine and phosphine diluted in a

propylene matrix were injected on the GC-MS in order to get an idea of the influence of propylene on

the chromatograms. A dilution of 25 ppb in propylene was made in a Tedlar® bag. The resulting

chromatogram is given in Figure 4-7. Figure 4-7(B) shows a zoomed part of this chromatogram in

order to indicate the retention time of phosphine (5.42 min) and arsine (6,92 min). The peak

observed in the Full Scan chromatogram in Figure 4-7(B) is caused by an impurity present in the

sample.

45

Figure 4-7(A). Chromatogram of the injection of 25 ppb arsine and phosphine diluted in propylene. Upper: Full Scan, middle: SIM phosphine, lower: SIM arsine.

Figure 4-7(B). Zoomed chromatogram of the injection of 25 ppb arsine and phosphine diluted in propylene. Upper: Full Scan, middle: SIM phosphine, lower: SIM arsine.

These chromatograms clearly show that arsine and phosphine elute before propylene, which starts

eluting after 8,16 minutes. This means that propylene shouldn’t have an interference with arsine and

phosphine, resulting in the correct concentrations calculated from the calibration lines. The

chromatograms were followed until the baseline had completely recovered. The latter corresponds

to propylene that should be completely eluted of the analytical column, which allows to carry out a

new injection. This takes about 3,5 minutes. The data obtained from the chromatograms are given in

Table 4-6.

46

Table 4-6. Data obtained from the chromatograms of the injection of 25 ppb arsine and phosphine diluted in propylene.

arsine phosphine

injection 1 injection 2 injection 3 injection 1 injection 2 injection 3

Peak surface 83574 25919 25592 32445 13156 12948

Concentration (ppb) 27,59 7,86 7,75 27,71 7,36 7,14

S/N 85 32 35 51 34 36

The first injection shows that the assumption, that propylene doesn’t interfere with arsine and

phosphine in terms of concentration calculation using the calibration lines, makes sense. This is

because the concentrations calculated using the calibration lines approached the real concentration

very well. However, the following injections showed a consistent underestimation of the

concentration. Also a significant drop in S/N from the arsine and phosphine peaks was found. The

cause of this phenomenon is unclear, but the presumption is that propylene remains on the column

longer than expected. Although the baseline seems to be recovered, apparently a slight elevation

compared to the first injection remains, which has a big influence on the results, especially

considering the low concentrations which are used. A temperature program of the GC-oven could be

a solution for this phenomenon, but this couldn’t be tested due to the limited time available to carry

out these experiments.

4. Conclusions

The LowoxMS was able to detect and quantify arsine in nitrogen. A calibration curve for the low

concentrations, i.e. low ppb level, was constructed and the detection limits were estimated by

extrapolating the S/N values from the lowest concentration measured (5 ppb). A rule of thumb

stating that the detection limit corresponds to a S/N of 3. The detection limit with the LowoxMS was

estimated at 845 parts-per-trillion. Phosphine however, proved impossible to be detected with the

LowoxMS.

The GC-MS set-up, in contrast to the LowoxMS, was capable of detecting and quantifying arsine and

phosphine in the low ppb region. Calibration curves in nitrogen were again constructed and the

detection limits were estimated at 1,364 ppb for arsine and 1,538 ppb for phosphine using the rule of

thumb. This is slightly higher as the one obtained with the LowoxMS, but still well below the

demanded specifications, which are 20 ppb.

Finally a limited number of injections of 25 ppb arsine and phosphine diluted in propylene were

carried out. The result of the first injection showed good promise that propylene didn’t interfere with

the arsine and phosphine peak surfaces, because it elutes well after these components. However, on

47

the following injections, a significant loss in S/N was assessed. This could be due to the fact that

there still was some propylene left on the analytical column. A solution for this could be to install a

temperature program on the GC-oven, but since there was no time left to test this further

experiments are needed to clarify this issue.

5. Future Work

First of all, a confirmation of the presumption that a temperature program would help the detection

and quantification of arsine and phosphine in propylene should be carried out. A rise in temperature

to 250-300°C for a short period of time should help clearing the column of propylene more easily.

Worst case scenario would be the fact that after each injection the column should be heated to a

high temperature for a longer time or that it should be conditioned for a relatively long time in order

to completely remove the matrix from the column. Another solution could be to apply a multicolumn

set-up using a pre-column and the actual analytical column. The pre-column should be chosen in a

manner so that the hydrocarbon matrix elutes later than the target components. After the target

components are sent to the analytical column, the first column can be back flushed in order to

remove the matrix to a vent.

If necessary, a hybrid set-up of both GC’s could be constructed in order to be able to analyze a

sample containing hydrocarbons, oxygenates, arsine and phosphine. A train of thoughts on a possible

set-up is given in Figure 4-8.

This set-up consists of three columns. The first column is a nonpolar column which can be used to

prevent the heavy hydrocarbons, e.g. decane and heavier, from eluting on to the analytical columns,

CP-Lowox and PoraBond QTM. This means that after this first columns a backflush system should be

installed in order to remove these heavier hydrocarbons and send them to the vent.

Presumably arsine and phosphine will elute first from this nonpolar column, but this should be

verified. A valve should send this fraction onto the PoraBond QTM column in order to separate them

from each other. After arsine and phosphine are sent to the PoraBond QTM column, the effluent from

the pre-column, which will most likely contain only the oxygenates and lighter hydrocarbons, should

be sent to the CP-Lowox column. For a good separation, the GC-oven should have to be equipped

with a cryogenics system.

48

Figure 4-8. Suggestion for a possible hybrid GC-MS set-up based on the LowoxMS and PoraBond GC-MS.

Finally, in order to be able to use the mass spectrometer for both columns, the system should be

able to work with a technique called “stopped flow”. This means that only one analytical column

receives a diluens flow, while the other is put temporarily on hold. Judging on the results achieved

with both systems, the PoraBond QTM column should be sent to the MS first, while the temperature

is very low. Next, the temperature program for the separation of the oxygenates can be started and

the diluens can be sent through the CP-lowox column in order to send this fraction to the MS.

49

6. References

1. Sigma Aldrich, C. Tedlar Gas Sampling Bag. 2012 [cited 2012 1 May]; Available from: http://www.sigmaaldrich.com/catalog/product/supelco/24633?lang=en&region=BE.

2. Vercammen, J., Calibration in Gas analysis. White paper: p. 1-7. 3. Gras, R., J. Luong, M. Hawryluk, and M. Monagle, Analysis of part-per-billion level of arsine

and phosphine in light hydrocarbons by capillary flow technology and dielectric barrier discharge detector. Journal of Chromatography A, 2010. 1217(3): p. 348-352.

50

Chapter 5: SIFT-MS analysis of trace impurities in complex

hydrocarbon matrices

1. Introduction

In this chapter the experimental results which were obtained with the Selected Ion Flow Tube – Mass

Spectrometer, SIFT-MS, are discussed and compared with the results described in chapters 3 and 4.

Different trace impurities, such as arsine, phosphine, acetone and formaldehyde were analyzed in

several matrices. The analytes were first measured in nitrogen to provide a reference and

afterwards, they were measured in hydrocarbon matrices such as methane, ethane, ethylene and

propylene. The goal of the experiments was to evaluate whether it is possible to detect these

impurities in hydrocarbons with SIFT-MS. Firstly, calibration curves for each component in the

different matrices were recorded. Afterwards, it was verified whether it is possible to reconstruct the

signal obtained in a complex hydrocarbon matrix. Therefore, average signals from individual

experiments in pure hydrocarbons were applied to assess additivity.

2. Experimental

2.1. Standards

The standards used for the experiments with arsine (AsH3) and phosphine (PH3) are the same as used

in the GC-MS experiments with these impurities (cfr. Chapter 4). Dilutions are made using Tedlar®

bags.

The dilutions containing oxygenate impurities were obtained using a Micro-Processor Controlled

Calibration System MK5. This system is shown schematically in Figure 5-1 [1].

Figure 5-1. Schematic overview of the Micro Processor Calibration System MK5 [1].

51

The Micro Processor Calibration System uses a permeation tube chamber in which permeation tubes

of different standards are inserted. The latter are small containers, made of an inert polymeric

material sealed on each end, which are filled with a pure chemical compound in a two-phase

equilibrium between the gas and the liquid phase. Examples of some typical permeation tubes are

given in Figure 5-2 [1]. The tubes have a length ranging from a few millimetres up to 20 centimetres,

depending on the intended emission rates.

Figure 5-2. Examples of permation tubes [1].

The principle of permeation is given schematically in Figure 5-3. At a certain temperature, each tube

emits a known amount of the chemical component it contains. Since the permeation rate at a

reference temperature is known, the permeation rate at the oven temperature can be calculated

using equation 5.1 [2].

( ) ( ) ( ) (5.1)

With:

P0 Permeation rate at reference temperature T0 (ng/min)

P1 Permeation rate at operating temperature T1 (ng/min)

α Temperature coefficient (0,034 °C-1if P0 < 150 ng/min, else 0,03 °C-1)

52

When a known flow rate of the dilution gas passes along the permeation tube, the concentrations of

each analyte in that gas can be calculated very precisely.

Figure 5-3. Permeation tube dilution principle [1].

The tubes applied in this thesis are listed in Table 5-1 with their respective permeation rates at the

reference temperatures, T0. The tubes were provided by Interscience Belgium.

Table 5-1. Used permeation tubes in this thesis.

Component Permeation rate P0 (ng/min) Reference temperature T0 (°C)

Acetone 49,0 50

formaldehyde 25,0 50

Several gases were used to dilute the samples, i.e. nitrogen, methane, ethane, ethylene and

propylene. The latter have been chosen because they are the main constituents of a steam cracker

effluent. Nitrogen served as a reference.

2.2. Selected Ion Flow Tube – Mass spectrometer

The principle of the SIFT-MS is extensively explained in the literature survey (cfr. Chapter 2 – section

6.1.). Only a brief summary will be given below.

SIFT-MS uses microwaves to generate so-called precursor or reagent ions: H3O+, O2

+ and NO+. These

are sent through a first quadrupole in order to be able to the select right one. Inside the flow tube, a

reaction between a precursor ion and the target analyte occurs. The latter causes the target

component to be ionized. The flow tube effluent is then sent through a second quadrupole, which

selects the correct mass ions to be transmitted to the detector. Pictures of the Voice 200 instrument

and the Micro Processor Calibration System are given in Figure 5-4.

53

Figure 5-4. Photos of the SIFT-MS (Left), user display (Right bottom) and Micro Processor Calibration System (Right top).

2.2.1. Implementing a new method on the Voice 200

Setting up a new method on the Voice 200 can be achieved relatively fast thanks to the easy

software user interface. Some screenshots of the user interface are given in Figure 5-5. All reactions

that the instrument is able to measure are stored in a component library. Creating a new method

simply requires to search for the components that one wants to measure. After that, a table with the

scanned masses is generated for each of the reagent ions (H3O+, O2

+ and NO+). Next, it has to be

verified that no interferences occur. This implies that common masses for different target

components have to be removed from the table for the same reagent ion. The latter is displayed in

Figure 5-5(B) on the left hand side. This is necessary because it is not possible to predict the origin of

the detected signal. Please note that for different reagent ions, this is not an issue since the reagent

ions are never sent through the flow tube at the same time. In addition, the detector response is fast

enough (< 200 ms) for it to distinguish between signals originating from mass ions generated by

reactions with different precursor ions.

54

Figure 5-5(A). Screenshots of the SIFT-MS software. Left: general scan settings, Right: component library.

55

Figure 5-5(B). Screenshots of the SIFT-MS software. Left: Scanned mass table indicating a conflict, Right: final scanned mass table.

56

New components can be added easily to the library, provided some properties are known. In order to

have an idea whether a component can be measured by the SIFT-MS, three questions need to be

answered:

1. Is the target analyte volatile? If so, SIFT-MS should be able to handle the component.

2. Is the proton affinity higher than that of water (691 kJ/mol)? If so, the component should react

with H3O+.

3. Is the ionization energy lower than that of oxygen (12 eV)? If so, the component should react

with O2+.

NO+ is the least reactive precursor ion, so it is likely that when a component doesn’t react with the

other two precursor ions, it also wouldn’t react with NO+.

3. Results

3.1. Phosphine and arsine detection and quantification

The goal of these experiments was to verify whether SIFT-MS can detect arsine and phosphine in a

hydrocarbon matrix in the intended concentration range, i.e. low ppb level. As in Chapter 4,

standards were prepared in nitrogen and propylene. Dilutions were made using Tedlar® bags. An

estimation of the detection limit was achieved by injecting some blanks, and performing the

necessary calculations on these signals.

3.1.1. SIFT-MS method

For the detection of arsine and phosphine, both components had to be added to the SIFT-MS library.

Therefore the proton affinity and ionization energy of both components had to be known. These data

are given in Table 5-2 [3].

Table 5-2. Proton affinity and Ionization energy of arsine and phosphine [3].

Component Proton affinity (kJ/mol) Ionization energy (eV)

arsine 747,9 9,89

phosphine 785,0 9,96

57

Based on these data, it can be concluded that the SIFT-MS can indeed detect both components. The

following reactions occur:

(5.2)

(5.3)

(5.4)

(5.5)

Only the masses 35 and 79 are monitored in the reaction with H3O+ for these experiments since these

provided the best results. Methods are compiled using the graphical interface of the software

delivered with the Voice 200 as explained in Chapter 5 – section 2.2.1.

3.1.2. Calibration curves in nitrogen

Calibration curves for both components were made in nitrogen. The detection limits for both

components were calculated based on results obtained by injecting pure nitrogen without adding of

the analytes. The detection limit can be calculated as given in equation 5.6 [4].

(5.6)

With:

LoD Limit of Detection (counts/s)

xbl The average signal of the blanks (counts/s)

sbl The standard deviation of the blanks (counts/s)

Based on the trend line obtained by performing linear regression on the data used to construct the

calibration curves, the corresponding concentration can be calculated. The calibration curves for

phosphine and arsine are given in Figure 5-6 and Figure 5-7, respectively.

Both calibration curves illustrate that SIFT-MS can detect and quantify arsine and phosphine in the

required concentration range, i.e. low ppb level. The error bars show that the standard deviation on

the data is small, which is another indication of the fact that SIFT-MS can detect arsine and

phosphine very well in the low concentration ranges. The results from the LOD calculations are given

in Table 5-3.

58

Figure 5-6. Calibration curve for phosphine in nitrogen obtained with the SIFT-MS by following the reaction with H3O

+.

Figure 5-7. Calibration curve for arsine in nitrogen obtained with the SIFT-MS by following the reaction with H3O

+.

Table 5-3. Calculated detection limits and quantification limits for arsine and phosphine in nitrogen.

Component xbl (counts/s) sbl (counts/s) LoD (counts/s) LoD (ppb) LoQ (= LoD x 3,3) (ppb)

Arsine 83,61 12,05 119,75 12,58 59,19

Phosphine 12,29 3,72 23,44 5,61 14,82

Notice that the limit of quantification (LoQ) was calculated based on the limit of detection (LoD) in

counts/s and not in ppb.

y = 5,8528x - 9,378 R² = 0,9992

0

200

400

600

800

1000

1200

1400

1600

0 50 100 150 200 250 300

Sign

al (

cou

nts

/s)

Concentration (ppb)

Calibration curve phosphine in N2

y = 5,9092x + 45,406 R² = 0,993

0

200

400

600

800

1000

1200

1400

1600

1800

0 50 100 150 200 250 300

Sign

al (

cou

nts

/s)

Concentration (ppb)

Calibration curve arsine in N2

59

Despite of the fact that these detection limits are higher for both components than those obtained

with the LowoxMS and GC-MS (cfr. Chapter 4), they’re well below the specifications demanded by

the industry, i.e. 20 ppb. Note however, that these results are obtained in nitrogen. Hydrocarbon

matrices can have a significant effect on the signal intensity and sensitivity.

3.1.3. Repeatability of the dilution method

As stated before, the accuracy of dilution with Tedlar® bags was verified with SIFT-MS, because a

single measurement only takes one minute. Three bags with a dilution of 20 ppb were prepared and

injected onto the SIFT-MS. The results of the analysis are presented in Table 5-4.

Table 5-4. Results of repeatability check of dilution method with Tedlar® bags.

Sample name bag 1 bag 2 bag 3

Concentration 20 ppb 20 ppb 20 ppb

Analysis 1 (counts/s) 104,00 94,15 128,31

Analysis 2 (counts/s) 100,62 115,23 126,31

Analysis 3 (counts/s) 121,41 108,15 120,77

Analysis 4 (counts/s) 111,38 119,53 124,38

Analysis 5 (counts/s) 109,69 109,85 115,69

Analysis 6 (counts/s) 115,94 121,85 108,31

Analysis 7 (counts/s) 104,68 109,54 109,23

Average 109,67 111,19 119,00

Standard deviation 7,29 9,16 8,09

%RSD 6,65 8,24 6,80

Calculated from calibration curve (ppb) 20,34 20,60 21,93

These results illustrate that the repeatability of the standard preparation is reasonable. Therefore it

is safe to trust the data that were obtained with this method in Chapter 4.

3.1.4. Calibration curve in polymer-grade propylene

After constructing calibration curves in nitrogen, it was attempted to establish calibration curves in a

propylene matrix for both components. However, when performing these measurements, it became

clear that SIFT-MS has difficulties handling this matrix. After each measurement an error message

appeared, stating that the remaining signals of the reagent ions are too low. This is merely a warning

indicating that the concentrations calculated by SIFT-MS are positively biased. The reason for this

error is that the matrix consumes most of the precursor ions. The results presented in this thesis, are

solely based on the raw signal (given in counts/s), not on calculated concentrations.

60

These measurements did not produce useful data for phosphine. The resulting signals were

extremely low, i.e. around 1 count/s for each concentration. This implies that SIFT-MS is not capable

of measuring phosphine in a polymer-grade propylene matrix. The resulting graph in which the

attempted calibration curve construction is displayed in Figure 5-8. Note that the error bars are big,

proving that these results aren’t repeatable at all. This could be due to the very low resulting raw

signal.

Figure 5-8. Graph of the attempted calibration curve construction for phosphine diluted in propylene

However, the SIFT-MS was capable of detecting and quantifying arsine. The calibration curve is given

in Figure 5-9. Note that the signal has dropped by approximately a factor of 7 compared to the curve

obtained in nitrogen. Also, the slope of the curve has decreased by a factor of 10. This is due to the

matrix effect of propylene, which consumes almost all of the available precursor ions.

Figure 5-9. Calibration curve for arsine in propylene obtained with the SIFT-MS by following the reaction with H3O

+.

0,00

0,50

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1,50

2,00

2,50

0 10 20 30 40 50 60

Sign

al (

cou

nts

/s)

Concentration (ppb)

Attempted calibration curve phosphine in propylene

y = 0,5246x + 12,771 R² = 0,9713

0,00

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10,00

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al (

cou

nts

/s)

Concentration (ppb)

Calibration curve arsine in propylene

61

The results shown in Figure 5-9 are remarkably well, considering that no pre-separation of the

analyte from the matrix occurs.

3.2. Oxygenates in hydrocarbon matrices

In this section, the results of SIFT-MS on analysis of oxygenates diluted in several hydrocarbon

matrices are discussed. The considered oxygenates are acetone and formaldehyde. Formaldehyde is

particularly interesting because there are only a few methods available nowadays for detecting

formaldehyde in hydrocarbon matrices and these are generally not very sensitive.

There are different possibilities for the detection of formaldehyde which have been used in previous

researches. Most of them are used in analyses on non-hydrocarbon samples, such as air and water.

An example of such a method, also used for the detection of formaldehyde in hydrocarbon matrices,

is the one prescribed by the American Society for Testing and Materials, ASTM. The ASTM D 5197

test method is suitable for analyzing formaldehyde and other carbonyl compounds in air. This is done

by exposing a sorbent tube, which is silica gel with ultra-low background coated with 2,4-

dinitrophenylhydrazine (DNPH), to the air one wants to analyze. The latter has a high selectivity for

carbonyl components at room temperature, which makes it an ideal sorbent for this application. This

is done by sucking air through the tube using a pump at a rate of 500 – 1200 ml/min during a period

of time ranging from 5 minutes up to 24 hours. The sorbent is later analyzed by performing high-

performance liquid chromatography – ultraviolet detector (HPLC-UV). The formaldehyde is desorbed

by sending a acetonitrile solution through the sorbent tube. The detector operates at 254 nm or near

360 nm. The detection limit of such a method lies around 1,8 ng formaldehyde [5, 6].

Zhang et al. did some analyses on experiments in which the direct conversion of methane to

formaldehyde was carried out. The analysis was done via micro Gas Chromatography, µGC. However,

as this method could not quantify formaldehyde and methanol in a reproductive manner, a solution

had to be found. For formaldehyde he used the method described above using the sorbent tubes

followed by HPLC-UV measurements. The %RSD for this method was estimated at about 7%, proving

a good repeatability for this method [7].

Baldwin et al. also did investigations on this same chemical process. For the analysis of formaldehyde

however, a GC-FID set-up was used. The analytical column applied for the separation of the

components was a 2 m Porapak QS column. Also a temperature program had to be applied. The GC

was first calibrated for the measurement of formaldehyde by sending different samples at different

temperatures through the GC [8].

62

3.2.1. Acetone

The acetone experiments consisted of three parts. First, the repeatability of the calibration curves in

propylene was verified. Propylene was chosen for these analyses because it was proven that

propylene is a challenging matrix for SIFT-MS (cfr. Chapter 5 – section 3.1.4.). Therefore, calibration

curves were constructed on three different days. Secondly, acetone was diluted in several

hydrocarbon matrices and compared to the results obtained with a nitrogen reference. The matrices

were methane, ethane, ethylene and propylene. Finally, acetone was diluted in a gaseous C2-

calibration mixture used to calibrate gas chromatographs in the LCT lab. The goal of these

experiments was to simulate a more genuine steam cracker effluent. The calibration curve was

compared to those obtained in the pure hydrocarbons. It was verified whether it was possible to

predict these results using the individual calibration curves in the pure matrices. Note that only the

main components in the calibration mixture were considered, and hence, some differences are

expected.

The results presented in the following sections were obtained using mass ion 77, which is produced

with H3O+. As opposed to the reactions followed with arsine and phosphine, in which a proton

transfer occurs, here H3O+ associates with acetone.

3.2.1.1. Repeatability of propylene calibration curves

Figure 5-10 shows the calibration curves of acetone in propylene obtained on three different days.

Given on the graph are the calibration curves with their respective correlation coefficient resulting

from the linear regression done on the data points. Also the error bars are given to indicate the

repeatability of each measurement.

Considering the difficulties of SIFT-MS to cope with propylene as a matrix, the results of this

repeatability check are acceptable. The data, on which Figure 5-9 is based, are given in Table 5-5.

63

Figure 5-10. Repeatability check of calibration curves for acetone diluted in a propylene matrix.

y = 0,4692x + 79,578 R² = 0,8999

y = 0,6605x + 68,942 R² = 0,9543

y = 0,2958x + 57,606 R² = 0,9363

40,00

50,00

60,00

70,00

80,00

90,00

100,00

110,00

120,00

0 10 20 30 40 50 60 70

Sign

al (

cou

nts

/s)

Concentration (ppb)

Propylene calibration curves repeatability (m/z = 77)

Day 1

Day 2

Day 3

64

Table 5-5. Calibration curve data of acetone diluted in propylene with mass ion 77+.

Day 1 calibration curve

Concentration (ppb) Signal (counts/s) Standard deviation (counts/s) %RSD

5 83,65 7,40 8,85

10 80,75 8,79 10,89

20 89,66 5,01 5,58

40 98,39 8,18 8,31

50 108,33 2,52 2,33

60 103,51 8,99 8,68

Day 2 calibration curve

Concentration (ppb) Signal (counts/s) Standard deviation (counts/s) %RSD

5 73,89 10,09 13,66

10 75,53 9,05 11,99

20 77,40 7,31 9,44

40 99,95 10,94 10,95

50 102,85 8,46 8,23

60 106,23 9,40 8,85

Day 3 calibration curve

Concentration (ppb) Signal (counts/s) Standard deviation (counts/s) %RSD

5 58,10 5,44 9,37

10 61,05 7,63 12,50

20 65,52 10,99 16,77

40 66,95 7,92 11,83

60 76,35 15,51 20,31

The relative standard deviation (%RSD) is an indicator for the repeatability of the measurements. As

can be seen in Table 5-5, it is low, maximum 20,31%, implying that these measurements are

considered repeatable, especially considering the propylene matrix. However, the calibration lines

obtained on different days lie reasonably far apart. A measurement done on the third day of about

60 ppb, would have resulted in a concentration of about 5 ppb using the calibration curve from the

second day. Therefore, a daily calibration check of the SIFT-MS is advisable. In the following sections,

an average calibration curve from those in Figure 5-10 is given.

3.2.1.2. Matrix influence

Figure 5-11 shows the results of analyses of acetone diluted in different matrices. Nitrogen can be

considered to be a reference for the other matrices. These matrices are methane, ethane, ethylene

and propylene.

65

Figure 5-11. Matrix influence on the SIFT-MS signal for acetone analysis.

R² = 0,9928

R² = 0,9682

R² = 0,9868

R² = 0,9919

R² = 0,9814

0,00

200,00

400,00

600,00

800,00

1000,00

1200,00

0 10 20 30 40 50 60 70 80

Sign

al (

cou

nts

/s)

Concentration (ppb)

Matrix influence on acetone signal (m/z = 77)

nitrogen

methane

ethane

ethylene

propylene

66

As can be seen from this figure, the matrix has a significant effect on the signal which is obtained

with the SIFT-MS. From the data, it could be seen that the SIFT-MS has difficulties, particularly with

propylene. This could confirm the suspicion stated in Chapter 5 – section 3.1.4., stating that the

double bond in this matrix makes it more easy to capture reagent ions.

The correlation coefficients obtained by linear regression are given in Table 5-6. These show that the

trend lines have a good fit for the obtained data points. The latter means that acetone can be

detected in the required concentration range very well in all of these matrices. Also the error bars

are very small, indicating an excellent repeatability of the measurements.

Table 5-6. Correlation coefficients obtained by linear regression on the data obtained with the different matrices.

Matrix correlation coefficient (%)

nitrogen 99,28

methane 96,82

ethane 98,68

ethylene 99,19

propylene 98,14

3.2.1.3. Matrix additivity check

After determining that SIFT-MS can handle the various matrices rather well, acetone was diluted in a

GC calibration mixture used to calibrate gas chromatographs for components up to ethane. This is

done in order to try to simulate a stream cracker effluent. The composition of the mixture is given in

Table 5-7. After the data acquisition, it was validated whether these results could be recreated by

weighted averaging of the individual calibration curves in Figure 5-11. The results are shown in Figure

5-12, which is the same as Figure 5-11, with the calibration curve obtained in the C2- calibration

mixture and the additivity check added.

Table 5-7. Composition of the C2- calibration mixture.

Component Content (vol%)

H2 18,3

CO2 3,5

CO 5,9

N2 12,6

CH4 25,3

C2H2 1,4

C2H6 3,3

C2H4 29,7

67

Figure 5-12. C2 calibration mixture results and the additivity check results based on the results obtained with the different matrices.

R² = 0,9302

R² = 0,9981

0,00

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1000,00

1200,00

0 10 20 30 40 50 60 70 80

Sign

al (

cou

nts

/s)

Concentration (ppb)

Additivity check on acetone signal ( m/z = 77)

nitrogen

methane

ethane

ethylene

C2 calibration mixture

Additivity Check

68

It has to be noticed that not all components in the calibration mixture were analyzed as a pure

matrix. Only about 70% of all information necessary for a complete reconstruction was available, i.e.

the nitrogen, methane, ethane and ethylene results. Figure 5-12 shows that, when performing this

additivity experiment, the net result evolves towards the actual results. However, given the fact that

already 70% of the information has been used, it is obvious that there will never be a complete

match. The reconstructed curve shows a signal nearly twice the one from the original calibration

curve.

Further investigation should point out whether this result is consistent for other components.

However, if it would be possible to recreate the results, SIFT-MS could be used to check the

composition of the steam cracker effluent. This would mean that SIFT-MS could give an indication of

early problems, making it possible for the operator to check this out in an early stage, without losing

to much time or money.

3.2.2. Formaldehyde

Formaldehyde is a component which is very hard to detect and quantify with the existing chemical

analytical methods. Therefore, it is verified whether SIFT-MS can detect this component in

hydrocarbon matrices, and more importantly, whether it is capable of quantifying the concentration

in the low ppb range. The results given in the following sections are those obtained by following mass

ion 67. This is the reaction of formaldehyde with (H3O+.H2O), where the latter associates to

formaldehyde forming H2CO.(H2O)2.H+.

3.2.2.1. Matrix influence

The matrix influence on the detection signal of the SIFT-MS is given in Figure 5-13 for nitrogen,

methane and propylene. To make the results more clear, the calibration curves obtained with

formaldehyde diluted in propylene are given on the secondary axis, on the right-hand-side of the

graph.

This graph displays first of all that the repeatability of the calibration curve for formaldehyde in

propylene is very good, as was the case for acetone (cfr. Chapter 5 – section 3.2.1.1.). The relatively

small error bars show an excellent repeatability of the measuring points, regardless of how difficult

the detection of formaldehyde might be. However, it shows again that a daily calibration check could

prove to be necessary.

69

The correlation coefficients obtained by linear regression on the data are given in Table 5-8. These

illustrate that there is an excellent fit between the trend line and the actual data. This is an indication

of the excellent capabilities of SIFT-MS for detecting formaldehyde in different hydrocarbon matrices

in the required concentration range.

Table 5-8. Correlation coefficients obtained by linear regression on the data obtained for formaldehyde in different matrices.

Matrix Correlation coefficient (%)

nitrogen 91,05

methane 99,26

propylene (Day 1) 81,72

propylene (Day 2) 80,66

As with acetone, this graph also shows that the matrix can have a huge influence on the obtained

signal with SIFT-MS. The order of magnitude of the signals differ about a factor 10 between nitrogen

and methane on the one hand and propylene on the other hand.

3.2.2.1. Calibration mixture results

Formaldehyde was diluted in the same calibration mixture, which was also used to check the

additivity of the calibration curves for acetone. The latter can’t be performed here, as there are too

little data known. These results are given in Figure 5-14. Notice that only the calibration mixture

results are given on the secondary axis, to make the results more clear.

This curve illustrates a good linearity for concentrations above 20 ppb. At lower concentrations the

curve is deflected. This is a promising result because, as stated before, formaldehyde is very hard to

detect with the current range of analytical methods, especially in this concentration range. The error

bars, although they appear large, are small compared to the signal value. The %RSD doesn’t exceed

1,5%, which is an excellent result. Remarkable about these results is the fact that the calibration

mixture causes much higher signals than the pure matrices. The cause of this is difficult to identify as

there are a lot of variables that could play a role. Further investigation on this topic is necessary in

order to discover the cause.

70

Figure 5-13. Matrix influence on the SIFT-MS signal for formaldehyde.

0,00

100,00

200,00

300,00

400,00

500,00

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800,00

0,00

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45,00

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sign

al (

cou

nts

/s)

Concentration (ppb)

Matrix influence on formaldehyde signal (67)

N2

methane

propylene (day 1)

propylene (day 2)

71

Figure 5-14. Calibration mixture results obtained with SIFT-MS compared to the pure matrix results.

4. Conclusions

It was found that the SIFT-MS can detect both arsine and phosphine in nitrogen with a calculated

detection limit of 5,61 ppb for phosphine and 12,58 ppb for arsine. This is slightly higher than those

obtained with the GC-MS equipped with the PoraBond QTM analytical column (1,538 ppb for

phosphine and 1,364 ppb for arsine), but it is still well below the required specification set by the

industry. When diluted in propylene however, the SIFT-MS wasn’t capable of detecting phosphine.

This could be due to its molecular mass, which, at 34 g/mole, is very low. For arsine, the SIFT-MS is

capable of detecting it. This can be concluded from the reasonable fit of the trend line obtained by

linear regression on the experimental dataset.

The SIFT-MS could provide a quick alternative for the detection of arsine in polymer-grade propylene,

although be it less sensitive than the GC-MS method discussed in Chapter 4. For phosphine, the SIFT-

MS doesn’t provide an alternative.

15000,00

16000,00

17000,00

18000,00

19000,00

20000,00

0,00

100,00

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300,00

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sign

al (

cou

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/s)

Concentration (ppb)

C2- Calibration mixture results (67)

N2

methane

propylene (Day 1)

propylene (Day 2)

C2 calibration mixture

72

The repeatability check of the calibration curve in propylene illustrated, despite given the fact that

the SIFT-MS has difficulties on handling the propylene matrix, a very reasonable result. However a

daily calibration check of the instrument seems necessary. The difficulties with the propylene matrix

were probably due to the fact that there is a double bond present in the molecule. This will cause

propylene to take away most of the reagent ions, leaving lower amounts for the analytes to react

with.

The matrix influence on the signal obtained in different hydrocarbon matrices, i.e. methane, ethane,

ethylene and propylene, is significant. However, the fit of the trend lines obtained by linear

regression for all matrices was very good, i.e. the lowest correlation coefficient was obtained for

methane (96,82%). This means that acetone can be detected and quantified in all the matrices very

well in the intended concentration range.

The reconstruction of the C2- calibration mixture result by averaging the signal obtained in the

different individual matrices showed good promise. However, it is obvious that a perfect match

won’t occur with more information available. Further investigation is needed.

The second oxygenate to be analyzed in hydrocarbon matrices was formaldehyde. This because the

current state-of-the-art techniques all have difficulties detecting and quantifying formaldehyde in

these very low concentrations or are very complicated. The results obtained in different matrices, i.e.

nitrogen, methane and propylene, showed that it is indeed possible to detect formaldehyde in those

matrices. This could mean that SIFT-MS can be used for a very fast and accurate detection of the

oxygenate. Formaldehyde was also analyzed while being diluted in the calibration mixture. Those

results showed that again it was possible to detect and quantify formaldehyde up to very low

concentrations.

A general conclusion from this chapter is that the SIFT-MS could be used as a tool to help in the

detection and quantification of certain impurities. For example, it could be used as an early

indication of problems arising in a steam cracker unit. It has to be emphasized that the SIFT-MS

cannot replace the current gas chromatographic techniques. It can be used as an on-line tool to

provide an early indication of problems arising in an attempt to resolve them quickly without losing

too much time or money.

73

5. Future work

First of all, as this was a limited research, more oxygenates, especially alcohols, should be checked on

their possibility to be detected in the hydrocarbon matrices. Some nitrogen containing components,

e.g. amines and other, should also be checked as this are also well known catalyst poisons.

The additivity of the detector signal should be checked for acetone and other components to verify

whether the hypothesis, stated in this chapter, is correct. In order to do this, all components present

in the complex matrix should be checked as a pure matrix. Next the component should be measured

in this complex matrix and it should be verified whether the additivity hypothesis holds up. Multiple

analyte components should be done as well to make sure that acetone wasn’t just a coincidence.

A suggestion could be to somehow remove the hydrocarbon matrix before the effluent is sent to the

instrument. This could be achieved by putting a heart-cut GC in front of the Syft Voice 200 in order to

have a separation between the analyte components and the hydrocarbon matrix. This has already

been tested briefly at Interscience, with whom is collaborated during this master dissertation. The

results showed good promise for this technique [9]. But one has to ask what the benefit is of using

this kind of system compared to a normal GC-MS system such as the LowoxMS? The biggest benefit

of the SIFT-MS, its ability to analyze a complex sample in a very short time, is lost because of this. So

this set-up would actually be a GC-MS with a more expensive mass spectrometer than the normal

single quad MS of TOF-MS.

Another, more elegant solution to this problem could be to apply Nafion tubes. These are tubes used

to remove water from gaseous streams. This is achieved because the material, which is a copolymer

of tetrafluoroethylene (Teflon®) and perfluoro-3,6-dioxa-4-methyl-7-octene-sulfonic acid, is highly

selective to letting water permeate through the membrane. This process is driven by a humidity

gradient existing between the wet gaseous effluent flowing through the tube and the dry purge gas,

e.g. nitrogen, flowing around the tube at a flow rate 2 to 3 times that of the sample flow rate [10,

11]. The principle of these Nafion dryers is shown schematically in Figure 5-15 [11].

74

Figure 5-15. Schematic overview of nafion tube principle [11].

The application of these tubes in the context of this master dissertation becomes clear when one

knows that, along with the water, other components such as acetone, methanol and tetrahydrofuran

also permeate through the membrane. As the SIFT-MS can handle water, be it though in gaseous

form, the stream containing the water and the analytes could be analyzed separately, without the

presence of the complex hydrocarbon matrix. This is provided the humid stream is heated to make

sure that everything is in its gaseous form.

It will have to be verified for each trace impurity individually if the use of Nafion tubes results in an

improvement.

75

6. References

1. Vercammen, J., Calibration in Gas analysis. White paper: p. 1-7. 2. VICI Metronics, I. Generating Calibration Gas Standards with Dynacal Permeation Devices:

Technical note 1001. 2012; 1-5]. Available from: http://www.gasdetection.com/TECH/tn1001.pdf.

3. Institute of Standards and Technology, N. NIST Chemistry Webbook. 2011 [cited 2011 15 November]; Available from: http://webbook.nist.gov/.

4. Thomsen, V., D. Schatzlein, and D. Mercuro, Limits of Detection in Spectroscopy. Spectroscopy, 2003. 18(12): p. 112-114.

5. ASTM. Formaldehyde - ASTM D 5197. Chemical Fact File 2011 [cited 2012 23 May]; Available from: http://www.skcinc.com/cff/1442.pdf.

6. Pockard, A.D. and E.R. Clark, The determination of traces of formaldehyde. Talanta, 1984. 31(10, Part 1): p. 763-771.

7. Zhang, J., V. Burklé-Vitzthum, P.M. Marquaire, G. Wild, and J.M. Commenge, Direct conversion of methane in formaldehyde at very short residence time. Chemical Engineering Science, 2011. 66(24): p. 6331-6340.

8. Baldwin, T.R., R. Burch, G.D. Squire, and S.C. Tsang, Influence of homogeneous gas phase reactions in the partial oxidation of methane to methanol and formaldehyde in the presence of oxide catalysts. Applied Catalysis, 1991. 74(1): p. 137-152.

9. Vercammen, J., Ultratrace catalyst contaminant analysis using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS). 2009, Interscience: Louvain-la-Neuve. p. 1-17.

10. Perma Pure, L. Drying technology: Microporous vs Nafion. 2012 [cited 2012 15 May]; Available from: http://www.permapure.com/tech-notes/key-concepts/drying-technology-microporous-vs-nafion/.

11. Southeastern Automation, I. Perma Pure Dryers. 2012 [cited 2012 15 May]; Available from: http://www.southeastern-automation.com/Files/Perma%20Pure/dryers.html.

76

Chapter 6: Conclusions and future work

The goal of this master thesis was to do a research on whether SIFT-MS could provide a quick

alternative for the current state-of-the-art in the detection of catalyst poisoners, such as arsine,

phosphine, oxygenates and other components. These components can adversely affect the

characteristics of catalysts used in processes such as the polymerization of propylene to

polypropylene. Therefore, the specifications set on the presence of such catalyst poisoners is very

strict, ranging in the low parts-per-billion level, e.g. the specifications for arsine and phosphine are

usually set at a maximum concentration of 20 ppb. Moreover, the industry demands analyzing

techniques which can detect and quantify these components very quick, preferable online, in these

low concentration ranges. SIFT-MS has the potential to provide for a very quick alternative for this. A

preliminary study on this possible application of SIFT-MS is done during this work and a comparison

is made with the present state-of-the-art.

The results of the experiments on the separation and detection of various oxygenates in n-hexane

using the LowoxMS showed a very good separation once cryogenic cooling in the oven was applied.

The detector, a mass spectrometer, was also compared to a more traditional detector, a flame

ionization detector. The results obtained with the MS proved to have a significant increase in

sensitivity compared to the FID. Especially when the MS was applied in Extraction Ion

Chromatography mode, EIC, or Single Ion Monitoring mode, SIM, the sensitivity gain was

considerably. EIC proved to be the most interesting operation mode of the MS because it essentially

is a hybrid method between Full Scan mode and SIM. EIC benefits partially from the sensitivity gain

by extracting the characteristic mass ions from the Full Scan signal. But, unlike SIM, EIC maintains the

advantage of the Full Scan mode, i.e. the capability of identifying components based on their mass

spectrum. The repeatability of the system on the detection of these oxygenates proved to be very

good, with a maximum Relative Standard Deviation of 18,7% for iso-propanol. Finally an application

check of the LowoxMS on a naphtha feed was successful.

The results of the experiments on the detection of arsine and phosphine with the LowoxMS

illustrated that the system was unable to detect phosphine. This could be due to the low molecular

mass of phosphine together with the fact that the CP-Lowox analytical column doesn’t interact with

the molecule. Despite of arsine also not interacting with the active phase of the CP-Lowox column, it

could be detected. This is most likely because of the higher molecular weight of arsine, 78 g/mole.

77

The detection limit was estimated based on a rule thumb stating that the detection limit of a

component corresponds to a signal-to-noise ratio, S/N, of three. This gave an estimation of the

detection limit for arsine using the LowoxMS of 845 parts-per-trillion, ppt.

The GC-MS system equipped with the PoraBond QTM, contrary to the LowoxMS, was capable of

detecting both arsine and phosphine. An isothermal FS/SIM method was applied to analyze these

samples. Calibration curves for both components in nitrogen were constructed, and using the rule of

thumb, the detection limits were estimated at 1,364 ppb for arsine and 1,538 for phosphine, which is

well below the specifications set by the industry. Experiments done with arsine and phosphine

diluted in propylene using this set-up showed good promise after the first injection, but a significant

underestimation was assessed on the subsequent injections. This is most likely due to the fact that

propylene remained on the column but this should be easily resolved by programming a temperature

program on the GC oven, but there was no time left to check this.

A suggestion risen from these experiments is to make a hybrid GC-MS method for the analysis of

more complex mixtures containing both oxygenates and arsine and phosphine, if found necessary. A

possible set-up is given in this work. The key aspects of this system are the abbility to apply

cryogenics, as this created a better separation of the oxygenates. Also a technique called stopped

flow, explained in the text, should be applied in order to be able to send everything to the MS.

The experiments on the detection of arsine and phosphine with the GC-MS systems were compared

to experiments performed with SIFT-MS. Calibration curves for both components were constructed

and the detection limits were calculated based on blank samples. The detection limits were 5,61 ppb

for phosphine and 12,58 ppb for arsine in nitrogen. Both results are still well below the industry

specifications, but are higher compared to those obtained with the GC-MS systems. When diluted in

propylene SIFT-MS only could detect and quantify arsine. This was concluded based on the achieved

calibration lines for both components in polymer-grade propylene. SIFT-MS doesn’t provide an

alternative for the GC-MS set-ups in the detection of both components.

Finally, acetone and formaldehyde were analyzed diluted in several pure hydrocarbon matrices.

Formaldehyde was analyzed in nitrogen, methane and propylene. SIFT-MS showed excellent

capabilities in the detection of this component. Even in a more complex mixture, i.e. a C2- calibration

mixture for gas chromatographs, formaldehyde was easily detected down to 20 ppb.

Acetone was analyzed in nitrogen, methane, ethane, ethylene and propylene. In all matrices

calibration curves were constructed and these were, considering the difficulties SIFT-MS had with the

hydrocarbon matrices, very good. The influence of each matrix on the signal obtained for acetone

however, was significant. Acetone was also analyzed in the C2- calibration mixture, in order to try to

simulate a steam cracker effluent. The obtained results were then attempted to be recreated based

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on the results from the pure matrix experiments. The results showed that it was obvious that the

reconstruction didn’t add up to the real results. Further investigation on this topic is necessary.

SIFT-MS could provide an alternative for the current state-of-the-art detection methods of

formaldehyde in hydrocarbon matrices. All calibration curves of the oxygenates were good,

illustrating the excellent capabilities of SIFT-MS of detecting these in hydrocarbon matrices. The

additivity check however needs some further investigation to verify whether or not this is possible.

Suggestions made for the SIFT-MS are first of all to continue the work in order to verify the obtained

results. If necessary some possible alternatives are suggested in the form of Nafion tubes or the

implementation of a heart-cut GC prior to the Voice 200. However, both have possible

disadvantages, which need to be investigated.

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ANNEX A – Overview of performed experiments

Experiment Lab journal pages Date Calibratie PH3 SIFT-MS 1-2 30/11/2011

Calibratie AsH3 SIFT-MS 3-4 09/12/2011

Hercalibratie PH3 SIFT-MS 5 16/12/2011

Biodiesel Cracking “BIORO” (1) 8 02/03/2012

Biodiesel Cracking “BIORO” (2) 9 05/03/2012

LowoxMS: AsH3 en PH3 (1) 10-11 08/03/2012

LowoxMS: AsH3 en PH3 (2) 12-13 09/03/2012

LowoxMS: Calibratie AsH3 14 15/03/2012

PoraBond QTM (1) 15 29/03/2012

PoraBond QTM (2) 17 30/03/2012

SIFT-MS in Gent – DAG 1 19 11/04/2012

SIFT-MS in Gent – DAG 2 20 12/04/2012

SIFT-MS in Gent – DAG 3 21 13/04/2012

SIFT-MS in Gent – DAG 4 22 16/04/2012

SIFT-MS in Gent – DAG 5 23 17/04/2012

SIFT-MS in Gent – DAG 6 24 18/04/2012

SIFT-MS in Gent – DAG 7 25 19/04/2012

SIFT-MS in Gent – DAG 8 26 20/04/2012