The Development of Ion-

Selective Membranes for use

in Multisensory Skin Patches

by

Oliver Pemble

A dissertation submitted to the Faculty of Science, Engineering and Food

Science in partial fulfilment of the requirements for the degree of

Masters of Analytical Chemistry: Analysis of Pharmaceutical Compounds

University College Cork

October 2016

Supervisors: Dr. Van Ahn Dam and Dr. Marcel Zevenbergen

Academic Supervisor: Dr. Eric Moore

Head of Department: Professor Justin Holmes

University College Cork

2

3

Acknowledgements

I would like to thank my supervisors Dr Van Anh Dam and Dr Marcel Zevenbergen for all

their help and support throughout my placement with imec in the Netherlands and the ion and

gas sensors team for providing new perspectives on current project work. I also wish to thank

the supervisor for the master’s course, Dr Eric Moore, for organising many engaging

workshops, talks and tours with local industries and companies and enabling our class to

acquire the relevant skills to pursue careers with such industries. Armed with the knowledge

gained from this course, I feel confident to broaden my horizons and seek out a career in

science.

I would like to thank my fellow classmates in the Analytical Chemistry course for always

having each other’s backs and creating a great sense of camaraderie between all of us. I wish

them all the best for the future.

Finally, I would like to express my deepest gratitude to my family for their moral support and

guidance over the past year, for allowing me to rant when times were tough and for always

making me laugh and forget my troubles. You guys are the best.

4

Declaration of Originality

I hereby declare that this thesis is my own work, in partial fulfilment of the requirements of the

Master of Analytical Chemistry degree. It is based on research carried out with imec

Netherlands in the Holst Centre, High Tech Campus, Eindhoven, Netherlands between April

2016 and September 2016.

Oliver Pemble

Date:

5

Abstract

This paper describes the fabrication and testing of a disposable generic electrochemical sensing

platform that utilizes multiple ion-selective electrodes fabricated on flexible substrates by using

screen printing and drop casting techniques, for continuous monitoring of the ionic composition

of fluids (biological or environmental). The analytes of interest are potassium and sodium ions,

the concentrations of which in biological fluids act as indicators to the subject’s health and

wellbeing and are particularly relevant to conditions such as cystic fibrosis and hyponatremia.

The flexible form factor could be ultimately incorporated into a skin patch device which would

assess the salt content in sweat that could provide information on dehydration. Specifically,

this work focuses on the assessment of the lifetime and stability of the ion selective electrodes

in relation to the potential established at the membrane by the ions of interest. For the

miniaturisation process the conventional internal filling electrolyte is replaced by hydrogel

preloaded with KCl (0.1 M) to stabilize the contact between the AgCl electrode and the

hydrogel. The selectivity of the sensor towards the ion of choice was achieved by

functionalising the electrode with an ion-selective membrane that contains a relevant

ionophore. The properties of a range of electrodes prepared using different ionophores and

membrane compositions were investigated.

Sensors fabricated initially using polyvinylchloride (PVC) based membranes displayed a

Nernstian sensitivity of approx. 55-59 mV per decade ion concentration, see Graph 1 and

Graph 2. Stability and lifetime tests were carried out on PVC based sensors by continuous and

non-continuous measurement of the average potential across the ion selective membrane over

a period of time. After 8 days of continuous measuring, the potential of a K+ sensor that was

submerged in a solution containing KCl (0.01 M) and NaCl (0.1 M), showed a linear drift of

approx. 40 mV, i.e. corresponding to a drift rate of 5 mV/day. Over a period of 6 weeks a K+

sensor, which was stored in moist conditions and was calibrated once every week showed the

sensitivity of approx. 25 mV per decade ion concentration.

The implementation of siloprene-based membranes was also investigated with a focus on

ionophore concentration and number of layers to be drop cast and how they affect the

sensitivity and stability of the sensors. An optimised membrane composition ratio of 0.63:100

stock solution to siloprene was developed alongside the ideal number of layers (7.5 µL + 7.5

µL) on the sensor.

6

Table of Contents

Acknowledgements .................................................................................................................... 3

Declaration of Originality .......................................................................................................... 4

Abstract ...................................................................................................................................... 5

Table of Contents ....................................................................................................................... 6

List of Figures ............................................................................................................................ 8

List of Tables ............................................................................................................................. 9

List of Graphs .......................................................................................................................... 10

Chapter 1: Introduction ........................................................................................................ 11

1.1 Title of Project ........................................................................................................... 11

1.2 Background ............................................................................................................... 11

1.2.1 Ion-selective electrode applications and advantages ......................................... 11

1.2.2 Wearable Devices .............................................................................................. 12

1.3 Objectives .................................................................................................................. 14

1.4 Scope ......................................................................................................................... 15

Chapter 2: Electrochemical Background ............................................................................ 16

2.1 Nernst Equation ......................................................................................................... 16

2.2 Difference between activity and concentration ......................................................... 17

2.3 Exchange Current ...................................................................................................... 19

2.4 Electrodes .................................................................................................................. 19

2.4.1 Silver/Silver Chloride Reference Electrode ....................................................... 20

2.4.2 Ion-Selective Electrode ...................................................................................... 21

2.5 Phase Boundary Potential.......................................................................................... 25

2.5.1 Phase boundaries as explained by an extraction experiment ............................. 26

2.6 Ion-Selective Electrode Characterisation ................................................................. 28

2.6.1 Calibration.......................................................................................................... 28

2.6.2 Detection Limit .................................................................................................. 28

2.6.3 Potential Drift..................................................................................................... 29

2.6.4 Precision ............................................................................................................. 29

2.6.5 Selectivity .......................................................................................................... 31

2.6.6 Screen Printing ................................................................................................... 32

2.7 Ion-Selective Membranes .......................................................................................... 33

2.7.1 Membrane Fabrication ....................................................................................... 33

2.7.2 Immobilised Valinomycin Molecule for K+ Sensor .......................................... 33

7

2.7.3 Sodium Ionophore IV ........................................................................................ 35

2.8 Sweat ......................................................................................................................... 35

2.9 Reproducibility, stability and lifetime ....................................................................... 36

2.9.1 Potassium sensors .............................................................................................. 36

2.9.2 Solid contact potassium selective electrodes – A Review Table ....................... 38

2.9.3 Sodium sensors .................................................................................................. 40

2.9.4 Solid contact sodium selective electrodes – A Review Table ........................... 41

Chapter 3: Experimental Materials and Methods .............................................................. 42

3.1 Formulation of the ion-selective membrane.............................................................. 43

3.1.1 Stock Solutions .................................................................................................. 43

3.1.2 Ion-selective membranes ................................................................................... 44

3.1.3 Hydrogel ............................................................................................................ 46

3.2 Ion-selective electrode miniaturisation ..................................................................... 47

3.3 Drop-casting method ................................................................................................. 48

3.4 Flexible sensor stick .................................................................................................. 49

3.5 Sensor Calibration ..................................................................................................... 50

Chapter 4: Results and Discussions...................................................................................... 51

4.1 Characterisation of the sensor ................................................................................... 51

4.2 Continuous measurement of the sensor ..................................................................... 57

4.2.1 3.8 days .............................................................................................................. 57

4.2.2 11.8 days ............................................................................................................ 61

4.3 Non-continuous measurement of the sensor ............................................................. 63

4.3.1 Sensitivity over 42 days ..................................................................................... 63

4.3.2 Device 2 sensitivity ............................................................................................ 65

4.3.3 Device 3 Sensitivity ........................................................................................... 66

4.3.4 Device 4 Sensitivity ........................................................................................... 67

4.3.5 All Test Comparison .......................................................................................... 68

4.4 Siloprene-based sensors ............................................................................................ 71

4.4.1 Measurements within 1 day ............................................................................... 72

Chapter 5: Conclusions and Future Work .......................................................................... 74

5.1 Conclusion ................................................................................................................. 74

5.2 Future Work .............................................................................................................. 75

References ................................................................................................................................ 77

8

List of Figures

Figure 1: “SWEATCH” device. .............................................................................................. 13

Figure 2: Screen printed sensor stick without ion-selective membrane .................................. 14

Figure 3: CRISON 50 44 reference electrode ......................................................................... 20

Figure 4: Ion Selective Electrode diagram .............................................................................. 22

Figure 5: A typical calibration set-up for a sensor. ................................................................. 31

Figure 6: Valinomycin and Lysine substituted Valinomycin ................................................. 34

Figure 7: Valinomycin ............................................................................................................ 44

Figure 8: Sodium Ionophore IV…...…………………………………………………………44

Figure 9: KTBC (tetrakis(4-chlorophenyl) borate) ................................................................. 44

Figure 10: Polyvinyl Chloride (PVC) ..................................................................................... 45

Figure 11: Di(2-ethylhexyl) sebacate (DOS) .......................................................................... 45

Figure 12: Hydroxyethyl cellulose (HEC) .............................................................................. 46

Figure 13: Triethylene glycol (TEG) ...................................................................................... 46

Figure 14: Miniaturisation of a conventional ISE onto a flexible substrate ........................... 47

Figure 15: Layers are drop-cast onto the flexible substrate .................................................... 48

Figure 16: Schematic of the flexible sensor stick with 4 electrode sites ................................ 49

Figure 17: Diagram of the general set up of a calibration measurement ................................ 50

Figure 18: OP-30 Na Sensor (siloprene/DCM) drop-cast and measured on the same day ..... 72

Figure 19: OP-30 Na Sensor (siloprene/DCM) measured on the day after ............................ 72

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List of Tables

Table 1: Required ranges for potassium ions in common biological fluids ............................ 37

Table 2: Review of current solid contact potassium selective electrode research .................. 38

Legend for Table 2 ................................................................................................................. 39

Table 3: Required ranges for sodium ions in common biological fluids ................................ 40

Table 4: Review of current solid contact sodium selective electrode research ....................... 41

Legend for Table 4 ................................................................................................................. 41

Table 5: OP-13 Calibration slopes and R values..................................................................... 53

Table 6: OP-13 all series slopes .............................................................................................. 55

Table 7: all series errors .......................................................................................................... 55

Table 8: OP-13 all series percentage errors ............................................................................ 55

Table 9: OP-13 all series error deviations ............................................................................... 56

Table 10: OP-13 all series percentage error deviations ........................................................... 56

Table 11: OP-24 all devices rate of drifts ............................................................................... 57

Table 12: OP-24 before and after continuous measurement slopes ........................................ 59

Table 13: OP-4 all devices rate of drifts ................................................................................. 61

Table 14: OP-4 calibration before continuous measurement .................................................. 62

Table 15: OP-3 all devices slopes over 6 weeks ..................................................................... 63

Table 16: OP-3 device 2 slopes and offsets ............................................................................ 65

Table 17: OP-3 device 3 slopes and offsets ............................................................................ 66

Table 18: OP-3 device 4 slopes and offsets ............................................................................ 67

Table 19 and 20: Ratios for the siloprene-based sensors........................................................ 71

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List of Graphs

Graph 1: OP-13 calibration series .......................................................................................... 52

Graph 2: OP-13 calibration slopes.......................................................................................... 52

Graph 3: OP-13 device 1 ........................................................................................................ 53

Graph 4: OP-13 device 2 ........................................................................................................ 54

Graph 5: OP-13 device 3 ........................................................................................................ 54

Graph 6: OP-13 device 4 ........................................................................................................ 55

Graph 7: OP-24 continuous measurement over 4 days .......................................................... 57

Graph 8: OP-24 before continuous measurement................................................................... 58

Graph 9: OP-24 after continuous measurement ..................................................................... 58

Graph 10: OP-4 continuous measurement over 12 days ........................................................ 61

Graph 11: OP-4 calibration before continuous measurement ................................................ 62

Graph 12: OP-3 non continuous measurement slopes(sensitivities) ...................................... 63

Graph 13: OP-3 device 2 all tests ........................................................................................... 65

Graph 14: OP-3 device 3 all tests ........................................................................................... 66

Graph 15: OP-3 device 4 all tests ........................................................................................... 67

Graph 16: OP-3 test 1 ............................................................................................................. 68

Graph 17: OP-3 test 2 ............................................................................................................. 68

Graph 18: OP-3 test 3 ............................................................................................................. 69

Graph 19: OP-3 test 4 ............................................................................................................. 69

Graph 20: OP-3 test 5 ............................................................................................................. 70

Graph 21: OP-3 test 6 ............................................................................................................. 70

Graph 22: OP-3 test 7 ............................................................................................................. 71

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Chapter 1: Introduction

1.1 Title of Project

The Development of Ion-Selective Membranes for use in Multisensory Skin Patches.

1.2 Background

This project was undertaken alongside the imec ion and gas sensors group over a period of 6

months under the supervision of Dr Marcel Zevenbergen and Dr Van Anh Dam. The imec team

is currently researching and developing a miniaturized, flexible multi-ion sensing wearable

device. The device is designed to simultaneously detect and quantify multiple ion

concentrations using volumes on a micro scale. Functionalising ion-selective membranes onto

the device enables customisable ion sensing detection of multiple analytes at once. The current

system is able to measure K+, Na+, Cl- NO3- and pH after thorough calibration and sensitivity

tests.

1.2.1 Ion-selective electrode applications and advantages

Ion selective electrodes (ISEs) are used in a wide variety of applications for determining the

concentrations of analyte ions in aqueous solutions:

Food processing and regulation: K in fruit juice and wine making, Ca in beer and dairy

products, NO3 and NO2 in meat preservatives.

Agriculture: NH4, Cl, K, NO3, I in soils and fertilizers.

Water quality: F in drinking water and CN, S, Cl NO3 in lakes and rivers for pollution

control.

Clinical analysis: Ca, K, Cl, Na in blood, plasma, sweat and serum.

The number of applications for ISEs in various industries has been steadily growing over the

past few decades. 1,2 They have many advantages that enable them to be used in different

environments and conditions, some of these advantages include:

Compared to many other analytical techniques, they are relatively easy to use and

inexpensive.

They can be manufactured with robust and durable materials that can be used in the

field or laboratory environments.

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With frequent calibration, they are able to determine the concentrations of ions with

high precision and accuracy. This is especially useful compared to other analytical

techniques that may require complex instrumentation to achieve such accurate data.

In favourable conditions when measuring in dilute aqueous solutions, interfering ions

do not affect the selectivity of the sensor.

They are useful in medical and biological applications since they measure the activity

of the analyte ion and not the concentration directly.

As mentioned above, applications for such a sensor device include food quality analysis,

determination of bodily fluids for clinical diagnostics and ion concentration determination in

water samples. For the purposes of this project, we will be focusing on the application of

integrating the sensor with a sweat patch to measure transferable ion concentrations.

1.2.2 Wearable Devices

Presently, commercial wearable devices are only able to detect an individual’s vital signs and

physical activities such as heart rate and number of steps taken (pedometer). These sensors do

not display molecular information which can provide insight into their general health and

wellbeing. The monitoring of human sweat is a non-invasive method to accessing physiological

information. Currently sweat analysis is used in clinical diagnostics, athletic performance

monitoring and drug use detection. 3 Monitoring hydration levels is of the utmost importance

to athletes and sports enthusiasts because a deficit of fluid can impair general performance and

increase the reliance on carbohydrates. 4 However, these applications require separate sample

collection and analysis and does not create a profile based on real-time sweat secretion. A

wearable device that functions as a sweat monitoring sensor can achieve this. 5

It is also important to note that a wearable device needs to be able to withstand everyday stress

from physical activity and frequent usage. Human body temperature changes and rate of

perspiration will contribute to the accuracy of the overall sensing system. This particular aspect

of the device is not investigated in this study but it may affect future work based off data from

this project.

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Recently, there has been research into developing a platform for the harvesting and analysis of

sweat via a wearable device. Glennon et al 6 have created a “SWEATCH” device for this

purpose. It is a watch-based platform that can collect sweat through a sampling orifice which

then passes over a sodium-selective electrode and reference electrode. The movement of the

liquid is entirely driven by capillary force; the flow rate can be altered by changing the width

of the microfluidic channels. The data that is collected is accessed remotely though wireless

Bluetooth connectivity.

Figure 1: “SWEATCH” device. Type (a) with vertical arrangement and “Pod” type platform.

1: sweat harvesting device in 3d-printed platform base, 2: fluidic sensing chip, 3: electronic

data logger and battery, and 4: 3d-printed upper casing 6

The device was able to achieve near Nernstian response with an average slope of 58.03±3.458

mV/decade Na+. 6 This is the ideal type of response for such a system and the platform is

somewhat similar to the work that the ion and gas sensors group at imec is undergoing. Indeed,

this is just one example of the many areas of research into a wearable device system for sweat

14

analysis. The interest in real-time monitoring of biological fluids is a rapidly growing field in

both chemical and biological studies. 7

Figure 2: Screen printed sensor stick without ion-selective membrane

1.3 Objectives

The objectives of this project can be broken down into 3 main goals:

1. Investigation of procedures for determining the lifetime and shelf life of ion selective

membrane based sensors with integrated reference electrodes from the literature.

2. Optimisation of the ion selective membrane formulas and method of development to

produce a highly sensitive electrode that is able to detect ion concentrations within the

ranges of human sweat.

3. Developing an efficient and easy method for determining the lifetime and shelf life of

the sensor devices based on monitoring the potential drift over a series of tests.

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1.4 Scope

The main focus of this project is to develop a method to determine both the lifetime of the

sensor when it is in use, and the shelf life of the sensor when it is not in use. We will look at

the calibration curves of each sensor both before and after a series of tests and compare each

result to identify the extent of potential drift. Through continuous and non-continuous

measurements of a range of standard solutions one can determine the rat of drift or deterioration

of the membrane. Based on these results, one can alter the formulation of the membrane

composition by changing reagent concentrations and changing the amount of membrane

solution that is used in making the sensors. The fabrication of the sensor sticks will not be the

main focus as the gas and ion sensor team have developed an optimised procedure for the

screen-printing method.

16

Chapter 2: Electrochemical Background

2.1 Nernst Equation

Potentiometry is an electroanalytical technique that measures the voltage or potential of a

sample solution between 2 electrodes at zero current. The electrodes and the composition of

the analyte remain unchanged since zero current flows through the cell. This makes

potentiometry a useful quantitative method.

Le Châtelier’s principle tells us that changing the concentrations of reactant or product for any

chemical reaction can shift the equilibrium to favour a certain outcome. The Nernst equation

describes the net driving force for a reaction and how it is expressed by its dependence on

reactant concentration. 8

Consider the half reaction:

𝑎𝐴 + 𝑛𝑒− ⇌ 𝑏𝐵

The Nernst equation for this reaction is expressed as:

𝐸 = 𝐸0 −𝑅𝑇

𝑛𝐹ln

𝛼𝐵𝑏

𝛼𝐴𝑎

Where E0 = Standard Reduction Potential (αA = αB = 1), R = Gas Constant (8.314 J/(K mol),

T = Temperature (K), n = Number of electrons involved in the half reaction, F = Faraday

constant (9.649x104C/mol), αi = Activity of species i.

The logarithmic term ln𝛼𝐵

𝑏

𝛼𝐴𝑎 can also be expressed as the reaction quotient Q.

Q has the same functionality as the equilibrium constant but pure solids, pure liquids and

solvents are omitted from Q because they have activities that are equal or close to unity. If all

activities are in unity, then Q = 1 and lnQ = 0 therefore E = E0.

17

Converting the natural logarithm in the above equation to base 10 and setting the reaction

conditions to room temperature (298 K) we can express the Nernst Equation in its most useful

form:

𝐸 = 𝐸0 −0.05916

𝑛log 𝑄

Where the potential is given in JC-1 = V. The potential changes by 59.16/n mV for each factor-

of-10 change in Q.

2.2 Difference between activity and concentration

Activity of an ion is the effective concentration, that is, the portion of ions that are free to react.

The reaction in this case is the ions coming into contact with the membrane surface. The

difference between the activity and concentration is expressed as the Activity Coefficient.

Generally, the activity is always numerically less than the concentration because the ions also

take part in inter-ionic interactions within the solution. These interactions can prevent the

movement of some ions and reduce the likelihood of them reaching the membrane surface. As

the concentration increases the activity becomes proportionately less but in practice the inter

ionic interactions are negligible. 8

Potentiometric sensors measure the activity of ions and not necessarily the concentration. By

using the activity coefficient, we can calculate the concentration from a potentiometric

measurement. The activity coefficient is the ratio of the activity divided by the concentration.

It is a variable factor that depends on the ionic strength of the solution and the valency and

ionic radius of the analyte ion. It is possible to calculate the activity coefficient from the

following formulas and incorporating ionic strength:

Ionic Strength: 𝐼 = 1

2∑ 𝑐𝑖𝑧𝑖

2𝑛𝑖=1

Where c is the concentration in moles and Z is the valency.

Activity: 𝛼𝐶 = 𝛾𝑖[𝐶]

Where αC is the activity of the ion C, γi is the activity coefficient, [C] is the concertation of

analyte ion C.

18

The Debye-Hückel limiting law can be used to calculate the activity coefficient γ:

ln 𝛾𝑖 = −𝑧𝑖

2𝑞2𝜅

8𝜋𝜀𝑟𝜀0𝑘𝐵𝑇

Where zi is the charge number of the ion, q is the elementary charge, κ is the inverse of Debye

length, εr is relative permittivity, ε0 is the permittivity in free space, kB is Boltzmann’s constant

and T is temperature.

ln 𝛾𝑖 = −𝑧𝑖

2𝑞3𝑁𝐴

12⁄

4𝜋(𝜀𝑟𝜀0𝑘𝐵𝑇)3

2⁄√

𝐼

2

Where NA is Avogadro’s number.

ln 𝛾𝑖 = −𝐴𝑧𝑖2√𝐼

Where a is a constant that depends on temperature. However, there is a simpler equation that

can be used:

log 𝛾𝑖 =−0.51𝑧𝑖

2√𝐼

1 + 3.3𝑑𝐶√𝐼

Where dC is the effected diameter of the analyte ion. 9

The most significant revelation from this equation is that the mean activity coefficient is

dependent on ionic strength I and not concentration of the ions within the ionic solution. The

above equation for Log(γi) is ideal for experimental measurements with low concentrations.

Ions that produce larger charges cause deviations from the overall Debye-Hückel theory due to

the simple nature of the model. Because of this, several assumptions are made for the model

which can give rise to limitations:

Composition of the solvent will affect electrolyte ion. Water molecules are polarisable.

Ion-solvent interactions are generally ignored in the Debye-Hückel theory.

Electrolytes not fully dissociating because they are weaker. Using a dissociation

constant one can calculate the extent of how much a particular electrolyte will

dissociate. By calculating this value, corrects may be made for the activity coefficient.

The assumption that ions are spherical and are not polarised, the charge remains

homogenous throughout. Many ions such as the sulphite ion SO32- are not spherical and

can be polarised due to the polyatomic structure.

19

Ion association may take place within ions of larger size and higher charge due to

complete dissociation.

The behaviour of an electrolyte ion deviates considerably from that of an ideal solution.

Utilizing the activity of the ion instead of the concertation we can fully understand the nature

of the ion. 8

2.3 Exchange Current

The voltage at equilibrium as described by the Nernst equation is dynamic with no net current

throughout the electrode. However, leakage currents at the reference and indicator electrodes

cause redox reactions at the sites. The reactions do not change the composition of the

electrolytes they are both occurring at the same rate. The exchange current density is a term

that expresses the dynamic flow of electrons in these redox reactions, i.e. the current at

equilibrium, the rate of reaction at reversible potential. At reversible potential the system is at

equilibrium and the forward and reverse rates of reaction are the same. The exchange current

density is the rates of reaction between the electrode and the electrolyte and can give insight

into the properties of a material.

2.4 Electrodes

As established above, the use of electrodes to determine the voltages of analytes to provide

chemical information is called potentiometry. Analytes are electroactive species that can either

donate or accept electrons at an electrode.

A typical set up of a potentiometric sensor consists of an indicating electrode submerged in an

analyte for exchanging ion/electrons with the ions of interest. This half-cell is then connected

to another half-cell by a salt bridge which maintains electronic neutrality within the internal

circuit. The second half cell has a fixed concentration and therefore a constant potential, the

reference electrode. The overall cell voltage is the difference between the constant potential of

the reference and the variable potential of the indicating electrode. It should be noted that the

electrode responds to the activity of the analyte ion and not specifically the concentration but

they are related as shown in section 2.2.

20

2.4.1 Silver/Silver Chloride Reference Electrode

Silver/Silver chloride (Ag/AgCl) is a widely-used type of reference electrode because of its

simplicity, ease of use, stability and non-toxicity. It is constructed as a thin tube containing

solution with high KCl concentration and a Ag/AgCl electrode dipped in. There exists a double

junction that further separates the analyte solution and the inner KCl, minimising their contact.

Unlike the Standard Hydrogen Electrode (SHE) it does not require H2 gas or a prepared

platinum surface that could easy be contaminated by many solutions.

The standard reduction potential for a typical Ag/AgCl system is shown below:

AgCl(s) + e- ⇌ Ag(s) + Cl- Eo = +0.222 V

E(saturated in KCl) = +0.197 V

The electrode contains an air inlet at the top which allows the electrolyte slowly though the

porous salt bridge plug that comes into contact with the analyte solution. A problem that can

arise is that the plug can get clogged which causes a slow electrical response from the electrode

and it will take longer for the signal to plateau. This can be solved by replacing the porous plug

with a free flowing capillary system, allowing quicker response times and swift signal

generation.

Figure 3: CRISON 50 44 reference electrode with internal 3 M KCl gel electrolyte and

lithium acetate electrolyte in salt bridge 10

21

2.4.2 Ion-Selective Electrode

Ion-selective electrodes are, as the name suggests, electrodes that can selectively detect and

measure a specific ion in an analyte or sample. Most ion-selective electrodes can be categorized

into the following classes: 8

Glass membranes for species such as H+ and other monovalent cations.

Solid-State electrodes based on inorganic crystalline compounds.

Liquid-based electrodes that use hydrophobic polymer membranes that are covered in

liquid ion exchanger.

Compound electrodes with analyte selective membranes enclosed by membranes that

separate the specific analyte from other components.

Ion-selective electrodes have the following advantages over conventional ion sensing methods:

Short response time.

Non-contaminating.

Linear response to Log[C+] over a range within the instrumental limits.

Non-destructive.

Unaffected by turbulence.

Can be used inside living cells when on the micro scale.

22

Figure 4: Ion Selective Electrode diagram 8

Figure 4 is a diagram of an ion-selective electrode immersed in an aqueous solution that

contains the analyte C+ and the hydrophobic anion R-. A typical membrane is made of PVC

with a plasticizer such as dioctyl sebacate that dissolves the ion-selective ionophore L and

softens the membrane.

23

Figure 4 shows a liquid based ion-selective electrode. The ion-selective membrane consists of

a hydrophobic organic polymer mixed with a viscous organic solution containing an ion

exchanger and a ligand that can selectively bind the analyte cation. On the inside of the

electrode is the filling solution containing the ions C+ and B- and on the outside is the analyte

solution containing the same ions. The voltage is the electric potential difference across the

ion-selective membrane, which is measured between internal and external reference electrodes.

As the concentration or activity of C+ ions change, so does the voltage measured between the

electrodes. By measuring this voltage and using the Nernst equation one can find the activity

of C+ ions and subsequently the concentration.

One of the key aspects of the ion-selective membrane model is the ionophore ligand denoted

by L. The ionophore has the ability to selectively bind to the analyte. The ligand L is chosen

based on its high affinity to the analyte cation C+ (high sensitivity) and relative low affinity

towards other ions (high selectivity). For example, the ionophore chosen for the K+ ion is

Valinomycin, the natural antibiotic. Ideally the ligand L will only bind to the desired ion

however real electrode will always have some affinity to other ions. To avoid interference from

these other ions a hydrophobic ion R- is incorporated to initiate charge neutrality.

Inside the membrane, we can see the analyte ion C+ is bound to the Ligand L in the complex

LC+ and is at equilibrium with the free C+ ions which can freely diffuse through the interface.

The anion R- cannot leave the membrane due to its hydrophobicity and A- cannot enter the

membrane because it is not organically soluble. If a few C+ ions diffuse into the analyte aqueous

phase there exists a net positive charge. The imbalance of charge generates the difference in

potential.

The C+ ion driving force for diffusing into the aqueous solution is solvation of the ion with

water. When the C+ ion leaves the membrane, a build-up of positive charge in the water close

to the membrane is observed. This separation of charge creates a potential difference over the

membrane. This potential difference is called Eouter. This energy can be expressed in terms of

Gibbs free energy difference:

∆𝐺 = −𝑛𝐹𝐸𝑜𝑢𝑡𝑒𝑟

Where n is the charge of the ion and F is the Faraday constant.

24

The net change of free energy for the diffusion of the ion across the membrane must be equal

to 0 at equilibrium. Similarly, the action of C+ diffusing into a region of activity from the

membrane αm to the outer solution α0 has the free energy change of:

∆𝐺 = ∆𝐺𝑠𝑜𝑙𝑣𝑎𝑡𝑖𝑜𝑛 − 𝑅𝑇 ln𝛼𝑚

𝛼𝑜

Where ΔGsolvation is the free energy change in the solvent. We can make these 2 equations equal

to each other at equilibrium combining the ΔG due to the transfer between the 2 phases and

difference in activity and the ΔG due to the imbalance in charge:

∆𝐺𝑠𝑜𝑙𝑣𝑎𝑡𝑖𝑜𝑛 − 𝑅𝑇 ln𝛼𝑚

𝛼𝑜+ (−𝑛𝐹𝐸𝑜𝑢𝑡𝑒𝑟) = 0

If we solve for Eouter we can find the electric potential difference across the point between the

membrane and aqueous solutions:

𝐸𝑜𝑢𝑡𝑒𝑟 =∆𝐺𝑠𝑜𝑙𝑣𝑎𝑡𝑖𝑜𝑛

𝑛𝐹−

𝑅𝑇

𝑛𝐹ln

𝛼𝑚

𝛼𝑜

From this we can find the potential difference between the boundary of the inner filling solution

and the membrane. The equation is simply:

𝐸𝑖𝑛𝑛𝑒𝑟 = 𝐸𝑜𝑢𝑡𝑒𝑟 − 𝐸

Unlike Eouter, which depends on the C+ activity, Einner is constant due to the constant activity of

C+ in the inner filling solution. However, the activity of the ion in the membrane αm is almost

constant due to the high concentration of LC+ being in equilibrium with free L and C+ in the

membrane. As mentioned above, the R- ion is poorly soluble in water and cannot leave the

membrane. In order for a C+ ion to diffuse into the aqueous phase it must leave behind one R-

ion, as a result very small amounts of C+ can diffuse freely. This means that as soon as a tiny

amount of C+ ions leaves the membrane to enter the aqueous phase, any further diffusion is

prevented by the net positive charge near the surface of the membrane. This can be expressed

in the following equation for the potential difference between the inner and outer solutions:

𝐸 = 𝐸𝑜𝑢𝑡𝑒𝑟 − 𝐸𝑖𝑛𝑛𝑒𝑟 = (∆𝐺𝑠𝑜𝑙𝑣𝑎𝑡𝑖𝑜𝑛

𝑛𝐹−

𝑅𝑇

𝑛𝐹ln

𝛼𝑚

𝛼𝑜) − 𝐸𝑖𝑛𝑛𝑒𝑟

By combining the constant terms into one we get the simplified equation:

𝐸 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 + (𝑅𝑇

𝑛𝐹ln 𝛼𝑜)

25

Therefore, the potential difference across the membrane depends on the activity of the analyte

in the outer aqueous solution. Simplifying further by converting ln to Log and inserting the

values of R, T and F at 25 oC we get:

𝐸 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 +0.05916

𝑛log 𝛼𝑜

With αo being the activity of the ion in the unknown outer solution. If the analyte is an anion,

then the value of n is negative. The above equation shares some similarities with the Nernst

Equation. 8

2.5 Phase Boundary Potential

As proved above the potential across the membrane between the aqueous and hydrophobic

sensing phase is dependent on the logarithmic activity of the analyte ion in the outer aqueous

phase. This potential is known as the phase boundary potential. There is no experimental

method to directly determine the phase boundary potential, we determine the Electromotive

Force (EMF) instead. The EMF is the difference in the electrical potential between the ISE and

the reference electrode. EMF is a sum of 2 components:

1. Phase boundary potentials at all interfaces of the electrochemical cell. There are

numerous phase boundary potentials present along the path from the metal of the

connector of an ISE though the electrode, sample and reference electrode. The interface

types include metal-metal, metal-salt, salt-liquid and liquid-liquid.

2. Drop in voltage based on Ohm’s law. A drop in voltage between the working electrode

and reference electrode caused by the electrolyte conductivity, distance between the

electrode and the magnitude of the current. By Ohm’s law, the drop in voltage is a

product of the resistance and the current. 11

The second component that effects EMF is not applicable to ion-selective potentiometry as it

is nearly almost always performed under currentless conditions and the Ohmic drop is

negligibly small. Therefore, the EMF in an ion-selective potentiometric measurement is the

sum of all phase boundary potentials.

ISEs operate above their detection limits to measure electrical potential which is typically

referred to as Electromotive Force (EMF). The EMF response is directly related to potential

difference across the phase boundary between the sample and the hydrophobic phases.

Considering this we must first look at the significance of the phase boundary between an

26

ionophore-doped hydrophobic phase and an aqueous sample phase. One must understand the

potential across the phase boundary is relevant to the response to the ion of interest and the

selectivity.

2.5.1 Phase boundaries as explained by an extraction experiment 12

Consider an aqueous KCl solution equilibrated with insoluble organic phase containing an

electrically neutral ionophore for K+ such as Valinomycin. The phase boundary potential

depends on the concentration of KCl in the aqueous phase. During equilibrium, some KCl will

be present in the organic phase. With low concentrations of KCl in the system the potassium

ions within the organic phase will be bound to the ionophore in the form of a complex and there

will be an excess of free ionophore. The concentration of [K+] ions that are not bound to the

ionophore in the organic phase is very low relative to the concentration of the ionophore

complexes. The concentration of these free K+ ions can be calculated from the formation

constant of the ionophore-potassium complex [LK+]:

𝛽1:1 =[𝐿𝐾+]

[𝐿][𝐾+]

[𝐾+] =[𝐿𝐾+]

[𝐿]𝛽1:1

As the concentration of KCl in the aqueous phase increases, the concentration of the complex

[LK+] increases and in turn the concentration of free ionophore decreases in the organic phase.

From this we can see that the ratio of free ionophore to complexed ionophore changes and that

the concentration of free K+ in the organic phase depends on the activity of K+ in the aqueous

phase. Within the range of an excess of ionophore the phase boundary potential does not depend

on the concentration of K+ ions in the aqueous phase. The excess of ionophore facilitates the

phase transfer of the K+ ion into the organic phase and in doings so increases the concentration

of KCl in the same phase. The independence of the phase boundary potential to the K+

concentration in the aqueous phase means that it cannot be the basis of a sample EMF for an

ISE. Merely doping the organic phase with an ionophore does not make the ISE suitable for

potentiometric measurements. 12-13 12

To keep the activity of the analyte ion in the hydrophobic sensing phase sample independent,

one must add a hydrophobic ion with an opposing charge to the organic phase alongside the

electrically neutral ionophore. With the case for the K+ analyte ion, such an ion could be a

tetraphenylborate derivative such as tetrakis(4-chlorophenyl) borate (KTBC). Due to the need

27

for electroneutrality the total concentration of potassium ions in the organic phase is equal to

the concentration of the hydrophobic anion in the same phase. If the hydrophobic anion is the

only anion present in the organic phase, then the K+ ion concentration is not dependent on the

concentration of KCl in the aqueous phase. The hydrophobic anion also prevents the transfer

of Cl- ions into the organic phase, the equilibrium constant Kex expresses the distribution of

KCl between the 2 phases:

𝐾𝑎𝑞+ + 𝐶𝑙𝑎𝑞

− ⇌ 𝐾𝑜𝑟𝑔+ + 𝐶𝑙𝑜𝑟𝑔

−

𝐾𝑒𝑥 = 𝛼𝐾𝑜𝑟𝑔

+ 𝛼𝐶𝑙𝑜𝑟𝑔−

𝛼𝐾𝑎𝑞+ 𝛼𝐶𝑙𝑎𝑞

−

If the hydrophobic anion is not present in the organic phase, then the concentrations of K+ and

Cl- in the organic phase are equal. However, when the concentration of K+ ions are high in the

organic phase the activity of Cl- in the same phase is very low. Le Châtelier’s principle shows

that a high concentration of K+ in the organic phase drives the transfer of Cl- ions into the

aqueous phase.

The hydrophobic counter-ion to the analyte ion is commonly referred to as an ionic site and is

necessary for the function of ISE that are based around electrically neutral ionophores. 13 Ionic

sites that are used with an optimised ratio of electrically neutral ionophores improves the ISE

selectivity. For the organic phase to contain a significant concentration of free ionophore, the

concentration of ionic sites needs to be relatively low. From this we can conclude that a

Nernstian response for an ISE requires the ionic sites and ionophore to buffer the analyte ion

during the sensing phase. 12

The 2 main factors for the fabrication of an ISE are to ensure that the activity of the analyte ion

within the hydrophobic sensing phase does not depend on the sample composition and is

constant, and that all measured EMF contributions are sample independent. Once these factors

are taken into account there will be a desired Nernstian response from the analyte ion.

28

2.6 Ion-Selective Electrode Characterisation

2.6.1 Calibration

Calibration is the process of determining the response of a system or device to a known

concentration of the desired analyte to allow the determination of the unknown concentration

of the same analyte in a sample. The calibration curve is made by measuring the electrode

response in a series of standard solutions containing analyte, usually increasing or decreasing

concentration in increments of 10. The result is a line curve with the average potential on the

y-axis and the concentration of the analyte standard on a logarithmic x-axis. The slope or

gradient of the line fitted through the potential-log[analyte] dependence equates to the

sensitivity of the electrode. The theoretical Nernstian slope at room temperature is roughly 59

mV per decade change in ion activity and hence concentration. However, practically the slope

is usually lower than the theoretical value due to the inability to meet ideal conditions.

Therefore, the measured slope of the calibration curve for a monovalent ion is within the range

of 50-55 mV per decade of ion activity. Once the slope and the offset of the calibration curve

is determined, inserting the potential measured in an unknown sample into the equation of the

calibration curve, the concentration of the analyte ion can be calculated.

2.6.2 Detection Limit

Each ion selective electrode has a detection limit which is essentially the concentration/activity

of an analyte/ion can be accurately detected. It is represented on the calibration curve as the

point of intersection where the concentration is so low or so high that any changes to the

concentration below or above that point produce a negligible response from the electrode. One

can use a calibration curve to determine the activity/concentration of an analyte ion from a

linear curve with a R2 value of 1 where the data closely fits the regression line. However, if the

activity/concentration is too high or too low the curve of the line starts to deviate exponentially

from the ideal value of R2=1. In these areas the use of the line formula to determine the

activity/concentration of the analyte ion is inaccurate, hence this is the limit the ion can be

detected at.

29

2.6.3 Potential Drift

Potential drift is the slow change or deviation of a measured potential between the reference

electrode and the indicating electrode over time. Initial measurement of an electrolyte solution

causes large changes in the value of the measured potential. When the indicating electrode

immerses in measuring analyte, the electrode potential will gradually change as the ions in

solution begin to equilibrate with those entering the ion-selective membrane. Once an

equilibrium condition is met the equilibrium potential is used to calculate the ion concentration.

The time it takes for the electrode potential to reach equilibrium is called the response time.

Response time is affected by the type of electrode, the magnitude of which the ion

concentrations differ after changing, temperature, presence of interfering ions and static

condition of the solution (if the solution is being stirred). With continuous immersion of the

electrode in the analyte solution over a long period of time a drift from this equilibrium

condition can occur. The drift may be caused by various additional potentials from the liquid

junctions within the electrode itself or evaporation of the solvent in which the analyte is

dissolved and hence increasing the overall concentration. Other factors that can cause potential

drift are electrode contamination and degradation during measuring and storage that can cause

loss of sensitivity.

2.6.4 Precision

The measure of the reproducibility of a method or mechanism. As with any analytical process

it is unlikely that any 2 measurements of the exact same procedure will give the same results

due to a certain amount of variation. The expression of this random variation is precision. Given

a series of measurements of the same sample, the precision of the method is the standard

deviation of the mean value. It is important to know the precision of the measurements in order

to determine if the results are significantly different or similar and how close they are to the

true value, the accuracy. For example, if the mean values for the results of the exact same 2

measurements differ by more than double the standard deviation they are statistically different

by 66%. If the same number of measurements are performed several times more, the new mean

values can be expected to be within the standard deviation of the initial 66 cases out of 100.

Therefore, if a particular analytical method has a standard deviation of 5% assuming there are

not systematic errors, we can expect that 95% of the measurements give a result that is within

10% of the true value.

30

The accuracy and precision of ISE measurements can be highly variable and are dependent on

several factors. Any error in measurement will cause an error in the concentration as the

measured voltage is proportional to activity and hence concentration. The concentration is

logarithmically dependant on the slope of the calibration line. Mono-valent ions generally have

a slope of about 55 mV per decade concentration so an error of 1 mV will cause approximately

4% error in the concentration. At the lower end of the range of concentration the slope is lower

in value and the graph descends into non-linear data points. This causes greater percentages of

error for concentration at low concentrations. Therefore, it is important to use a system that can

measure millivolts precisely and accurately to minimise the error. This is factored in with the

use of a multiplexer and adequate computer software that is able to display the data concisely.

The most important factors in acquiring the most accurate results possible are controlling the

liquid junction potential of the reference electrode and controlling the drift of the electrode. In

controlling these factors, the measured voltage will be reproducible.

The factors that affect the accuracy of the results are the presence of interfering ions, the

difference in ionic strength between the standards and the sample and any variation of the slope

at different points of the curve. It is highly unlikely that the slope of the calibration curve will

be the same along the range of standard solution measurements. If the overall slope is used in

the calculation of sample concentration, it will give different concentrations to those calculated

using the individual slope between the two points where the sample voltage lies. Using a range

of standards and frequently calibrating the sensor is the best way to ensure that the results will

be accurate and reproducible.

31

Figure 5: A typical calibration set-up for a sensor. The sensor and reference electrode are

submerged together in a range of standard solutions (small beakers) of increasing

concentration and the potential is measured.

2.6.5 Selectivity

Selectivity is the ability of an ion-selective electrode to distinguish one ion from another.

However, ISEs are not 100% selective to only one ion, most are also sensitive to other ions

(interfering ions) within a mixture. Sometimes there exists interfering ions that alter the

recorded potentials, giving an inaccurate result. To minimise the influence of interfering ions,

they can be removed beforehand by adding precipitation reagents or by complexing them with

other molecules leaving only the analyte ion in the measuring solution. To determine the

magnitude of an electrodes ability to selectively detect a specific ion, the selectivity coefficient

is used.

The selectivity coefficient is an expression of how an ISE reacts to an interfering ion relative

to how it reacts to the measured analyte ion. A selectivity coefficient value of 0.2 implies that

the electrode is 5 times more responsive to the analyte ion than the interfering ion. The

selectivity coefficient depends on a number of factors such as ionic strength of the sample

32

solution, temperature and the ratio of ions within the solution. The coefficient is denoted by

KA,B with A being the analyte ion and B being the interfering ion. When KA,B is equal to 1 there

is an equal response to both ions A and B. Another name for the selectivity coefficient is the

Nikolski-Eisenman coefficient, which can be used in an extension of the Nernst Equation to

relate the potential to the activities of the analyte and interfering ions.

For the Nernst equation: 8

𝐸 = 𝐸0 −0.05916

𝑛log 𝑄

Log(Q) is replaced with: 8

log 𝛼𝑥 + [𝐾𝑥,𝑦(𝛼𝑦)𝑍𝑥𝑍𝑦 + 𝐾𝑥,𝑧(𝛼𝑧)

𝑍𝑥𝑍𝑧 +] … 𝑒𝑡𝑐

Where Kx,y is selectivity coefficient for ion y of an electrode sensitive to primary ion x, Kx,z is

selectivity coefficient for ion z of an electrode sensitive to primary ion x, αx is the activity of

primary ion x, αy and αz is the activity of interfering ions y and z respectively, Zx is the charge

on the primary ion x, Zy and Zz is the charge of the interfering ions y and z respectively.

A membrane shows significantly good selectivity when the selectivity coefficient is just below

the value of 1. Selectivity is not only a main characteristic of ISEs but it is also used to

determine the stabilities and stoichiometries of ionophore complexes for their use in specific

applications.

2.6.6 Screen Printing

The main advantage of screen printed sensors from a manufacturing perspective is the ability

to reproducibly fabricate large numbers of sensors by relatively simple and low cost

techniques.14 The printing process is fast, inexpensive and suitable for mass production. Screen

printing has indeed played an important role in the development of sensors over the years for

example, the fabrication of microfluidic paper based sensors by Zhihong et al. They determined

that paper was an ideal matrix for electrochemical devices, providing a thin mechanically

stabilized film that directs analyte fluids to the surface of electrodes. The utilization of the

screen printing method allowed them to create sensors with relative ease. 15 16, 16b, 17

33

2.7 Ion-Selective Membranes

2.7.1 Membrane Fabrication

Hydrophobic polymers such as PVC are generally used as substrates for the fabrication of the

ion selective membrane. The polymer is capable of producing thin films of sufficient

permeability and in combination with the ionophore and the plasticizer, creates ionic mobility

across the membrane. During its use, the plasticizer and ionophore ligand may continuously

migrate out of the membrane.

To meet the demand for portable sensing devices with small sample volumes, low cost and

easy maintenance we need to overcome several limitations facing the fabrication of ion-

selective electrode devices. The ISE liquid contact filling solutions are sensitive to temperature

and pressure and therefore are at risk of evaporation. Also the differences of ionic strengths

between the sample and the inner filling solution can cause rapid changes in osmotic pressure

and large changes in volume. 18

2.7.2 Immobilised Valinomycin Molecule for K+ Sensor 19

Valinomycin is an appropriate choice for a ligand in a K+ sensor as it exhibits very high affinity

towards the ion. It is potent antibiotic that can act as a K+ ionophore which induces K+

conductivity in cell membranes. As it does not have any residual charge, it is a natural neutral

ionophore. Highly selective for potassium ions over sodium ions, it functions as a potassium

transporter. Valinomycin facilitates the movement of potassium ions through lipid membranes

with an electrochemical potential gradient. 20

The ionophore is used in the creation of the potassium selective membrane alongside other

membrane components such as a plasticizer and a polymer matrix. However, some difficulties

have arisen with this composition of the K+ membrane, the ligand and plasticizer are observed

to leach out of the membrane and cause inaccurate results due to potential drift.

Considering this problem, Pepi et al. 19 has sought to create a stabilised K+ ion selective

membrane using lysine-substituted Valinomycin that is covalently bound to an insoluble solid

polymeric substrate. They have found that this membrane has improved stability within an ISE

and the lifetime has extended because the ligand does not leach out of the membrane.

Additionally, they have a method to determine the optimum concentration of the ligand within

the membrane to reproduce the solution consistently.

34

The use of Valinomycin allows for improved potassium ion selectivity which is imparted to

the Ion-selective sensor. They incorporate the use of lysine derivative of Valinomycin that is

covalently bonded through the primary amine group of the lysine to a carboxyl group present

on an insoluble organic solid polymer substrate. The covalently linked Valinomycin ligands

can be applied with conventional ISEs if they are immobilised to a polymer within a conductive

membrane. Substituting Lysine in place of Valine in Valinomycin provides a side chain

through which the molecule can be immobilised.

Figure 6: Valinomycin and Lysine substituted Valinomycin 19

The mechanism that binds K+ ions to Valinomycin is similar to that of water. Free potassium

is surrounded by the oxygen atoms in H2O and the same is observed in Valinomycin with the

6 oxygen atoms in the Valine groups. The ring structure of the molecule allows it to coordinate

into the most favourable orientation and provides a polar interior to accommodate the

potassium ion. The size of the Valinomycin ring is larger than the ionic radius of K+ and the

ligand can wrap around the cation and a high selectivity for K+ is achieved. Consequently, the

molecule then also creates a non-polar lipophilic exterior.

Potassium ions are the most abundant physiological metal ions present in the body and they

have various crucial roles in biology. They maintain suitable pH equilibrium and cellular

osmotic pressure and throughout the nervous system they are involved in different sensory

transduction cascades. 21 Potassium has roles in the biological processes that are associated

with the regulation of nerve transition, blood pressure, kidney function and muscle contraction.

An imbalance of potassium can trigger certain diseases and conditions such as hypertension,

anorexia, strokes, diabetes, heart disease and renal disease. 11 With these significances of

potassium ions in mind, it is crucial that accurate analytical techniques are available to detect

and quantify these ions.

35

2.7.3 Sodium Ionophore IV

2,3:11,12-DIDECALINO-16-CROWN-522 (Sodium Ionophore IV) is an ideal ionophore of

choice to reversibly bind to sodium ions. It has a high affinity towards sodium due to the same

basic mechanism described in the previous Valinomycin section. This ionophore also provides

an interior polar ring structure that allows for the accommodation of a Na+ ion.

2.8 Sweat

Human sweat consists of a complex mixture of numerous ions and trace substances and can

provide physiological information. The complexity of sweat means that in order to detect and

quantify components, simultaneous, multiplexed and integrated measurements are required to

ensure complete accuracy. 5

There exists a relationship between the concentrations of specific ions in sweat and conditions

that lead to diseases. Ions such as sodium and potassium are among the most important

electrolytes present in the body. 23 Excessive loss of Na and K can lead to hyponatremia, muscle

cramps or dehydration. Hyponatremia is the most common electrolyte disorder and is caused

by serum sodium levels dipping below 135 mmol per L. Total serum sodium level is affected

by the total exchangeable sodium and potassium levels in biological fluids, blood, sweat etc.

Patients with acute hyponatremia likely need treatment in the intensive care unit with

hypertonic saline solution to prevent any permanent neurologic injury. 24 The relationship

between total serum sodium levels and exchangeable ions is shown in the following equation:

[𝑁𝑎]𝑠 =[𝑁𝑎]𝑒 + [𝐾]𝑒

𝑇𝐵𝑊

Where [Na]s is serum sodium level, [Na]e is exchangeable sodium level, [K]e is exchangeable

potassium level and TBW is total body water content.

One of the main symptoms of dehydration is excess levels of chloride and sodium in sweat.

The average sodium sweat concentration in humans is roughly 35 mmol L-1 and average

chloride sweat concentration is 30 mmol L-1 with variables including diet, genetic

predisposition and sweat rate. 25

Monitoring the concentration of chloride ions in sweat can confirm or deny the diagnostic of

Cystic Fibrosis. If the sweat chloride concentration exceeds 60 mmol L-1 it indicates the

presence of the expressed recessive gene that leads to the disorder. 26

36

A flexible sensor that can incorporate a quality of detection for clinical purposes must be able

to perform accurately and consistently over a long period of time. As with any sensing system,

a minimal drift from consecutive tests is ideal and inspires confidence in the abilities of the

sensor. Both the stability of the sensor during analysis and the lifetime when it is not being

used (shelf-life) need to be studied to optimise the sensors capabilities.

2.9 Reproducibility, stability and lifetime

Typically for analytical devices or electrochemical biosensors the definition of reproducibility

is the measure of the drift in a series of tests or observations over a specified period of time,

generally determined within a usable range of analyte concentrations. 27

The stability of operation of a sensor varies considerably depending on preparation, design,

chemical environment and physical conditions. Operational stability can be determined

optimally with known analyte concentrations, continuous or sequential contact between the

sensor and analyte and ideal lab conditions such as temperature, pressure and pH. Knowing the

rate limiting step for the sensor is important for knowing the stability. For the assessment of

storage stability significant considerations include the state of storage, i.e. wet or dry, presence

of additives, atmospheric pressure and temperature etc. 27

It is important to distinguish between the lifetime of storage (shelf-life) and operational lifetime

and to take into account the various conditions in each. Also, it is important to specify the mode

of assessment of lifetime, i.e. by referring to the initial sensitivity test using a linear

concentration range for a calibration curve. D.R. Thévenot et al states that the definition of

lifetime tL is:

“The storage or operational time necessary for the sensitivity, within the linear concentration

range, to decrease by a factor of 10” 27

To determine the lifetime in storage, one must compare the sensitivities of different sensors

from the same production method after different storage times under identical conditions.

2.9.1 Potassium sensors

The last decade has seen researchers and scientists focusing on the development of solid state

potassium ion selective electrodes with conducting polymers as the material for solid contact.

There are 2 main types of conducting polymers that have been studied, polypyrroles and

polythiophenes. Based on these groups, different methods of fabrication have been tested with

37

specific selective membranes. For the purposes of this review we will consider the most

important sensor characteristics to be stability, potassium selectivity and response time. A

stable potential is crucial for tests that require the electrode to stay in contact with the sample

for long periods of time. Potential stability is particularly important for reproducibility without

frequent calibration and response time is important for ease of use and commercialisation. For

biological fluids such as sweat, selectivity to other ions such as sodium and chloride must be

considered also. Due to the fact that most modern sensors have a lower limit of detection far

lower than typical levels in biological samples we shall consider this characteristic of little

importance. The required ranges for potassium ions in common biological samples are given

in Table 1. 28

The required operating range for potassium ions in sweat is 2.5-6 mmol L-1. This means an ion

selective electrode must be able to measure potential approximately between -50 mV and -100

mV in order to detect the ion in a sample of sweat.

Specimen Reference Range (mmol L-1)

(normal adult range)

Analytical Range (mmol L-1)

(required operating range to

measure values relevant to

clinical practice)

Saliva 10.9 – 25.5 2 – 25

Sweat 4 – 7 2.5 – 6 6

Blood 3.5 – 5.1 2 – 10

Urine 25 – 125 5 – 170

Table 1: Required ranges for potassium ions in common biological fluids

An overview of relevant research into solid-contact potassium ion-selective membrane

electrodes is given in the Table 2. The majority of research into potassium selective membranes

utilises Valinomycin as an ionophore and PVC as a base for the membrane. The focus on

potassium selective membranes for this project will also be based around Valinomycin and

PVC solutions. Their effectiveness as reagents is proved by their popularity with other research

groups.

38

2.9.2 Solid contact potassium selective electrodes – A Review Table 28

Table 2: Review of current solid contact potassium selective electrode research

Internal

Contact

Selective

Membrane

Sensitivity

(mV/decade)

[lin range M]

Baseline Drift Selectivity

[interfering ion]

Response

Time (s)

Authors

PPy/FeCN Va, PVC, DOS 59.1 ± 0.8

[N.S.]

-2.0 mV/h (0.03

ideally*)

Slight O2

interference

N.S. Gyurcs´anyi et

al.29

PEDOT/

PSS

Va, PVC, DOP 39

[10−2.5 − 10−1]

N.S -2.3 [Na+],

FIM

N.S. Odijk et al.30

CB:Gr

(3:1)

Va, PVC,

KTpCIBP, o-NPOE

59.1 ± 0.02

[10−6.5 − 10−1]

<1 µV/h

(50 hours)

Insensitive to O2,

CO2 and light

N.S. Paczosa-

Bator31

PPy/CbD PVC/ ocac 51 ± 2

[10−6 − 10−1]

N.S.

(only stable

slope)

-2 [Na+],

MPM

t95 < 14.2 Zine et al.32

PPy/TPB PVC/ dbc N.S.

[10−3 − 10−1]

1 mV/day -1.39 [Na+],

-2.76 [Ca2+],

FIM

< 5 Pandey et al.33

PEDOT/

PSS

Va, PVC, DOS 56.2 ± 0.7

[10−4 − 10−1]

N.S. N.S. N.S. V´azquez et

al.34

POT MMA-DMA/ocac 59.2

[10−7 − N.S.]

N.S. (2.7 mV/h

for Ag-ISE)

N.S. N.S. Chumbimuni-

Torres

et al.35

PEDOT/

PSS

Va, PVC, DOS 58.8 ± 0.8

[10−6 − 10−1]

N.S −5.3 ± 0.06 [Mg2+],

−5.8 ± 0.06 [Ca2+],

−5.5 ± 0.07 [Ba2+]

N.S Michalska and

Maksymiuk36

Gc CwNT, Va, PVC 51.9 ± 0.6

[10−6 − 10−1]

SD of 4.4 mV −3.6 [Na+],

−3.6 [Ca2+],

N.S. Mousavi et al.37

CwNT,

ODA

Va, nBA 57.2 ± 1.2

[10−6 − 10−2]

0.19 mV/h

(24 hours)

−5.0 ± 0.1 [Na+],

−2.0 ± 0.1 [NH+4],

−5.7 ± 0.1 [Ca2+]

< 10 Rius-Ruiz et

al.38

Cc Va, PVC, DOS 59.9 ± 0.7

[10−5 − 10−1]

0.36 mV/day

(42 days)

N.S N.S Mattinen et

al.39

Gr Va, PVC, DOS 60.0 ± 1.8

[10−7 − 10−1]

SD of 4 mV

(3 weeks)

−3.5 ± 0.2 [Na+],

−3.7 ± 0.3 [Ca2+]

N.S. Jaworska et

al.40

FCB Va, PVC, DOP 59.9 ± 1.0

[10−6 − 10−1]

0.11 mV/h N.S. 4.2 Ivanova et al. 41

39

N.S.: not specified. PPy: polypyrrole,

POT: polythiophenes PEDOT: polythiophenes

FeCN: hexacyanoferrate, PSS: poly(sodium4-styrenesulfonate),

TPB: tetraphenylborate, SSA: 1-Hydroxy-4-sulfobenzoic acid,

CwNT: carbon walled nanotubes, ODA: octadecylamine,

Cc: carbon cloth, Gr: graphene,

Gc: glassy carbon, (F)CB: (fullerene) carbon black,

El-21: resin, CIM: Colloid-Imprinted mesoporous carbon,

Cobalt: salt of [Co(phen)3](T P F P B)2), CbD: cobaltabis(dicarbollide),

Va: valinomycin, PVC: polyvinylchloride,

DOS: Dioctyl sebacate, nBA: nbutyl acrylate,

dbc: dibenzo-18-crown-6, SD: standard deviation,

ocac: 1,3-(di-4-oxabutanol)-calix[4]arene-crown-5,

MMA-DMA: metyl methacrylate-decyl methacrylate,

KTpCIBP: potassium tetrakis(4-chlorophenyl) borate,

o-NPOE: o-nitrophenyl octyl ether, SSM: separate solution method,

FIM: fixed interference method (interferent of 10mM),

DOP: bis(2-ethylhexyl)phthalate, MPM: matched potential method. 28

*After 1-year storage and extensive conditioning procedures.

**All relevant cations in blood were accurately determined by the sensor, with a commercial

electrolyte analyser as reference.

Legend for Table 2

40

2.9.3 Sodium sensors

Specimen Reference Range (mmol L-1)

(normal adult range)

Analytical Range (mmol L-1)

(required operating range to

measure values relevant to

clinical practice)

Saliva 2 – 21 42 1 – 25

Sweat 20 – 100 6 10 – 120

Blood 135 – 145 24 125 – 150

Urine <30 24 20 – 40

Table 3: Required ranges for sodium ions in common biological fluids

The required operating range for sodium ions in sweat is 20-100 mmol L-1. This means an ion

selective electrode must be able to measure potential approximately between 40 mV and 90

mV in order to detect the ion in a sample of sweat.

As mentioned above in the potassium review, we will consider the most important sensor

characteristics to be stability, potassium selectivity and response time. The overall

concentration of sodium in sweat is higher than that of potassium so the detection limit is also

much higher. The total concentration of sodium ions in serum is also directly related to the

condition hyponatremia so focus on this ion in particular is very important.

41

2.9.4 Solid contact sodium selective electrodes – A Review Table

Table 4: Review of current solid contact sodium selective electrode research

PUR: Polyurathane, PB: Prussian Blue

CCF: commercial carbon fibres TCNQ: 7,7,8,8-tetracyanoquinodimethane

MWCNT: Multi-walled carbon nanotubes NaxWO3: Sodium-tungstan-bronze

AuNPs: Gold nanoparticles Na-IV: sodium ionophore IV

LAc: Lipoic acid LAm: Lipoic amide

PEDOT: polythiophenes PSS: poly(sodium4-styrenesulfonate),

HHCAE: hydrophilic high-capacity anion-exchange membrane (fumion FAA-3 ionomer)

Na-X: 4-tert-Butylcalix[4]arene-tetraaceticacid tetraethyl ester

N.S: not specified

Legend for Table 4

Internal

Contact

Selective

Membrane

Sensitivity

(mV/decade)

[lin range M]

Baseline Drift Selectivity

[interfering ion]

Response

Time (s)

Authors

PUR

Ag/AgCl-

Paper

Va, (HHCAE) 56.6 ± 1.0

[10-0.7 - 10-3.1]

N.S. N.S. N.S. Hu et al. 43

CCF/

MWCNT

Na-X 59.2 ± 0.6

[10-3 - 10-1]

-0.4 ± 0.3 mV/h

(4.5 hours)

-2.3 [K+]

-2.3 [Mg2+]

-2.5 [Li+]

-2.6 [Ca2+]

N.S. Parrilla

et al. 44

PEDOT/PB Na-X/PVC 55.5

[10-5 - 10-1]

-0.04 ± 0.01

mV/min

(4 hours)

N.S. N.S. Matzeu

et al. 45

NaxWO3 HCl 54.6 ± 0.6

[N.S.]

-0.1 mV/min -8.2 [Na+]

-8.7 [K+]

-8.7 [Ca2+]

-8.1 [Mg2+]

40 Cisternas

et al. 46

AuNPs/LAc-

LAm

Na-X/PVC 33.23 ± 2.5

[10-5 - 10-2.5]

0.02 ± 0.008

mV/min

Removed by

preconditioning

N.S. Matzeu

et al. 47

TCNQ Na-IV/PVC 58.68

[10-6 - 10-1]

9.1 ± 1.1 µV/h

(172 hours)

-3.0 [K+]

-3.2 [NH4+]

-3.9 [Ca2+]

-4.1 [Mg2+]

4-5 Paczosa-

Bator et

al. 48

PEDOT/PSS Na-X/PVC 57.59 ± 1.47

[10-1 - 10-6]

5 µV

(14 hours)

-3.7 [K+]

-1.4 [Ca2+]

N.S. Jasinski

et al. 49

42

Summary

At imec, K+ and Na+ selective sensors were developed due to their physiological importance in

the diagnosis of various conditions such as hyponatremia and dehydration. In earlier study, the

ionophores Valinomycin and Na ionophore IV were chosen for the potassium and sodium ion-

selective membranes due to their high selectivity for K+ and Na+, respectively. Plasticized PVC

was selected for immobilization of the ionophores due to its ability to create thin, permeable

films which allow for sufficient mobility for ionic species. Composition of the PVC based

potassium and sodium selective membranes and their thickness were successfully optimized to

achieve the sensitivity close to the Nernstian value for both potassium and sodium ions [V.A.T.

Dam, M. Zevenbergen, P. van Schaijk, Flexible ion sensors for bodily fluids, Proceeding

Eurosensors XXX, September 2016].

The objective of this project is to investigate the lifetime of PVC-based ion selective electrodes

through continuous and non-continuous measurement of the electrode potential. Siloprene will

also be investigated as a base for an ion-selective membrane without a plasticizer. The

formulation of the ion selective membrane solutions will also be assessed further and optimised

to give the best sensitivity and response.

Ultimately the information gained through this project will be used in the implementation of

the ion-selective electrodes onto a wearable sensing platform to analyse human sweat. The goal

of the gas and ion sensors group is to create a non-invasive, easy to use and cheap wearable

device in the form of a skin patch that can measure multiple ions at the same time from sweat

collected on the skin. This information may be of great use to clinical professionals or

individuals with a keen interest in sports and exercise.

43

Chapter 3: Experimental Materials and Methods

3.1 Formulation of the ion-selective membrane

3.1.1 Stock Solutions

(1) General Stock Solution: The concentration of the ionophore in the membrane solution

is relatively low, but the initial stock solution prepared in an appropriate solvent contains a

high concentration of ionophore. The high concentration of ionophore reduces the error of

weighing out specific amounts of solid. The stock solution is then diluted to the desired

concentration for the membrane solution. In addition, KTBC is added to the membrane stock

solution to provide hydrophobic anions that electrically neutralize the positive charge on the

ionophore when the ion of interest binds.

Potassium Stock Solution (PVC) Ratio

Valinomycin (mg) 22.8

KTBC (tetrakis(4-chlorophenyl) borate) (mg) 4.4±1

Cyclohexanone (mL) 0.55

Sodium Stock Solution (PVC) Ratio

Na ionophore IV 20.9

KTBC (tetrakis(4-chlorophenyl) borate) (mg) 10.2

Cyclohexanone (mL) 1.24

Potassium Stock Solution (Siloprene) Ratio

Valinomycin (mg) 20.8

KTBC (tetrakis(4-chlorophenyl) borate) (mg) 4.03

Dichloromethane (CH2Cl2) (mL) 0.50

Sodium Stock Solution (Siloprene) Ratio

Na ionophore IV 26.1

KTBC (tetrakis(4-chlorophenyl) borate) (mg) 12.5

Dichloromethane (CH2Cl2) (mL) 1.50

44

Figure 7: Valinomycin Figure 8: Sodium Ionophore IV

Figure 9: KTBC (tetrakis(4-chlorophenyl) borate)

3.1.2 Ion-selective membranes

(2) PVC ion-selective membrane: Polyvinyl Chloride (50 mg,) was dissolved in

Cyclohexanone (625 µL) and stirred continuously for 2 hours. Di(2-ethylhexyl) sebacate

(112.8 µL) was added and the solution was stirred continuously for 10 minutes. The stock

solution (125 µL) was added via a micropipette and the solution was stirred for 10 minutes.

The ion selective membrane is kept in the fridge because the ionophores needs to be stored at

temperatures under 20 oC.

45

(3) Siloprene ion-selective membrane: Siloprene K1000 (100 mg,) and the siloprene

crosslinking agent (10 mg) were weighed out on an electronic balance by careful use of a

micropipette and dissolved in CH2Cl2 (874 µL) and stirred continuously for 30 mins. The stock

solution (126 µL) was added via micropipette and the solution was stirred continuously for 10

minutes. The ion selective membrane is kept in the fridge because the ionophores needs to be

stored at temperatures under 20 oC.

Ion-selective Membrane (PVC) Ratio

Stock Solution (µL) 125

Polyvinyl Chloride (PVC) (mg) 50

(DOS) (µL) 113

Cyclohexanone (µL) 625

Ion-selective Membrane (Siloprene) Ratio

Stock Solution (µL) 126

Siloprene K1000 (mg) 100

Siloprene crosslinking agent (mg) 10

Dichloromethane (CH2Cl2) (µL) 874

Figure 10: Polyvinyl Chloride (PVC)

Figure 11: Di(2-ethylhexyl) sebacate (DOS)

46

3.1.3 Hydrogel

(4) Hydrogel-1 (2% HEC, 0.1 M KCl in DI water). Potassium Chloride (48.4 mg) was

dissolved in deionized water (5 mL). Triethylene glycol (1.85 mL) was added and the solution

was stirred continuously and heated at 45-50 oC for 40 minutes. Hydroxyethyl cellulose (196

mg) was added and the mixture was stirred continuously for 1 hour until the solution became

very viscous.

Hydrogel-1 (2% HEC, 0.1 M KCl in DI water) Ratio

Potassium Chloride (mg) 48.4

Hydroxyethyl cellulose (HEC) (mg) 196

Triethylene glycol (TEG) (mL) 1.85

Deionized Water (mL) 5

Figure 12: Hydroxyethyl cellulose (HEC)

Figure 13: Triethylene glycol (TEG)

47

3.2 Ion-selective electrode miniaturisation

Figure 14: Miniaturisation of a conventional ISE onto a flexible substrate

48

The miniaturisation of a conventional ion-selective electrode into a system that operates on a

flexible substrate is a process that replaces certain aspects of the sensor with more convenient

alternatives. The internal electrolyte is replaced by a HEC hydrogel that has been doped with

KCl solution and the membrane is replaced by an ion-selective membrane solution that is

allowed to dry on the hydrogel. The two phases do not mix so a phase boundary still remains

between an aqueous layer and a hydrophobic layer.

Once a stock solution has been made, a membrane solution can be made in less than an hour.

This paired with the fact that the ion selective membranes are high customizable means that

many different and unique sensor samples can be made in one day.

3.3 Drop-casting method

Figure 15: Layers are drop-cast onto the flexible substrate

49

1. The screen-printed electrode on a flexible substrate was selected and the amount of

reagent to be deposited was calculated based on the diameter of the well on the

substrate. The diameter was either 2 mm or 3 mm.

2. Using a micro pipette, the hydrogel was deposited into the well to fill approximately

80% of the total volume and ensuring the Ag/AgCl electrode is fully covered by the

gel. The gel was then left for 20 min to settle while ensuring it does not fully dry out.

3. The ion selective membrane was then deposited on top of the hydrogel layer ensuring

it is completely covered. By using the end of the pipette tip the membrane was spread

over the rim of the surface. The sensor was stored in humid conditions to prevent the

hydrogel layer from drying out fully. The amount of membrane solution deposited and

the number of membrane layers was dependant on the solvent used when making the

membrane solution.

It should be noted that the drop-casting method for a siloprene-based membrane was very

temperamental due to the volatile solvent dichloromethane. The solvent was observed to

evaporate very quickly so the spreading step was carried out hastily.

3.4 Flexible sensor stick

Figure 16: Schematic of the flexible sensor stick with 4 electrode sites

Each printed Ag/AgCl electrode may by modified with ion-selective membranes for potassium

or sodium sensing or with the hydrogel solution for an integrated reference electrode. The

electrodes are screen printed on a DEK HORIZON 03i printer from silver chloride conducting

paste and the material on which the electrodes are printed on is thermoplastic polyurethane.

50

Each Ag/AgCl electrode has a diameter of 1 mm and is contained within a well of 2 or 3 mm

diameters.

3.5 Sensor Calibration

Figure 17: Diagram of the general set up of a calibration measurement

The sensors were calibrated by measuring the potential of the ion-selective electrode against a

commercial reference electrode (CRISON 50 44) in a series of standard solutions. Each

electrode on the sensor stick was connected to a channel and fed through a multiplexing unit

that compressed the channels into a single signal that is processed by a voltmeter. The software

used to analyse the potentiometric data was LabVIEW. One calibration test for a single sensor

took 40 mins to 1 hour to finish. The data from the test was processed and plotted using

Microsoft Office Excel 2016.

51

Chapter 4: Results and Discussions

4.1 Characterisation of the sensor

The performance of the flexible ion selective membrane was evaluated by measuring the

potential difference between the ion selective electrode and reference electrode upon changes

of activity of the analyte ion by using a series of standard solutions of varied concentrations

from 1 – 10-4 M. It should be noted that the reference electrode was not conditioned before

starting the calibration procedures. During non-use, the reference electrode was submerged in

KCl solution (3 M).

A calibration of the flexible sensor was performed by measuring the EMF versus time in

seconds and changing the activity of the analyte ion by swapping out standard solutions of

differing concentrations. During the changing period, the reference electrode and membranes

were washed with de-ionised water and patted down with standard lab tissue. At no point was

the surface of the membrane allowed to touch the reference electrode or the inside of the beaker

that contained the standard solution.

The sensor in Graph 1 demonstrated a response time of about 10 seconds. This sensor contains

4 ion selective electrodes which all show a linear range from 10-4 M to 1 M KCl and display

Nernstian behaviour of about 54 mV per decade of KCl concentration (see Graph 1 and Graph

2). This response is appropriate for monitoring physiological parameters in sweat during

exercise. Potassium concentration during sweating progressively decreases when the body

begins to burn proteins instead of carbohydrates due to the depletion of sugars in the transition

from aerobic to anaerobic states.

52

Graph 1: OP-13 calibration series

Graph 2: OP-13 calibration slopes

-200

-150

-100

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0

50

100

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Po

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(m

V)

Time (s)

OP-13 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm]

Device 1

Device 2

Device 3

Device 4

-200

-150

-100

-50

0

50

100

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

Ave

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Po

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(m

V)

Log10[KCl]

OP-13 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm]

Device 1

Device 2

Device 3

Device 4

53

Device 1 y = 56.587x + 46.1 R² = 0.9996

Device 2 y = 53.012x + 46.034 R² = 0.9995

Device 3 y = 53.331x + 54.689 R² = 0.9997

Device 4 y = 55.9x + 41.83 R² = 0.9997

Table 5: OP-13 Calibration slopes and R values

This ranges covers the typical levels of potassium in sweat which fall within 0.2 to 6 mM.50 As

seen from the figure above, the sensor was able to function for over 4000s (approx. 66 min)

and maintains a reproducible level of response. This timeframe is ideal for a general physical

workout in which a substantial amount of sweat can be produced to be detected by the sensor.

The selectivity of the sensor was achieved by functionalizing the electrode with an ion-selective

membrane that is doped with an ionophore molecule specific to the analyte ion to be measured.

In the case of potassium sensing the ionophore of choice was Valinomycin. By utilizing this

ion-selective membrane we are able to avoid any interferences that might occur from other

cations that are present in sweat such as Na+ and NH4+.

The repeatability and reproducibility of the response of a single sensor was evaluated by

performing several calibration plots from each series. The following data shows the statistical

accuracy of each device relative to the Nernstian value at room temperature, 58.5 mV/decade:

Graph 3: OP-13 device 1

Series 1y = 54.664x + 39.892

R² = 0.9998

Series 2y = 56.294x + 45.777

R² = 0.9996

Series 3y = 56.459x + 45.79

R² = 0.9996

Series 4y = 58.517x + 52.006

R² = 0.9986-200

-150

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0

50

100

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5

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(m

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Log10[KCl]

OP-13 Device 1 All Series

Device 1 Series 1

Device 1 Series 2

Device 1 Series 3

Device 1 Series 4

54

Graph 4: OP-13 device 2

Graph 5: OP-13 device 3

Series 1y = 52.056x + 42.804

R² = 0.9998

Series 2y = 53.481x + 47.229

R² = 0.9994

Series 3y = 53.266x + 46.734

R² = 0.9993

Series 4y = 53.45x + 47.983

R² = 0.9995

-200

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0

50

100

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5

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(m

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Log10[KCl]

OP-13 Device 2 All Series

Device 2 series 1

Device 2 series 2

Device 2 series 3

Device 2 series 4

Series 1y = 52.923x + 52.839

R² = 0.9999

Series 2y = 54.159x + 56.153

R² = 0.9996

Series 3y = 53.51x + 56.049

R² = 0.9994

Series 4y = 53.047x + 54.661

R² = 0.9995-200

-150

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-50

0

50

100

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5

Ave

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(m

V)

Log10[KCl]

OP-13 Device 3 All Series

Device 3 series 1

Device 3 series 2

Device 3 series 3

Device 3 series 4

55

Graph 6: OP-13 device 4

Slopes Device 1 Device 2 Device 3 Device 4

Series 1 54.644 52.056 52.923 55.38

Series 2 56.294 53.481 54.159 56.782

Series 3 56.459 53.266 53.51 56.348

Series 4 58.517 53.45 53.047 55.617

Table 6: OP-13 all series slopes

Table 7: all series errors

Percentage Error Device 1 Device 2 Device 3 Device 4

Series 1 6.591453 11.01538 9.533333 5.333333

Series 2 3.77094 8.579487 7.420513 2.936752

Series 3 3.488889 8.947009 8.529915 3.678632

Series 4 0.02906 8.632479 9.321368 4.928205

Table 8: OP-13 all series percentage errors

Error Device 1 Device 2 Device 3 Device 4

Series 1 -3.856 -6.444 -5.577 -3.12

Series 2 -2.206 -5.019 -4.341 -1.718

Series 3 -2.041 -5.234 -4.99 -2.152

Series 4 0.017 -5.05 -5.453 -2.883

Average -2.0215 -5.43675 -5.09025 -2.46825

Series 1y = 55.38x + 40.178

R² = 0.9999

Series 2y = 56.782x + 43.911

R² = 0.9997

Series 3y = 56.348x + 43.402

R² = 0.9996

Series 4y = 55.617x + 41.416

R² = 0.9993-200

-150

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-50

0

50

100

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5

Ave

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(m

V)

Log10[KCl]

OP-13 Device 4 All Series

Device 4 series 1

Device 4 series 2

Device 4 series 3

Device 4 series 4

56

Error Deviation Device 1 Device 2 Device 3 Device 4

Series 1 1.8345 4.4225 3.5555 1.0985

Series 2 0.1845 2.9975 2.3195 0.3035

Series 3 0.0195 3.2125 2.9685 0.1305

Series 4 2.0385 3.0285 3.4315 0.8615

Table 9: OP-13 all series error deviations

Percent Error Deviation Device 1 Device 2 Device 3 Device 4

Series 1 3.135897 7.559829 6.077778 1.877778

Series 2 0.315385 5.123932 3.964957 0.518803

Series 3 0.033333 5.491453 5.074359 0.223077

Series 4 3.484615 5.176923 5.865812 1.47265

Table 10: OP-13 all series percentage error deviations

The average results for 4 series yielded slope values of 56.587 mV/decade (Device 1), 53.012

mV/decade (Device 2), 53.331 mV/decade (Device 3) and 55.9 mV/decade (Device 4) (3.29%

RSD) and intercept values of 46.1 mV (Device1), 46.034 mV (Device 2), 54.689 mV (Device

3) and 41.83 mV (Device 4) (11.45% RSD). Each sensor that was tested was characterised and

calibrated to the same degree and standards.

From a practical and analytical perspective, these values are evidence of some limitations and

some of the most attractive features of potentiometry in real life scenarios. One of the features

of this technique is the Nernstian Response, the constant value of the sensitivity. For large scale

determinations outside the ideal conditions of the lab, a method that possess a known sensitivity

and is virtually independent of operational parameters can reduce the functional complexity

and minimize the uncertainty of results. However, considerations such as temperature and

pressure should be focused on in the future when developing for real life applications. A change

in temperature from lab a lab environment (298 K) to human body temperature (310 K) should

give a slope variation of about 4% according to the Nernst Equation.

One of the limitations of this current system is the variability in the intercept. A common

problem for wearable potentiometric devices is the need for constant and regular calibration.

Rius-Ruiz et al demonstrated that a one-point calibration procedure for a quick, decentralized

measurement of K+ ions in saliva was sufficient to provide reliable results. 38 Whether this

calibration can be performed at the final stages of manufacture and can be reliably used later

depends on the shelf life of the sensor.

57

4.2 Continuous measurement of the sensor

4.2.1 3.8 days

Graph 7: OP-24 continuous measurement over 4 days

Device 1 Device 2 Device 3

Formula of line y = 0.141x + 33.66 y = 0.133x + 34.16 y = 0.144x + 32.94

R2 value R² = 0.964 R² = 0.945 R² = 0.974

Rate of drift 0.141 mV/hour 0.133 mV/hour 0.144 mV/hour

Total drift 14.53 mV 13.7 mV 11.65 mV

Table 11: OP-24 all devices rate of drifts

A PVC-based sodium sensor with 3 channels (3 mm diameter) and 4 layers (3 µL each) was

tested on its lifetime by measuring the EMF upon changes of activity of the analyte ion by

submerging it in NaCl solution (0.01 M) for 4 days. Graph 7 shows the steady drift in voltage

over time. The cause of the noise that is observed at 75 hours remains unexplained but it is

likely due to movement of equipment and disturbing the sample solution. The sensitivity of the

sensor was evaluated before and after the 4-day continuous test, the results are displayed in

Graph 8 and Graph 9.

58

Before:

Graph 8: OP-24 before continuous measurement

After:

Graph 9: OP-24 after continuous measurement

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Log10[NaCl]

OP-24 Na+ sensor PVC/Cyclohexanone Calibration 1 - 4 x 3 μL Layers

Device 1

Device 2

Device 3

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60

80

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160

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Log10[NaCl]

OP-24 Na+ sensor PVC/Cyclohexanone Calibration 2 - 4 x 3 μL Layers

Device 1

Device 2

Device 3

59

91.5 hours Sensitivity Before

(mV/decade[Na+])

Sensitivity After

(mV/decade[Na+])

Device 1 54.15 45.59

Device 2 52.26 47.81

Device 3 51.87 49.14

Table 12: OP-24 before and after continuous measurement slopes

The calibration graphs show that the sensitivity of the devices still remain within the range of

detection for sodium ions in human sweat (20-100 mmol/L). However, the value of the

sensitivity factors and the offset after the continuous measurement indicate that the devices are

not as accurate as they appear to be.

The values for the sensitivity of the devices before the continuous test are 54.15

mV/decade[Na+] (Device 1), 52.26 mV/decade[Na+] (Device 2) and 51.87 mV/decade[Na+]

(Device 3) (0.99% RSD) and after the continuous test are 45.59 mV/decade[Na+] (Device 1),

47.81 mV/decade[Na+] (Device 2) and 49.14 mV/decade[Na+] (Device 3) (1.46% RSD). The

relative standard deviation of the sensitivity values before and after are 4.28% (Device 1),

2.23% (Device 2) and 1.36% (Device 3).

The offset values for the devices before continuous testing are 142.27 (Device 1), 135.22

(Device 2) and 134.54 (Device 3) (3.49% RSD) and after continuous testing are 124.61 (Device

1), 131.98 (Device 2) and 135.28 (Device 3) (4.46% RSD). The relative standard deviation of

the offset values before and after continuous testing are 8.83% (Device 1), 1.62% (Device 2)

and 0.37% (Device 3).

From Table 15 it is seen that there is a small but definite decrease in sensitivity following the

continuous submersion of the sensor in the sample solution. The percentage decrease of

sensitivity for each device are 15.8% (Device 1), 8.5% (Device 2) and 5.3% (Device 3).

The average rate of drift for the sensor is 0.14 mV/hour. There are several causes to the

potential drift; the solvent from the sample solution evaporating and increasing the

concentration of the overall solution, liquid seeping into a small gap between the membrane

and the hydrogel and the plasticizer leaching out of the membrane. The first two causes can be

avoided by perfecting the preparation and testing methods, ensuring the membrane forms a

60

tight seal over the hydrogel during dropcasting and completely covering the sample solution

with aluminium foil during the continuous test to avoid evaporation of the volatile solvent. The

plasticizer is a component in PVC-based ISEs that has undergone much evaluation. 51

A challenge facing the application of PVC-based ISEs in real samples is the deterioration of

membrane performance over time. The primary reason for this deterioration is the leaching of

plasticizer components into the sample, which impact the lifetime of PVC materials.

The use of highly lipophilic plasticizers is a way to overcome these challenges. Incorporating

long alkyl chain in plasticizers increases their lipophilicity which has a direct impact on the

retention of the components of the membrane.

The sensor is still functional but the drift in data must be accounted for in order to achieve the

same level of accuracy as when it was first created. Continuous measurement is not an accurate

simulation of how the sensors will ultimately function but it provides valuable insight into the

stability of the sensor. The results cannot be interpreted as a direct comparison for the

functionality as a wearable device because conditions such as temperature, physical strain and

compatibility are all ideal in a lab setting. This set of data is relatively novel as there is little in

the literature that present experiments involving continuous measurements of an ion-selective

membrane based sensor. With this in mind, there is still much to be studied regarding this

particular method, a full description of potential future work will be discussed in Chapter 5.

61

4.2.2 11.8 days

Graph 10: OP-4 continuous measurement over 12 days

Table 13: OP-4 all devices rate of drifts

A PVC-based potassium sensor with 3 channels (3 mm diameter) and 4 layers (3 µL each) was

tested on its lifetime by measuring the EMF upon changes of activity of the analyte ion by

submerging it in KCl solution (0.01 M) for 12 days. Graph 10 shows the steady drift in voltage

over time. Note that devices 3 and 4 broke on day 8 and device 2 broke halfway through day

9. The manner in which these devices broke was by dislodgement of the ion-selective

membrane from the flexible sensor stick.

The sensor was calibrated before the continuous measurement but due to the membranes

breaking off the flexible substrate, it was deemed unnecessary to calibrate the sensor

afterwards. The sensitivity of the sensor stick was evaluated before the continuous

measurement and the results are shown in Graph 11.

Device 2 Device 3 Device 4

Formula of line y = 4.60x + 5.86 y = 4.62x + 1.31 y = 4.61x + 5.29

R2 value R² = 0.986 R² = 0.988 R² = 0.970

Rate of drift 4.60 mV/day 4.62 mV/day 4.61 mV/day

Total drift 47.90 mV 47.55 mV 46.65 mV

62

Graph 11: OP-4 calibration before continuous measurement

Calibration Before Slope

Device 2 55.78

Device 3 55.52

Device 4 55.81

Table 14: OP-4 calibration before continuous measurement

The average rate of drift for this sensor was 0.19 mV/hour. This value is comparably similar to

the average rate of drift for the continuous measurement over 3.8 days by 21.43% RSD. One

must take into account that this similarity is only valid for the first 8 days, as seen in Graph 10,

the potentials beyond that point are not applicable due to the sensor breaking.

Despite being formulated for the intent of measuring different ions, both membrane solutions

for OP-4 and OP-24 are the exact same. The use of a different ionophores may have affected

the overall rate of drift for either sensor as the values are significantly different by 21.43%

RSD. Valinomycin exhibits the ability to selectively bind to K+ ions over Na+ ions for

spontaneous transfer by use of a ring system. Sodium Ionophore IV uses the same mechanism

for Na+ only but it is unknown if it affects K+ in the same way as there is very little information

on 2,3:11,12-DIDECALINO-16-CROWN-5. It was observed that the limit of detection for

sodium ions was below 0.001 M NaCl, any concentration lower and the potential began to

plateau. The potassium membrane was able to detect K+ concentration below 0.001 M KCl

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with no plateau. This difference in calibration range could be the reason why the potassium

sensor was seen to drift at a faster rate than the sodium sensor. The calibration graphs are

objectively different as the potassium sensor has 5 data points and the sodium sensor has only

4 due to the aforementioned plateau behaviour. Further study of the difference between the 2

ionophores is required to come to definitive conclusions.

4.3 Non-continuous measurement of the sensor

4.3.1 Sensitivity over 42 days

Graph 12: OP-3 non-continuous measurement slopes(sensitivities)

OP-3 Slopes Device 2 Device 3 Device 4

31-05-16 56.21 56.41 56.72

08-06-16 56.78 56.78 56.21

13-06-16 57.22 57.06 56.63

20-06-16 50.15 49.75 -

27-06-16 45.37 47.72 48.70

05-07-16 42.60 41.12 42.96

12-07-16 36.38 32.45 30.1

Table 15: OP-3 all devices slopes over 6 weeks

A PVC-based potassium sensor with 3 channels (3 mm diameter) and 4 layers (3 µL each) was

calibrated 7 times over 42 days. During the period in which the sensors were not in use, they

were stored in humid conditions to prevent the membrane from drying out completely. The

0

10

20

30

40

50

60

70

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

Sen

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Number of Days

OP-3 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm]

Device 2

Device 3

Device 4

64

calibration procedure was the same as described in section 4.1 and the standard sample

solutions were freshly prepared for each test. Graph 12 shows the average slopes of each test

per day, the sensitivity of each device is seen to decrease as time passes. On the 4th test, device

4 was unresponsive but it returned to normalcy on the next test.

The general decrease in sensitivity during storage could be due to the leaching of the plasticizer

out of the membrane despite the sensor not being continuously submerged in a sample solution.

It is possible that the humid and moist conditions in which the sensor is stored draws out the

plasticizer through its polar groups. This further justifies the use of highly lipophilic plasticizers

which will increase retention within the solution.

Another possible explanation for the decrease in sensitivity could be that the action of

measuring the sensor itself contributes to the degradation. It was observed on day 35 that the

metal contact that is connected to the voltmeter showed signs of wear. It is unlikely that this is

the core reason for the potential drift however as this type of physical damage will affect the

signal and noise of the calibration graph.

The sensitivities of each device on each day are shown in Graphs 13, 14 and 15. The graphs

show the large difference in each day and the change that occurs with each calibration. From

Graph 13, Graph 14 and Graph 15 a pattern is observed from the slope values and the offset

values. The slope values for each device generally remain the same for the 1st 3 tests and then

they start to decrease numerically and the trendlines gradually get flatter. The offset values are

seen to drastically increase on the 2nd and 3rd tests causing the position of the trendline to be

higher on the graph. After the 3rd test, the offset values decrease numerically and the positions

of the trendlines shift down the vertical axis.

65

4.3.2 Device 2 sensitivity

Graph 13: OP-3 device 2 all tests

Day Slope Offset

1 56.21 65.61

9 56.78 100.27

14 57.22 110.56

21 50.15 93.52

28 45.37 75.34

35 42.59 51.82

42 36.38 29.87

Table 16: OP-3 device 2 slopes and offsets

Slope RSD = 16.47%

Offset RSD = 37.99%

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OP-3 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm] Device 2 All Tests

Device 2 Test 1

Device 2 Test 2

Device 2 Test 3

Device 2 Test 4

Device 2 Test 5

Device 2 Test 6

Device 2 Test 7

66

4.3.3 Device 3 Sensitivity

Graph 14: OP-3 device 3 all tests

Day Slope Offset

1 56.41 66.59

9 56.78 102.13

14 57.06 109.19

21 49.75 87.12

28 47.72 64.95

35 41.12 33.99

42 32.45 29.54

Table 17: OP-3 device 3 slopes and offsets

Slope RSD = 19.06%

Offset RSD = 44.22%

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OP-3 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm] Device 3 All Tests

Device 3 Test 1

Device 3 Test 2

Device 3 Test 3

Device 3 Test 4

Device 3 Test 5

Device 3 Test 6

Device 3 Test 7

67

4.3.4 Device 4 Sensitivity

Graph 15: OP-3 device 4 all tests

Day Slope Offset

1 56.716 69.234

9 56.21 95.918

14 56.63 103.03

28 48.692 60.67

35 42.966 46.326

42 30.1 29.514

Table 18: OP-3 device 4 slopes and offsets

Slope RSD = 21.84%

Offset RSD = 41.99%

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OP-3 K+ sensor (PVC/cyclohexanone) 4 layers (4 x 3μL) [3mm] Device 4 All Tests

Device 4 Test 1

Device 4 Test 2

Device 4 Test 3

Device 4 Test 5

Device 4 Test 6

Device 4 Test 7

68

4.3.5 All Test Comparison

The following graphs further show the difference between each device with each test over the

weeks. The changes in offsets and slopes indicate that each device is deteriorating in relatively

the same way and the same rate.

Graph 16: OP-3 test 1

Graph 17: OP-3 test 2

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Test 1Device 2

Test 1Device 3

Test 1Device 4

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69

Graph 18: OP-3 test 3

Graph 19: OP-3 test 4

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Test 3Device 2

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70

Graph 20: OP-3 test 5

Graph 21: OP-3 test 6

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Test 5Device 2Test 5Device 3Test 5Device 4

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71

Graph 22: OP-3 test 7

4.4 Siloprene-based ion-selective membrane

Figure 18 and Figure 19 show a sodium sensitive Siloprene-based sensor with the following

concentration ratios:

Table 19 and 20: Ratios for the siloprene-based sensors

As seen in the figures the signal is very noisy and after a day of storage the sensitivity is lost

significantly. The sensor was drop-cast and measured on the same day, a total of 3 hours was

given between the casting and testing. Device 3 was the only device to display somewhat

Nernstian behaviour on day 1. From this data one can conclude that the correct thickness of

layers is 7.5 µL + 7.5 µL for maximum sensitivity, but the noise remains to be the same

throughout all devices. The optimised membrane composition ratio of 0.63:100 stock solution

to siloprene was developed alongside the ideal number of layers on the sensor.

Stock 0.3

Siloprene K1000 47.6

K11 4.76

CH2Cl2 0.652

Na Ionophore 26.06

KTBC 12.5

CH2Cl2 1.5

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72

4.4.1 Measurements within 1 day

Figure 18: OP-30 Na Sensor (siloprene/DCM) drop-cast and measured on the same day

Figure 19: OP-30 Na Sensor (siloprene/DCM) measured on the day after

73

The signal of the sensitivity graphs is significantly noisy due to the waiting time between drop-

cast. The time between casting was too long and therefore the membrane was not homogenous

and the conductivity was too high.

Clearly there needs to be further study in the use of siloprene-based membranes, there exists

many complications regarding the fabrication and formulation however. Both Siloprene K1000

and K11 crosslinking agent are volatile and evaporate at room temperature. This creates a

difficultly in accurately measuring out the correct ratios for the desired concentration. One must

work swiftly to accurately weigh a volatile liquid. Consequently, the solvent dichloromethane

is also very volatile. This allows for faster sensor fabrication as the solvent evaporates off the

stick almost instantly but it also creates a difficulty in accurately measuring the amount of

liquid used.

The advantage of using siloprene over PVC is that there is no need for a plasticizer as the regent

acts as one once combined with the crosslinking agent. However there still exists many

complications for the fabrication and much future work is needed to fairly accept or reject the

concept of a siloprene-based ion selective membrane.

74

Chapter 5: Conclusions and Future Work

5.1 Conclusion

The formulation of a PVC-based ion-selective membrane solution for its use on a flexible

substrate has been optimised and is successfully customisable for a specific ion of potassium

or sodium. Using this formula, a miniaturised electrode sensor stick was prepared and has been

shown to have reproducible and promising results with an ideal Nernstian response. This is a

step further to the implementation of ion-selective membranes on a flexible substrate for non-

invasive sweat analysis. The optimised formula will be functionalised onto a customisable

sensing platform such as a wearable skin patch, the mechanics of which will be discussed by

the gas and ion sensors group.

A single sensor device having PVC-based ion-selective electrodes for potassium and sodium

was able to give statistically accurate and reproducible data for the analysis of ion

concentrations within the time span of 1 hour. This time period is the ideal length for a single

athletic workout in which enough sweat can be produced from a human for analysis. This is a

Further step to the implementation of ion-selective membranes for non-invasive sweat analysis.

Upon establishing the method of development for the sensor device, further sensors were made

following the same procedure. The sensors are still unable to function correctly if they are

stored in dry conditions as the membrane dries out and sensitivity is lost.

The average lifetime of the sensor based off the continuous measurement tests was found to be

roughly 4 days. It was shown that the sensor was functioning at sub-Nernstian standards after

3.8 days of continuous measurement. After a period of 7 days the membrane layers began to

break and dislodge from the flexible substrate. The loss of sensitivity is unclear at the breaking

point but the whole sensor is rendered essentially non-functional. This result is in contrast to

that of the non-continuous measurement, which remained at sub-Nernstian levels in a span of

at least 5 weeks. The sensor was able to function correctly and accurately when tested once a

week for 5 weeks. For the commercial application of a wearable ion sensor, testing sweat ion

content once a week is an agreeable regime for health monitoring. This rate of usage is

particularly applicable to sports conscious individuals who attend training at least once a week.

75

5.2 Future Work

The continuous measurement tests were carried out with only one ion-selective membrane per

sensor stick. For the sensor to be ultimately incorporated onto a wearable sweat patch device,

it must be able to detect multiple ions at the same time. Therefore, for any future work it would

be advantageous to test the lifetime of multiple ion-selective membrane solutions at the same

time. The ideal number of layers and membrane composition has been established on their own,

so the next step will be to incorporate multiple sensing membranes onto one device. A further

pursuit would be to integrate a reference electrode as part of the overall sensor stick. This is

simply done by normally drop-casting the hydrogel in the electrode well but not covering it

with an ion-specific membrane solution. However, there will be some limitations to this

approach, particularly ensuring that the hydrogel does not dissolve when the stick is submerged

in a sample solution.

The fabrication of the sensors sticks in this project were done so by hand and precautions were

taken to make sure they were all made in the exact same way. However, the prospect of human

error is guaranteed. This can be avoided and regulated by using an automated machine system

for the drop-casting procedure. The automated system must be calibrated before each procedure

as the viscosity of the hydrogels and membrane solutions differ significantly. By using such a

system, one can track exactly how much solution is used in each of the 4 wells on a single stick.

Furthermore, it has the potential to produce sensor sticks on a greater scale which would allow

for more tests to be carried out.

The working lifetime and shelf life of the sensor are 2 of the most important factors of the

sensor. The next step in future studies would be to determine how to improve these factors.

This could be through changing the formulation of the ion-selective membrane, introducing an

external feature that prevents degradation or altering the overall size and construction of the

sensor stick. As outlined in Chapter 1, there has been many interesting developments in the

manufacture of a wearable device for ion analysis. 6 One feature of the “SWEATCH” device

that could be implemented in the future could be the use of capillary force as a method of

sample intake. It is a simple, yet effective method of collecting enough sample fluid for an

accurate and reproducible result and it would be interesting to see if it can be applied to the

imec sensor.

The siloprene-based sensors require more calibration tests and formulation changes in order to

create the optimal ratio for maximum sensitivity and signal strength. Only then can tests be

76

carried out to evaluate the lifetime and shelf life of the sensors created with these membrane

solutions. The main challenge lies with the reagents that are used to create the membrane

solution. The key chemicals are highly volatile and evaporate at room temperature. In order to

create a reproducible formula for the membrane solution, one must first develop a method that

ensures the reagents are handled in the correct manner. Perhaps an automated system similar

to that proposed for the drop-casting method. In any case, siloprene-based ion selective

membranes need to be studied in greater detail for future work.

77

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