Development of Cross-Reactive Sensors Array: Practical

DEVELOPMENT OF CROSS-REACTIVE SENSORS ARRAY: PRACTICAL

APPROACH FOR ION DETECTION IN AQUEOUS MEDIA

Yuanli Liu

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfilment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

December 2012

Committee:

Dr. Pavel Anzenbacher, Advisor Dr. Junfeng Shang Graduate Faculty Representative Dr. Peter Lu

Dr. Thomas H. Kinstle ii

ABSTRACT

Dr. Pavel Anzenbacher Jr., Advisor

Chemical sensing platforms that can sensitively, rapidly and accurately detect specific

chemical species in various operating media are of great importance, especially in complex

aqueous environments. Chemical sensor array coupled with chemometrics methods provide an

impressive option for high performance chemical sensing. The efforts of this dissertation focus

on the application of fluorimetric sensors array in ions detection in complex aqueous media.

This dissertation first presents the various signal transduction mechanisms that involve in

optical chemosensor design. The sensing elements are created by embedding chemosensors into

polymer matrix, then they are arrayed in microtiter plate containing multiple wells. The

chemometrics methods used for analyzing the multivariate signal arising from the sensor array

are introduced in detail.

In the part of practical application of sensors array. The first work aims to detect ions in

water by a simple sensors array contains six off-the shelf chemosensors. It turns out that the

sensors array is able to recognize cations, anions and ion-pairs with recognition efficiency (6:35)

and higher than 93% classification accuracy in water at a wide range of pH (5-9). Such a high

discrimination capacity was generally achieved only with structurally complex chemical sensors.

The second work presents a fluorescent sensor array containing eight hydrogen-bonding

based chemosensors capable of qualitative and quantitative detecting fourteen carboxylic drugs

in water and human urine. PCA and LDA are employed to optimize the size of sensors array. It

shows that a simple sensors array containing S5 and S8 can achieve 100% classification in water,

and another sensors array containing S5 and S7 can achieve 100% classification in human urine. iii

The third work demonstrates a new method for the construction of sensor array by employing the synergy between the chemosensor and polymer matrix. The results of anions and urine samples detection as well as quantitatively NSAIDs detection confirm that the established sensing system possesses the bright prospect in real-world application.

To my beloved wife

ACKNOWLEDGMENTS

The years of pursuing my PhD in the Center of Photochemical Sciences have been the

most memorable times for me. There are many great people that I got to know during this period deserve my heartfelt gratitude. In this acknowledgement I would like to first express my sincere gratitude to my advisor Dr. Pavel Anzenbacher Jr. for his consistent inspiration, support and guidance by which made me to grow as a scientist.

Thanks to all my Committee members Dr. Junfeng Shang, Dr. Peter Lu and Dr. Thomas H.

Kinstle for their continue instruction and for helping me improving and broadening my

knowledge. Special thanks to Dr. Arthur S. Brecher for the great help and patience in improving

my oral English.

I would like to thank all my lab-mates, especially Dr. Tsuyoshi Minami, Dr. Manuel

Palacious, Dr. Zhuo Wang, Dr. Cesar Perez, Dr. Lorenzo Mosca, Dr. Grygory Zyryanov, Dr.

Dula Man, Dr. Ryota Kabe, Dr. Shin-ya Takizawa and Dr. Marketa Schinkmanova. With whom I

have worked with and learnt a lot over the past years. Also, I am thankful to Selin Ergun, Maria

Kozelkova, Fereshteh Emami Piri, Alex Orlov, Petr Koutnik, Nina Esipenko, Dustin Theibert,

Sachin Vahile, Tanxin Du. It has been my greatest pleasure to collaborated with you and learnt

from all of you guys. The incredibly inspiring atmosphere and invaluable friendship created by

you guys has made my journey so meaningful and enjoyable.

Words fail me to express my heartily appreciation to Nora Cassidy, the coordinator of

graduate program in Center for Photochemical Sciences, whose help in various ways walked me

through the hardest time. My sincere thanks also goes to Dr. Alexander N. Tarnovsky for his help in PhD course study. The financial support from the Department of Chemistry was also

greatly appreciated. vi

Special thanks to Wei Xiong, Dan Chen, Sile Wang, Yuan Yao, Lingzhi Yang, Jing Li,

Jianxia Zhang, Hongtao Yu, Fei Xie, Xiaodong Liu and other friends who started the same journey and pursuing the same dream as me. I would never feel lonely with your companionship.

I must acknowledge Hong Xu couple, Xiaoqun Zhang couple, Zhao Yang couple, Dapeng Zhou couple, Qie Li couple, for your continues support which made my life in Bowling Green filled with happiness.

I must acknowledge my family for all the invaluable assistance and cooperation in various aspects of my daily life. Finally, warmest and deepest thanks to my wife Yuexin Chen for her always support, understanding and cares, which allows me getting through the most stressful time. vii

TABLE OF CONTENTS

CHAPTER I. INTRODUCTION ...... 1

1.1 Background ...... 1

1.2 Optical Chemosensors and Signal Transduction Mechanism ...... 3

1.3 Cross-Reactive Sensors Array...... 9

1.4 Role of Polymer Matrix in Chemical Sensing ...... 11

1.5 Goals of the Thesis ...... 13

1.6 References ...... 15

CHAPTER II. SENSORS ARRAY: DETECTION SCHEME AND DATA ANALYSIS ...... 25

2.1 Introduction ...... 25

2.2 Plate for Sensors Array ...... 25

2.3 Detection Schemes of Sensors Array ...... 26

2.4 Data Acquisition and Instruments...... 27

2.5 Chemometrics in Data Analysis ...... 32

2.6 Summary ...... 35

2.7 References ...... 35

CHAPTER III. RECOGNITION OF ION PAIRS IN WATER USING A SIMPLE FLUORIMETRIC SENSORS ARRAY ...... 39

3.1 Introduction ...... 39

3.2 Selection of Polymer Matrix ...... 40 viii

3.3 Selection of Chemosensors ...... 41

3.4 Sensing of Ion-pairs in Water ...... 42

3.5 Statistical Analysis of Multivariate Response Patterns ...... 45

3.6 Conclusion ...... 53

3.7 Experimental Section ...... 53

3.8 References ...... 55

CHAPTER IV. A FLUORIMETRIC SENSORS ARRAY FOR CARBOXYLIC DRUGS DETECTION IN WATER AND URINE ...... 60

4.1 Introduction ...... 60

4.2 Selection of Chemosensors for Preparation of Sensors Array ...... 61

4.3 Sensing of Carboxylic Drugs in Water ...... 64

4.4 Size Optimization of Sensors Array: Exploring the Simplest yet Effective Sensors

Array for Carboxylic Drugs in Water…………………………………………………..70

4.5 Discriminatory Capacity of a Single Sensor 4.5 ...... 73

4.6 Sensing of Carboxylic Drugs in Human Urine ...... 74

4.7 Size Optimization of Sensors Array: Exploring the Simplest yet Effective Sensors

Array for Carboxylic Drugs in Urine…………………………………………………...78

4.8 Conclusion ...... 80

4.9 Experimental Section ...... 82

4.10 References ...... 84

CHAPTER V. LEVERAGING MATERIAL PROPERTIES IN FLUORESCENCE SENSORS ARRAY: A PRACTICAL APPROACH ...... 87

5.1 Introduction ...... 87

5.2 Selection of Chemosensor...... 88

5.3 Selection of Polymer Matrices ...... 89

5.4 Sensing of Anions in Water and Urine ...... 90

5.5 Evaluation of Discriminatory Power of the Sensors Array ...... 93

5.6 Sensing of NSAIDS in Water and Saliva ...... 98

5.7 Sensing of Human Urine Samples ...... 100

5.8 Conclusion ...... 104

5.9 Experimental Section ...... 105

5.10 References ...... 107

APPENDIX A: LIST OF ABBREVIATION AND SYMBOLES ...... 110

APPENDIX B: SUPPLEMENTARY DATA FOR CHAPTER III ...... 112

APPENDIX C: SUPPLEMENTARY DATA FOR CHAPTER IV ...... 117

APPENDIX D: SUPPLEMENTARY DATA FOR CHAPTER V ...... 123

LIST OF FIGURES

Figure Page

1.1. Schematic representation of chemosensor in sensing process ...... 2

1.2. Example of PET based turn-on chemosensor ...... 4

1.3. Examples of ICT based chemosensors ...... 5

1.4. Examples of ESIPT based chemosensors ...... 6

1.5. Example of excimer chemosensor ...... 7

1.6. Schematic representation of analyte and sensor interaction model ...... 10

2.1. Example of multi-well microtiter plate...... 26

2.2. Schematic representation of the data matrix of sensors array...... 31

3.1. General molecular structure of poly(ether-urethane) ...... 40

3.2. Structure of pH indicators 3.1-3.6 used for ion-pairs recognition ...... 41

3.3. Examples of pH indictors 3.1-3.6 respond to cations and anions (0.5mM, 200nL)...... 43

3.4. Integrated fluorescence in the green channel shows the variability in the response of pH indictors 3.1-3.6 to cations and anions (0.5mM, 200nL)...... 43

3.5. Relative response (heat) map of the recorded 24×180 replica-averaged values illustrates the high output signal variability recorded from the array across whole dataset...... 44

3.6. LDA canonical score plots describe the response of the 6-member sensors array to 7 sodium series of ion pairs (500µM, 200nL) and a control at five different pHs (5, 6, 7, 8 and 9)...... 47

3.7. LDA canonical score plots describe the response of the 6-member sensors array to 5 fluoride series of ion pairs (500µM, 200nL) and a control at five different pHs (5, 6, 7, 8 and 9)...... 49

3.8. LDA canonical score plots describe the response of the 6-member sensors array to 35 ion pairs and a control ((500µM, 200nL, 10 trails each) at pH 7...... 50

4.1. Molecular structure of carboxylic drugs...... 61 xi

4.2. Molecular structure of chemosensors contained in the sensors array for recognition of

carboxylic drugs...... 62

4.3. Molecular structure of poly(ether)urethane used in sensing of carboxylic drugs...... 64

4.4. Fluorescence responses of 4.1-4.8 sensors array(200nL, 1mM) in the presence of 14

carboxylic drugs (200nL, 5mM in H2O at pH 8.5 ). Pseudo-color representation generated by superimposing of the equally weighed images corresponding to RGB channels ...... 65

4.5. Response pattern of the 4.1-4.8 sensors array (200nL, 500µM) to different carboxylic drugs

(200nL, 500µM, pH8.5) in H2O...... 66

4.6. PCA score plot of the first three principal components of statistical significance for 150 samples (200nL, 500 µM in H2O, pH8.5, 14 carboxylic drugs plus a control, 10 trials each)

produced by the 4.1-4.8 sensors array. Percentages on the axes account of variance to each PC

axis for a total of 69.4% of variance...... 68

4.7. LDA canonical score plot for the response of 4.1-4.8 sensors array to 14 carboxylic drugs in

water. The first 3 factors were used in order to describe at least the 80% of the total variance.

The cross-validation routine shows 100 % correct classification...... 69

4.8. Response profiles of chemosensor 4.5 to all carboxylic drugs in 15 channels. And LDA

canonical score plots for the response of 4.5 to 14 carboxylic drugs in H2O ...... 73

4.9. Fluorescence responses of 4.1-4.8 sensors array(200nL, 1mM) in the presence of 14

carboxylic drugs (200nL, 5mM in urine at pH 8.5). Pseudo-color representation generated by

superimposing of the equally weighed images corresponding to RGB channels...... 75

4.10. PCA score plot of the first three principal components of statistical significance for 150

samples (200nL, 500 µM in urine, pH8.5, 14 carboxylic drugs plus a control, 10 trials each) xii

produced by the 4.1-4.8 sensors array. Percentages on the axes account of variance to each PC axis for a total of 69.4% of variance ...... 76

4.11. LDA canonical score plots for the response of 4.1-4.8 sensors array to 14 carboxylic drugs in urine. The first 3 factors were used in order to describe at least the 80% of the total variance.

The cross-validation routine shows 100 % correct classification...... 77

5.1. General molecular skeleton of Poly(ether)urethane. Three segments with varying ratio:

Polyethylene Oxide(PEO), Urethane connectors, Polybutylene oxide(PBO) determine the overall hydrophilicity of the polymer ...... 90

5.2. Relative response (yellow channel) of the sensing films prepared using PU1-PU10 matrices

and fluorescent probe 5.1...... 91

5.3. Heat map of responses recorded for all the PU-probe 5.1 sensing films in all four emission channels (Blue, Green, Yellow and Red) shows a high level of useful variance in the sensor output ...... 92

5.4. Schematic representation of the sensing process ...... 93

5.5. Anion-induced 10-dimensional response profile generated by the changes in relative response (yellow channel) of 10-member sensors array (probe 5.1 embedded in 10 different PEU matrices).And LDA score plot of the first two factors describing circa 67% of the total variance for all eight anion samples (7 trials each). Cross-validated LDA (leave-one-out) shows 100% accurate classification of all 56 samples of 8 anions ...... 95

5.6. LDA score plot of the first two factors of four sensor arrays corresponding to different number of sensing elements and the clustering of eight anion samples (7 trials each, 500 µM, 200 nL, pH 8.5)...... 97 xiii

5.7. Relative response (Left: green channel; Right: yellow Channel) of the sensing film prepared

using PU6 matrix and probe 5.1 to different concentrations of Ibuprofen ...... 99

5.8. Relative response (Left: green channel; Right: yellow Channel) of the sensing film prepared by using PU6 matrix and probe 5.1 to different concentrations of Diclofenac...... 100

5.9. Urine analysis using the PU1-PU10:probe 5.1 sensors array. LDA score plot of 8 urine samples show a clear clustering and pattern of correlation with total chloride/phosphate concentrations (meq/L)...... 101

5.10. LDA canonical score plot for the response of established 10-member sensors array to 8 anions and urine samples. The LDA cross-validated (leave-one-out) shows 100% classification accuracy for all 16 clusters...... 102

5.11. HCA dendrogram obtained from Ward linkage showing the clustering and square

Euclidean distance between the trials of anions and urine samples ...... 103

xiv

LIST OF TABLES

Table Page

3.1. Classification accuracy for anions at different pH values obtained from LDA using leave- one-out cross-validation...... 51

3.2. Classification accuracy for cations at different pH levels obtained from LDA using leave-

one-out cross-validation...... 52

3.3. Classification accuracy for ion pairs at different pH values obtained from LDA using leave-

one-out cross validation ...... 52

4.1. The binding constants for anions in the form of tetra-n-butylammonium salts were

determined in MeCN (4.1-4.7) and DMSO (4.8), respectively...... 63

4.2. LDA jackknifed classification matrix shows 100% classification accuracy for 150 samples

(200nL, 500µM in H2O, pH8.5, 14 carboxylic drugs + control, 10 trials each)...... 70

4.3. LDA jackknifed classification matrix shows 100% classification accuracy for 150 samples

(200nL, 500µM in urine, pH8.5, 14 carboxylic drugs + control, 10 trials each)...... 78

5.1. Structure of the fluorescent probe 5.1 and the affinity constants (M-1) for aqueous anions at

22 °C from UV-Vis spectrometry titrations ...... 89

5.2. Ratio of PEO:PBO in ten poly(ether)urethane matrices ...... 90

LIST OF SCHEMES

Scheme Page

1.1. Frontier orbital scheme of PET and back ET process...... 3

2.1. Schematic demonstrating the fabrication processes of sensors array...... 27

2.2. Image processing routine for fluorimetric sensors array...... 28

4.1. Schematic illustration of the optimization process for the 4.1-4.8 sensors array by using PCA and LDA. The simplest version of sensors array containing 4.5 and 4.8 shows 100% classification accuracy for 14 carboxylic drugs in water...... 71

4.2. Schematic illustration of the optimization process for the 4.1-4.8 sensors array by using PCA and LDA. The simplest version of sensors array containing 4.5 and 4.7 shows 100% classification accuracy for 14 carboxylic drugs in urine...... 79 1

CHAPTER I. INTRODUCTION

1.1 Background

Identification and quantification of chemical species that surround us (cations, anions,

radicals as well as neutral molecules) have attracted considerable attention for many decades due

to the extensive influence on human life in areas such as environmental analysis,1 health care,2,3,4

food quality control and medical diagnostics.5,6

A number of detection technologies based on the intrinsic properties of chemical species

have been developed from the emerging of chemical science,7 such as GC-MS, IC-ICP, HPLC, etc. These methods offer direct detection but usually require high cost analytical instruments and high-purity samples. Chemical sensing technology has been proved as an alternative for effective detection of chemical and biologically important substances in complex environment.8

Molecule probes,9 sensors array10 and miniaturized devices11 are the three typical

chemical sensing platforms. Chemical sensor is the required component and common feature

among the three platforms. In principle, the term of “chemosensor” refers to a device capable of

transforming chemical information into analytically signal output.12 Most chemosensors

comprise two functional parts: a receptor (recognition unit) and a reporter (signal unit),13 they are

linked by a spacer (Figure 1.1). The receptor is a moiety responsible for binding the target

analytes utilizing different chemical interaction, such as hydrogen bonding, metal coordination, hydrophobic forces, van der Waals forces, pi-pi interactions and electrostatic effects.14 These binding events usually cause perturbation in physical or chemical properties

ranging from electron distribution, frontier orbital energy to redox potentials, etc. The reporter is

then a moiety responsible for transforming and amplifying the perturbed properties into an

observable signal output. 2

Receptor Signaling Unit Spacer Reporter

Off

Analyte

Figure 1.1. Schematic representation of chemosensor in sensing process. The analyte is bound by the receptor and results in a change in physical property of reporter.

According to various signal transduction mechanisms employed in recognition events,

chemosensors are usually classified as different type of chemical sensors.15 For example, optical

chemosensor, the analyte-receptor binding causes changes in the optical properties (absorbance,

fluorescence or lifetime).15c Electrical sensor, the analyte-receptor binding causes changes in the electrical properties (conductivity, permitivity, etc.). Electrochemical sensor, the analyte-receptor binding causes changes in the electrochemical properties (potential, current, etc.). Thermal sensor, the analyte-receptor binding causes changes in the thermal properties (temperature, heat etc.). The details of signal transduction mechanisms for the design of optical chemosensors will be presented since the main work focuses on the application of optical sensors array in identification and quantification of target analytes (anions, ion pairs, carboxylic drugs). 3

1.2 Optical Chemosensors and Signal Transduction Mechanism

In the last two decades, the signal transduction mechanism of optical chemosensor has

been widely explored and a number of optical chemosensors have been developed.16

Photoinduced electron transfer (PET) is a typical signal transduction mechanism for

chemosensor design employing the “fluorophore–spacer–receptor” model.21 The PET process

occurs between an electron donor moiety (receptor) with a higher HOMO energy level and a

fluorophore with a lower HOMO energy level. Upon photo excitation an electron is promoted to

the LUMO from the HOMO of fluorophore and generating an electron vacancy. One of the

electrons will transfer to the vacancy from the HOMO orbital the receptor due to the existence of

the energy gap between the HOMO orbital the receptor and the HOMO orbital of the fluorophore.

After the PET occurred, the excited electron in the LUMO orbital of the fluorophore will transfer

back to the HOMO of receptor to re-construct the molecule ground state (Scheme 1.1). This will results in the quenching of fluorophore emission.

LUMO LUMO Back ET PET E

HOMO HOMO HOMO HOMO Analyte - free Analyte - free Rece tor Rece tor Fl uoro hore* p p p Fl uorophore*

Scheme1.1 Frontier orbital scheme of PET and back ET process (adapted form ref.21).

Photoinduced electron transfer mechanism provides an easy implementation route to

design “turn- on” or “turn-off” optical chemosensor without significant change of the spectra.

Gunnlaugsson’s research group designed a “turn-on” sensor for the detection of Zn(II). The

oxidation potential of the receptor increased upon Zn2+binding which results in the decrease of 4

the receptor’s ability to quench the fluorescence of the naphthalimide moiety via PET (Figure

1.2).22,23

+ O N O Zn2 LUMO

υ PET - E h (Fluorescence) CO2

HOMO - N CO2 HOMO hυ Fluorophore* Analyte - bound HN Receptor Fluorescence

Figure 1.2. Example of PET based turn-on chemosensor. Left: Frontier orbital scheme for blocking the PET upon analyte binding (adapted from ref.22). Right: The PET process was blocked in the presence of Zn2+, the chemosensor displays fluorescence turn-on behavior (Adapted from ref.23).

Intramolecular or internal charge transfer (ICT) is another signal transduction mechanism

frequently utilized in the design of chemosensors. Usually, the ICT based chemosensors bearing

electron-donating (EDG) group which act as charge transfer donor and electron-withdrawing

(EWG) group which act as charge transfer acceptor. The chromophore undergoes a charge

transfer from the donor to the acceptor upon photo excitation. The enhancement or disruption of

the electron distribution in the chemosensor in the presence of analytes results in the change in

the optical properties. Intramolecular charge transfer based chemosensors have been widely

explored for the detection of heavy metals and anions. Figure 1.3 (top) shows an example of ICT

based chemosensor for mercury detection.19 The coordination of Hg2+ triggered the ICT and cause a color change of the chemosensor from pink to yellow. Anzenbacher’s research group developed a series of ICT based chemosensors for anions detection by attaching electron-poor moieties to electron-rich calix[4]pyrrole group ( Figure 1.3, Bottom).20 5

Me Me

N N S S 2+ O N Hg O N O O + O O Hg2

O S O S

N PUSH PULL H O δ- NH - H N δ+

H N

NC CN

Figure 1.3. Examples of ICT based chemosensors. Top: The coordination of the Hg2+ promotes the occurrence of ICT which results in a change in color from pink to yellow (Adapted from ref.19). Bottom: Hydrogen bonding between an anion and the electron-rich pyrrole results in partial intramolecular charge transfer (PICT) from pyrrole (push) to the electron-poor 1- indanylidene moiety (Adapted from ref.20).

Intra-/intermolecular proton transfer (ESIPT) reaction is one of the fundamental

processes in biology and chemistry such as the enzymatic reactions and acid-base neutralization

reactions. Since the first observation of ESIPT, It has been considered as an important signal

transduction mechanism used in the design of optical chemosensor. Proton transfer process

consists of protonation or deprotonation reactions that affect the electronic structure and electron

distribution in the chromophore. This will results in a change in both of the absorption and

emission spectra (Figure 1.4).17 Generally, the ESIPT processes require an intra-/intermolecular

hydrogen bond (H-bond) between proton donor (-OH, -NH 2) and proton acceptor (- N =, -C = O) groups in close proximity.18 Upon photo excitation, the redistribution of electronic charge results

in a dramatically increase in the acidity of the proton donor and the basicity of the proton 6 acceptor. This will cause the occurrence of proton transfer reaction from the proton donor to the proton acceptor.

- OH O + H AcO- H H

N - N AcO

ν 1 h ν h O 2 O

392nm 525nm

Weak fluorescence High fluorescence

SH SH

N NH Mg2+

OH O

Figure 1.4 Examples of ESIPT based chemosensors. Top: The ICT is promoted from 3- hydroxyl-2-naththanilide in presence of acetate, which causes an enhanced red-shifted emission of the chemosensor (Adapted from ref.17a). Bottom: ESIPT is promoted form the chemosensor to generate a keto tautomer in the presence of proficient Mg2+ which allows for ratiometric detection of Mg2+ (Adapted from ref.17b).

Excimer (or exciplex) based chemosensor is also a common approach used for chemosensor design. The term excimer or exciplex refers to a dimer which was formed by two same or different molecules respectively. Several fluorophores like naphthalene, anthracene and pyrene can form excimer when an excited fluorophore molecule comes in close to another fluorophore during the excited state. Generally, the formation of excimer results in dual fluorescence corresponding to monomer and excimer emission. Also the formation of excimer will cause a decrease in energy level of the excited state which will results in the emission of 7 excimer appears in longer wavelength range with a broad band and weaker intensity. Such a unique ratiometric property allows for practical applications in chemical sensing field (Figure

1.5).24

H HN H + N HN H

PPi

H O O- HN H P + N - O HN H

H O- HN P + N H O O- HN H Figure 1.5. Example of excimer chemosensor. Formation of the excimer results in a remarkable change in the ratio of emission intensities of excimer to monomer (Adapted from ref.24).

Indicator displacement assay (IDA) is a powerful method that is different with the traditional chemosensor design which employs “receptor-spacer-signaling unit”. In an IDA, an indicator (signaling unit) is first allowed to reversibly bind to a receptor. The common interactions between the indictor and receptor including hydrogen bonds,25 ionic bonds, electrostatic forces,26 Boron–diol interaction,27 and formation of metal complex.28 In the presence of competitive analytes with high affinity in the system, a displacement reaction takes place, as a result, the indictor were released from the reactor by the analytes, this process in turn cause a change in the signal transduction of the system. The main principle 8

governing the occurrence of an IDA is that the affinity between the analyte and the receptor

should be higher than that between the indictor and the receptor. Signal transduction

involved in an IDA includes several mechanisms, such as electronic energy transfer (EET),29

30 31 photoinduced electron transfer (PET), fluorescence resonance energy transfer (FRET) , etc.

Förster resonance energy transfer (FRET), also named as fluorescence resonance energy

transfer, is a mechanism usually utilized for “near-field” detection. The mechanism of FRET

involves a donor fluorophore in the excited state transferring its excitation energy to a

nearby acceptor (in proximity, typically 10-100 Å) fluorophore through a nonradiative dipole-

dipole interaction.12 It requires an overlap between the emission spectra of the donor fluorophore

and absorption spectra of receptor fluorophore. The efficiency of the FRET depends on many

factors, including the quantum yield of the donor in absence of acceptor, the refractive index of

the solution, the actual distance and the spectral overlap between donor and acceptor, the mutual

molecular orientation. Since the FRET occurs within 100 Å, a distance that is comparable to the

diameters of biological membranes, proteins, and the dimension within which the conformation

of DNA changes. FRET based mechanism is particularly useful for studies of biological

molecules.32

Signal transduction mechanism is the most fundamental and crucial issue in the design of

optical chemosensors. Besides the presented signal transduction mechanisms above, there are

some other important signaling mechanisms, such as electronic energy transfer (EET),29

photoexcited metal-to-ligand-charge-transfer (MLCT)33 cation-π interaction,34 or anion-π

35 interaction, that have not been specified since they are not in the scope of this thesis.

1.3 Cross-Reactive Sensors Array

Exploiting the cross-reactive sensor array approach for the simultaneously detection of

multiple chemically and biologically important compounds has been widely explored in the last

two decades. Unlike the classical Emil Fisher “lock-and-key” molecule recognition model which

based on the design a proper receptor with certain binding affinity toward specific analyte of

interest. Cross-reactive sensor array approach offers significant advantages with respect to

recognition methodology. Figure 1.6 demonstrates the mode of cross-reactive of sensor array.

The sensor array device consists of a number of non-specific sensing elements (chemosensor), each sensor display different level of affinity to different analytes. A multi-variable response

pattern will be created in the presence of different analytes due to the non-specific interactions

between the analytes and sensors. Employing the pattern recognition method, the multi-variable

response dataset could be interpreted and be used for the identification and classification of

analyte of interest. The advantage of the cross-reactive sensor array is that it provides an

alternative for circumventing the difficulties associated with synthesis of highly selective

chemosensors.36 Another most significant feature of pattern recognition based cross-reactive sensor array is that they could allow simultaneous identification and quantification of multiple analytes.37 In addition, the cross-reactive sensor array approach could be extended to detect not

only the analytes with strong affinity but also those analytes with much weaker affinities, this

feature open an avenue for the detection of a number of biologically important compounds such

as hybridized short DNA sequences and binding antibodies with low-affinity.38 10

Figure 1.6. Schematic representation of analyte and sensor interaction model. Different analytes can interact with different sensors in the array and generate a multi-variable response pattern (Adapted from ref. 36).

Since the introduction in the 1990s,39 sensor array technology has expanded rapidly into

every major field of biological studies, such as DNA replication,40 gene expression,41

neurodegenerative disease,42 protein trafficking,43 genome mismatch scanning,44 inflammation,45

hormone action,46 cell cycle,47 cytoskeleton,48 oxidative stress,49 and apoptosis.50 Later

development of sensor array keeps expanding rapidly outside the bio-related disciplines, ranging

from solution phase analytes including heavy metal ions,51 organic anions,52 beverages,53 toothpaste,54 nucleic acids,55 to pollutant gases,56 volatile organic compounds (VOC)57 etc.

A great number of cross-reactive sensor arrays in biology related studies utilize changes

in optical properties for signal transduction. Generally, these observed changes include color and

luminescent emission intensity. There are also sensor arrays that employing other signal

transduction mechanism such as potentiometry,58 conductivity,59 voltammetry,60 polarographic61

impedance,62 radiofrequency.63

A number of platforms of sensor array have been developed for chemical sensing experiments. For example, microtiter plate with microchannels or microwells-based assays,64 11

array of micro-bead based sensor elements immobilized with different biomolecules,65 and

optical fibers based assay.66 The rapid progressing in this field also stimulates the researchers

preparing new sensing material for the building of sensor array, ranging from organic

chemosensor, microelectrodes, conductive polymers, to new nanomaterials including random-

sized quantum dots,67 molecularly modified metal nanoparticles, carbon nanotubes,68 metal oxide

nanoparticles, and semiconducting nanowires.69 etc.

1.4 Role of Polymer Matrix in Chemical Sensing

A general method to construct sensing system is to incorprate optical sensors into polymeric

matrix to form a solid sensing film.70 As a component in such a sensing system, the role of the

polymeric matrix is not only offer a solid mechanical support to the dispersed chemosensors, but also

act as an element participating in the recognition process by leveraging the properties of the sensing

system. A number of polymers with different chemical nature have been used in the study of optical

sensing such as cellulose,71 agarose,72 silicone,73 polydiacetylene,74 polyacrylamide,75 polymethacrylamides,76 plasticized poly(vinyl chloride),77 polysaccharides,78 polyurethanes,79 etc.

Usually, there are three methods to immobilize the chemosensors into polymeric matrix.80

The first method is to physically embed chemosensors into polymeric matrix which was achieved by

dissolving chemosensors in polymer solution.70,77b A number of examples have been emerged by applying this method, for instance, Hamachi et al. embeded the artificial receptors into supermolecular hydrogel to construct sensing assembly for detection of phosphate derivatives.81

Anzenbacher et al. embded the calix[4]pyrrole based chemosensors into hydrophilic polyurethanes to

detect anions in aquous environment.82 The second method for chemosensor immobilzation is to 12

chemically (covalent) attach chemosensors to the polymeric matrix.83 In contrast to physical

immobilization method, chemical immobilization can improve the stability by preventing the

leaching of chemosensors out of polymer matrix, this feature in turn improve the reproducibility of

the sensing system. The downside is the chemical immobilization requires extra synthesis process

for the formation of covalent bonds between chemosensors and polymeric metrix. The third method

is electrostatic immobilization.84 which usually used in those sensing systems that possess the

electrostatic interaction between the sensors and corresponding polymeric matrix. Since the

chemical immobilization and electrostatic immobilization methods go beyond the scope of this

thesis, I decided to focus on the introduction of physical immobilization method which is the most

common method used in our research laboratory.

The selection of polymer matrix is an important step in the process of fabrication of sensing

device. Some crucial factors that might influence the final sensing performance should be considered

during the construction of sensing film, such as the mechanical stablity, the permeability for target

analyte. Most importantly, Immobilization might change chemosensors’s optical properties

compared with the free status of chemosensors, for instance, shifting the optical spectra, changing the

binding affinity of chemosensors toward analyte, luminescence lifetimes, systems’ pKa value, etc.85

These make the proper selection of polymer matrix even more important.

It has been demonstrated that the supramolecular properties of chemosensors displayed in

solution could be leveraged in the supramolecular-polymer matrix,86 it’s the synergy between

chemosenosrs and polymer matrix determine the overall sensing performance.87 In the two- component sensing system, the primary role of the chemosensors is to bind analytes and generate luminescence signal output. On the other hand, the main role of the second component-polymer matrix, is to internalize the analyte from aqueous solution. Thus, the affinity of polymer matrix 13

toward water will influences the analyte transportation, which in turn affects the sensitivity of the

chemosensor film and the selectivity toward different analytes as well. This feature provides an

alternative for developing sensing system by adjusting the properties of polymer matrix instead of

solely tuning the optical properties of the chemosensors.

The approach that exploting the synergy between the polymer matrix and chemosensors to

design sensing system has shown promosing utility in real-application. The studies in Dr.

Anzenbacher’s research laboratory have shown that an array sensor comprising ten sensing elements made by embedding one single chemosensor into defferent polyurethanes matrix capable of detecting anions and carboxylic drugs in water and biologically important fluid such as human urine and saliva.

1.5 Goals of the Thesis

This thesis seeks the identification and quantification of multiple chemical/biochemical important substances in water and in complex environments with competing elctrolytes by utilizing sensors array approach. The discriminatory capacity of sensors array might be improved by selecting proper selective yet cross-reactive chemosensors and tuning the sensing environment in which the sensing event takes place. As an essential component for sensing device, polymer matrix has attracted more and more attention from researchers, this thesis also involves in exploring the synergy between chemosensors and polymeric matrices in the sensing process. The data generated by the sensors array will be interpreted by pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), 14 hierarchical cluster analysis (HCA), and artificial neural network (ANN). The main issues contained in this thesis are described in the following chapters:

♦ Chapter I is the introduction part. We firstly illustrated the importance of the

chemical/biological compounds sensing and specified the operation principle of

chemosensor. Then the signal transduction mechanisms in optical chemosensor design

and operation principle of sensors array have been introduced. The supporting

material_polymer matrix and it’s role played in the sensing process also presented in

this chapter.

♦ Chapter II focuses on the description of the sensing scheme of cross-reactive sensors

array. In this chapter, the protocol details of sensors aray, the data acquisiton process

and pattern recognition techniques which used to evaluate the discrimination

capability of sensors array have been presented.

♦ Chapter III presents the results of recognizing cations, anions and ion pairs in water at

pH 5-9 achieved with a small sensor array consisting of six off-the-shelf pH

indicators. To our best knowledge, none of previous efforts demonstrated array

sensing of ion pairs in water.

♦ Chapter IV describe a fluorimetric sensor array consisting of eight hydrogen-bonding

based anionic chemosensors for recogniiton of carboxylic drugs in water and human

urine. The 8-member sensors array demonstrated promising discriminatory capacity in

psychological fluid with complex competing electrolytes. This provides a alternative

for the development of analytical platforms in real-world applications.

♦ Chapter V presents a new method to fabricate high performance sensors array. In this

chapter, the descriped sensors array consisting of 10 sensing elements fabricated by 15

doping one single chemosensor into 10 different polyurethane with varying co-

monomer proportions. The new sensors array is shown to recognize eight different

aqueous anions (acetate, benzoate, fluoride, chloride, phosphate, pyrophosphate,

hydrogen sulfide and cyanide) and 8 urine samples with 100% classification accuracy.

In addition, the applicability of the described sensors array in quantitative sensing is

also being tested. The priliminary results demonstrated that individual sensor (FAAS-

3/ PU-6) respond to variant concentrations of non-steroidal anti-inflammatory drugs

(NSAIDs), such as diclofenac and ibuprofen in human saliva showing a reasonable

dynamic range and limit of detection (LOD).

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CHAPTER II. SENSORS ARRAY: DETECTION SCHEME AND DATA ANALYSIS

2.1 Introduction

The demanding of simultaneously detection of multiple analytes in complex environment facilitates the focus of chemical sensing shifting from selective molecule sensors toward cross-reactive sensors array.1 The high density of multivariate signal

generated by cross-reactive sensors in the array chip need to be interpreted into useful

information for targets identification and quantification. Chemometrics is a chemical

discipline that employs mathematical or statistical tools to extract chemical information from

complex chemical data,2 which provide an option for analyzing the data obtained from

sensors array. In this chapter, the detection scheme of sensors array and data analysis

methods will be discussed.

2.2 Plate for Sensors Array

Usually, a number of sesning materials which prepared by dissoloving chemosensors

into polymer solution will be arranged into the ultrasonically drilled multiple wells of

microtiter plate to make sensor array. The parameters of the wells in microtiter plates are

1mm in diameter and 0.25mm in depth respectively(Figure 2.1). There are several tpyes of

microtiter plates based on the number of the wells. The selection of microtiter plate

depending on the real need, for example, how many sensing elements will be assembled in a

single plate, or how many analytes need to be detect simutaneously. 26

Figure 2.1. Example of multi-well microtiter plate. Top: 8×12 wells microtiter plate. Bottom: 3D profile depicts the real dimensions of a single well in microtiter plate.

2.3 Detection Schemes of Sensors Array

The detection processes of sensors array is schematically shown in Scheme 2.1. The

sensor solutions were prepared by imbedding chemosensors into polymer matrix that was

prepared by dissolving polymer in organic solvent. In a typical sensor array chip, the wells of

microtiter plate were filled with 200nL chemosensor/polymer solution by using a hamilton

disperser and dried to form a 5µm thick sensing film in each well, then the sensors array chip

was treated by spotting water in the wells aiming at curing sensing film. The sensors array

chip was scanned two times to obtain the corresponding fluorescence images, the first

scanning was implemented after sensing films were treated by water, the second scanning

was implemented after adding the analyte solutions into the wells containing sensing film. To 27

avoid the effect of water on the sensing process, the sensors array chip need to be dried for

15 min at room temperature in air and dried in vacuum oven for 5min at ~45°C before each

time scanning.

Scheme 2.1. Schematic demonstrating the fabrication processes of sensors array.

2.4 Data Acquisition and Instruments

The digital imaging based techniques provide an easy-to-operation approach to simultaneously record the fluorescence response that generated by the sensing elements in the sensor array. Scheme 2.2 shows the scheme of image processing routine for fluorometric sensors array. Once the images of sensors array been recorded in individual channels(Scheme 28

2.2, middle), the molecular imaging software associated with the Kodak Image Station was used to integrate the gray value of each sensing element contained in the array plate (Scheme

2.2, right).

Scheme 2.2. Image processing routine for fluorimetric sensors array. Left: Kodak Image Station 4000MM Pro and sensors array microtiter plate. Middle: Images recorded in individual channels. Right: Integration of the gray value of each sensing element in the array plate.

For the recording of fluorescence signal of sensors array, two Kodak digital imaging systems, Image Station 440CF (IS 440CF) and Image Station 4000MM Pro (IS 4000MM Pro) were used in Dr. Anzenbacher research laboratory. Both of the imaging systems provide three standard modes of illumination including epi-illumination, transillumination and luminescent (no-illumination). The diversified illumination modes expand the number of 29

luminescent channels for observation and offer the potential for multipurpose detection based

on the specific detection conditions.

The IS 440CF equipped with a broadband UV lamp (300-400 nm, λmax=365 nm) as

excitation light source and a thermoelectrically cooled Charged-Coupled Device (CCD) camera for image recording. The epi-illumination mode exploits the UV lamp in the optical path chamber to provide 300-400nm UV excitation light for a variety of fluorophores. The optical path chamber provides a light-tight environment so that there is no need to operate in a darkroom. The transmission mode exploits external light with a light diffuser for absorbance images of samples, the light diffuser - a white translucent acrylic plate is placed over the sample to diffuse light uniformly across the sample. The luminescent mode is used for imaging those samples with luminescent properties, such as chemiluminescent probes and radioactive samples. The CCD camera captures the images of samples at 12-bit at a pixel density of 752 x 582. A 6×zoom lens combined the CCD camera can generates field of view form 25 x 20 cm (~330µm/pixel) to 4.2 x 3.3cm (~57 µm/pixel). For the observation of luminescent signal in different channels, a filter wheel with five filters is placed before the lens. The filters including 1) Blue: band-pass filter 380-480 nm, λmax=435 nm, (2) Green: band pass filter 480-580 nm, λmax=525 nm, (3) Yellow: long pass filter >520 nm, (4) Red: long pass filter >580 nm, (5) Dark red: long pass filter >620nm. The selection of filters depends on the specific application. For instance, the wavelength at which fluorophore presents strongest emission, the number of desired observation channels. The IS440CF image acquisition and analysis software provides a powerful tool for manipulating the images of the sensor arrays and yields quantitative fluorescent intensity data. 30

The Image Station 4000MM PRO equipped with a xenon lamp and a broadband UV

lamp as excitation light source and a thermoelectrically cooled Charged-Coupled Device

(CCD) camera for image recording. The xenon lamp provides a wide breadth of excitation

light source ranging from 370nm-800nm for epi-illumination. In the IS 4000MM Pro imaging system, the light source of epi-illumination mode can be conditioned by select the appropriate excitation filter (band-pass filter with 10nm band-breadth). The optional excitation filters include: 390nm, 430nm, 470nm, 500nm, 530nm, 550nm, 610nm, 630nm,

710 nm, and 730 nm. After the light emanating from the xenon lamp and pass through the selected filter, the conditioning light is projected to the platen surface by using a fiber optic.

The method of conditioning the light source by using the selectable filters enables the efficient illumination of the objects of interest with a desired band of wavelengths. The UV lamp was installed in an additional lid for transillumination of sensor array if UV excitation necessary, the additional lid also provides white light transillumination mode utilizing external room light with a translucent white acrylic Light Diffuser that is placed over the sample to diffuse light across the sample area uniformly. The CCD camera in the IS

4000MM Pro system is thermoelectrically cooled to maximize the sensitivity for objects of interest. The camera collects the image data on 2048 x 2048 pixel intensity. Single frame image data is digitized at 16-bits. A 10×zoom lens combined the CCD camera can generates field of view form 20 x 20 cm (~100µm/pixel) to 2 x 2cm (~10µm/pixel). In order to observe the luminescent signal in different channels, an emission filter wheel containing 4 band-pass filters is placed before the automated lens system, the filters include: 440nm, 480nm, 535nm, and 600nm respectively, in addition, the filters are replaceable if necessary. The revolutionary software in IS 4000MM Pro imaging system offers the optimal combination of 31

automation, precision, and versatility for images capturing. It provides all the necessary tools

for image analysis, image control and capturing parameters setting, such as exposure mode,

adjusting the focus plane, field of view, and aperture (f-stop) as well.

In order to obtain desired output, the selection of proper feature of the digitalized signal for subsequently analysis is of great importance since some fundamental problems still

exist, for example, roughness of the interior surface in the microtiter plate wells, interference

from the background. To avoid such kind of problems, typically, the sum intensity is

employed to characterize the fluoresence response change. The resulting fluorecence

response data are usually arranged into a matrix that contains a number of columns

corresponding to the detected response variables and a number of rows corresponding to the

cases or observations. Usually, the data matrix is very large which contains hundreds of

variables, and hundreds of cases. Figure 2.2 shows a typical data matrix that include an

additional row for loading variable conditions and an additional volumn for loading samples

(cases).

Figure 2.2. Schematic representation of the data matrix of sensors array. 32

The data preprocessing has received extensive attention from the emerging of the

sensor array since the appropriate data preprocessing strategy plays an crucial role in the

sensing performance. A number of data preprocessing algorithm have been proposed based

on practical sensing platform and desired data features. Among them, the widely used data

preprocessing methods include background correction, zero-center, range scale, differential scaling, relative scaling, normalization, etc. In the case of fluorimetric sensor array, a valuable data preprocessing strategy is to combine both differential scaling and relative scaling to generate relative intensity for further evaluation.

Here, R stands for the relative fluorescence intensity, F0 and F stands for the fluorescense

intensities of the sensors before(F0) and after(F) expose to analyte of interest. In case of

situation that the background brings potential interference, background correction is a

necessary step to preprocess the data, which can be achieved by substract the baseline. In

addition, normalization is also a useful method to achieve better resolution if the obtained

response data are too entangle.

2.5 Chemometrics in Data Analysis

Chemometrics is the discipline aiming at extracting information from chemical

systems by employing statistical and mathetical means.3 Pattern recognition is a widely used

chemometrics method for the interpretation of multivariate response pattern obtained from 33 sensors array.4 Usually, the data interpretation of sensor array involves both the descriptive and predictive problems. The descriptive problems focus on revealing the underlying relationship of the data, the predictive problems involve predicting the new propetries of the analytes. The most frequently used chemometrics methods for solve aforementioned problems include unsupervised and supervised methods. In this section, these chemometrics methods will be introduced.

Principal component analysis(PCA) is a unsupervised mathematical method that employs orthogonal transformation to interpret a high-dimensional dataset into a low- dimensional dataset in which the significant features of the original data still preserved.5 This is operated by computing the orthogonal eigenvectors(principal components, PCs) which corresponds to the vector direction that contains the possible maximum variances. So the first PC accounts for the highest degree of variance, and each succeeding PC in turn accounts for the variances in dereasing order. Since the operation doesn’t distort the internal structure of the dataset, PCA is used for visualizing the intrinsic characteristics of the dataset, for example, whether there are any groups in the dataset, wether there are any outliers, whether there are any trends exist. Another important application of PCA is optimizing the size of sensor array, this can be achieve by evaluating the contribution of individual sensor to each

PCs.6

Hierarchical clustering analysis (HCA) is another unsupervised method specifically used for separating data into specific groups,7 which can be used for classify the observations by seeking the similarity of observations or computing the distance between data points. The observations are clustered the resulting clusters are then progressively linked together to form a dendrogram based on their similarity or distances. In such a way, HCA provide an 34

intuitionistic method to observe the real clustering or dispersion of the data. Several

algorithm involved in calculating the similarity or distances and all of them have been

specified in detail in a number of books or reviews.7b

Linear discriminant analysis (LDA) is one of the most important supervised methods

for the data interpretation of sensor array.8 Generally, supervised methods use the identity of

training data samples to create a model for further classification of unknown samples. LDA

is such a method for classification of observations. The operation of LDA use leave-one-out

(cross-validation) routine. In which, one data point was leave out of the dataset while using the rest of the data as training set to construct a linear discriminant(LD)function. The linear discriminant function is then used to put the excluded data point to the correct cluster. This routine is carried out to each observation in the dataset. In such a way the classification of the observation was achieved. While, in some cases, leave-one-out routie prone to result in overestimate the correct classification, with the development chemometrics techniques, leave-multiple-out routine was used to correct this problem.9

Artificial neural network (ANN) is a mathematical model that mimics the structure

and function of neural brain networks and it is usually utilized to seek patterns in data by

model relationships between input and output.10 The basic structure of a typical neural

network consists of input layer, hidden layer and output layer. Several neurons exist in

different layers serve for information transportation between layers in certain interactions

that defined by defferent funtions. ANN is a powerful tool to reveal complex phenomena

especially for those demonstrate non-linearr relationship. The application of ANN in

multivariate data analysis has drastically expanded in the past two decades. Dr. Anzenbacher

research group has applied ANN in the field of protein sensing. 35

Aanalysis of variance (ANOVA) plays an important role in the screen of protocol precess for sensor array.11 ANOVA and associated F-ratios are important statistical method for the determination of the relative significant variables that contribute to the distinguishment of different groups. By using ANOVA, one can exclude the redundant or irrelevant variables.12

Several other chemometrics methods have also been employed in the evaluation of sensor array data. For example, Partial Least Squares Discriminant Analysis (PLS-DA),5d

Support Vector machines (SVM),13 etc. However, the detail of those methods will not be discussed here since their application beyond the scope of this thesis.

2.6 Summary

The detection scheme of sensors array has been presented. The data acquisition the instruments used for images recording of fluorescent sensors array have also been introduced in detail. The protocol for each practical assay will be introduced in corresponding chapter.

Additionally and most importantly, chemometrics methods that have been used in analysis of sensor array data in this thesis such PCA, HCA, LDA and ANOVA have been presented in detail in this chapter.

2.7 References

(1) (a) Lavigne, J. J.; Anslyn, E. V. Angew. Chem.,Int. Ed. 2001, 40, 3119.

(b) Rakow, N. A.; Suslick, K. S. Nature 2000, 406, 710. 36

(b) Ciosek, P.; Wroblewski, W. Analyst 2007, 132, 963.

(d) Gardner, J. W.; Bartlett, P. N. Sens. Actuators, B Chem. 1994, 18, 211.

(2) (a) Carey, W. P.; Beebe, K. R.; Sanchez, E.; Geladi, P.; Kowalski, B. R. Sensor Actuat.

1986, 9, 223.

(b) Jurs, P. C.; Bakken, G. A.; Mcclelland, H. E. Chem. Rev. 2000, 100, 2649.

Sensors 2003, 3, 458.

(d) Natale, C.; Martinelli, E.; Pennazza, G.; Orsini, A.; Santonico, M. Advances in Sensing

with Security Applications 2006, 147.

(3) Massart, D. L.; Vandeginste, B. G. M.; Deming, S. N.; Michotte, Y.; Kaufman, L.

Chemometrics: A Textbook; Elsevier: Amsterdam, 1998.

(4) (a) Shaffer, R. E.; Rose-Pehrsson, S. L. Anal. Chem. 1999, 71, 4263.

(b) Johnson, S. R.; Sutter, J. M.; Engelhardt, H. L.; Jurs, P. C.; White, J.; Kauer, J. S.;

Dickinson, T. A.; Walt, D. R. Anal. Chem. 1997, 69, 4641.

(d) Shabbir, S. H.; Joyce, L. A.; da Cruz, G. M.; Lynch, V. M.; Sorey, S.; Anslyn, E. V. J.

Am. Chem. Soc. 2009, 131, 13125.

(5) (a) Malinowski, E. R. Factor Analysis in Chemistry, 2nd ed.; John Wiley & Sons: New

York, 1991.

(b) Jambu, M. Exploratory and Multivariate Data Analysis; Academic Press: Boston, 1991. 37

(d) Jolliffe, I. T. Principal Component Analysis; Springer: New York, 2002.

(6) Wright, A. T.; Edwards, N. Y.; Anslyn, E. V.; McDevitt, J. T. Angew. Chem., Int. Ed.

2007, 46, 8212.

(7) (a) Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. in Chemometrics: A Practical Guide;

Wiley: New York, 1998.

(b) Varmuza, K.; Filzmoser, P. Introduction to Multivariate Statistical Analysis in

Chemometrics; CRC Press-Taylor & Francis: Boca Raton, 2009.

(c)Suslick, B. A.; Feng, L.; Suslick, K. S. Anal. Chem. 2010, 82, 2067.

(8) Otto, M. in Chemometrics: Statistics and Computer Application in Analytical Chemistry.

Wiley-VCH; Weinheim; New York, 314.

(b) Yu, H.; Yang, J. Pattern Recogn. Lett. 2001, 34, 2067.

(9) The development and optimization of LDA in analysis of sensor array data is in progressing in Dr. Anzenbacher research group.

(10) (a) Burns, J. A.; Whitesides, G. M. Chem. Rev. 1993, 93, 2583.

(b) Zupan, J.; Gasteiger, J. Neural Networks in Chemistry and Drug Design; Wiley: New

York, 1999.

(d) Egmont-Petersen, M.; de Ridder, D.; Handels, H. Pattern Recogn. 2002, 35, 2279.

(11) Guenther, W. C. Analysis of Variance; Prentice Hall: Englewood Cliffs, 1964. 38

(12) The practical application of ANOVA is involved in the analysis of protein recognition project in Anzenbacher research group.

(13) Ivanciuc, O. Rev. Comput. Chem. 2007, 23, 291.

CHAPTER III. RECOGNITION OF ION PAIRS IN WATER USING A SIMPLE

FLUORIMETRIC SENSORS ARRAY

3.1 Introduction

Recognition of ion pairs1,2,3 is an important area of research of bio-medical4 and

environmental5 significance. As a result, much efforts have been devoted to developing the systems useful for ion pairs sensing in recent years. A potential method for the designing of the ion pairs sensor is to synthesize ion-pair receptors which contains both a cation and an anion recognition site simultaneously.6 However, due to the experimental complexities

associated with tracking ion pairs in vary conditions and the synthetic challenges, the amount

of well developed ion pair receptors remains limited.7,8,9

The field of fluorescence sensing10 due to its high sensitivity11,12 offers an alternative to solving the long-standing problem of ion-pairs sensing.13,14 The formats that utilizing

arrays of multiple chemosensor elements in the field of fluorescence sensing are often used

to target complex analytes and alleviate the potential problems stemming from non-selective

(cross-reactive) nature of the chemosensors. Arrays of cross-reactive chemosensors often comprise a large number of highly redundant elements to assure accurate analysis. Efforts toward a rational design of array sensors based on the supramolecular association15

knonwledge to achieve a optimal number of chemosensors were recently described.16

In this study, we present the results that recognizing thirty-five ion pairs at pH 5-9 in

water obtained with a small sensor array consisting of six pH indicators doped into a polymer

matrix to prepare chemosensor elements. Polymers doped with chemosensor molecules were previously utilized in arrays by our group15-17and others.18 Yet, to our best knowledge, none

of these efforts demonstrated array sensing of ion pairs. That is, perhaps, reflecting the 40

complexity of the task and the fact that only a handful of studies13 have demonstrated sensing of ion pairs in water.

3.2 Selection of Polymer Matrix

As described in the chapter 1, the role of the polymer matrix in chemical sensing system not only plays in providing mechanical support for chemosensors, but also aids in transporting analytes into the system where the sensing event takes place. As a result, the tunable hydrophilic/hydrophobic characteristic of the polymer matrices can be used adjusting the preference of the sensor film for different analytes.19

We demonstrate the utilization of a polymer matrix that comprises moieties known to

bind both anions (amide) and cations (oligo-ethylene glycol) (Figure. 3.1). Such polymers

swell while drawing the analyte ions with water into the interstitial space in the matrix. To

highlight this active role of the polymer matrix, we decided to use pH indicators 3.1-36

(figure 3.2) that do not comprise a receptor for anion-binding with exception of 3.6 which comprises a thiourea moiety.

Polyethylene Oxide Urethane connectors Polybutylene Oxide (soft-hydrophilic) (hard-lipophilic) (soft lipophilic) O O O O O N N O n m H H

Figure 3.1. General molecular structure of poly(ether-urethane).

3.3 Selection of Chemosensors

The selection of chemosensors were guided by the following criteria: The

coordination chemistry of the chemosensor should allow for significant crossreactivity yet

certain extent of selectivity toward different ion pairs in diverse pH conditions; The

chemosensors should have strong absorption in the near UV (300-400 nm), in which range

the excitation light sources are currently available in our research group, as well as

reasonably strong emission in the visible region. Guided by the above criteria, we used five

off-the-shelf indicators20 (3.1- 3.5) and one currently available chemosensors (3.6) which was synthesized by our group (Figure 3.2) to construct a simple sensors array.

O O N N OH O Br Br

NaO O O HO O O NH Br Br uoresce n . Fl i (3 1) Auramine O (3.2) Ethyl Eosin (3.3)

H H N N O O O S OH OH OH Cl Cl

HO O O HO O O HO O O Br Br

4',5'-Dibromofluorescein (3.4) 2',7'-Diclorofluorescein (3.5) 3.6

Figure 3.2. Structure of pH indicators 3.1-3.6 used for ion-pairs recognition. With exception of 3.6, none of the molecules comprises an anion receptor (magenta).

3.4 Sensing of ion-pairs in Water

The sensing system for the ion pairs was fabricated by incorporating the

chemosensors (pH indicators 3.1-3.6) into a hydrophilic polyether-urethane (PEU) in THF solution and casting 200 nL of the chemosensor-PEU solution into a microwell array. The role of the polyether-urethane matrix is to provide mechanical support to the chemosensors and to draw the bulk aqueous analytes into the sensing system and partially strip the hydrate off the ions, thus rendering the ion pairs (cations and anion as well) available for the chemosensors and allowing recognition process to take place.

The high ability of this six-element array to recognize ion pairs (as well as cations

+ + + + and anions) is demonstrated using 35 ion pairs comprised by five cations (Li , Na , K , NH4

+ ------and tetrabutylammonium TBA ) and seven anions (F , Cl , Br , I , AcO , NO3 and H2PO4 ).

Each salt (cation-anion combination) measurement was carried out at five different pH levels

(pH=5, 6, 7, 8 and 9) in ten replicas. The responses in the blue, green, yellow, and red channels were recorded using a UV-excitation image station with four emission filters

following the method described in experimental section.16,17,21

Figure 3.3 shows the example of six-element responds to cations and anions. In spite

of the naked-eye observation does not reveal large changes between the analytes, upon

deconvolution into separate channels the variability in the response becomes clear. Figure 3.4

shows the varying fluorescence intensity in green channel obtained from the two arrays in

Fig. 3.3 to illustrate variability of the response. 43

3.1 3.2 3.3 3.4 3.5 3.6 3.1 3.2 3.3 3.4 3.5 3.6

Fig. 3.3 Examples of pH indictors 3.1-3.6 respond to cations and anions (0.5mM, 200nL). Left: Changes in the fluorescence intensity of the sensor-elements upon addition of aqueous nitrate salts. Right: Responses after addition potassium anion salts.

Fig. 3.4 Integrated fluorescence in the green channel shows the variability in the response of pH indictors 3.1-3.6 to cations and anions (0.5mM, 200nL). The raw signal variability is then propagated in the whole dataset.

While Figure 3.4 shows the signal variability in one channel, Fig 3.5 shows the relative response (heat) map to illustrate the variability across the whole dataset, the whole dataset comprises a total of 24×1,800 recorded values to include 10 replicas for each value recorded, in Fig. 3.5 the 10 replicas were averaged. Heat map thus shows relative signal variability of the 24×180 replica-averaged values.

Fig. 3.5 Relative response (heat) map of the recorded 24×180 replica-averaged values illustrates the high output signal variability recorded from the array across whole dataset.

3.5 Statistical Analysis of Multivariate Response Patterns

For ion pairs (cations and anions as well) recognition and classification, LDA was employed to test the array responses. In this study, the responses were organized into cation and anion subsets, each subset was divided into five pH groups, which were then split into anion(counter-cation) and cation(counter-anion) groups. Thus, double redundancy was built into the final dataset as the responses form, for example, Na-Cl and Cl-Na pairs give the same response (error ≤ 4%).

In order to investigate the effect of the anions on the discrimination capacity of the 6- member sensors array. LDA was first applied to evaluate the sub-dataset which describing the array response to ion pairs that were composed of different anions yet with the same counter-cation. Figure 3.6 shows an example which illustrating the LDA canonical score plots for the response of the sensors array to sodium series of ion pairs at pH range (5-9). For each pH, a dataset corresponding to 7 ion pairs and a control (10 trials each) was generated.

The left column in figure 3.6 shows LDA canonical score plots for the first 2 factors and the right column shows the first 3 factors at five different pH conditions. One can see that the sodium series of ion pairs can be clearly classified even only with the first two factors.

pH 5 NaF 10 NaCl

NaBr 0 NaI

NaAcO -10 F3 (12.1%) F3 NaNO F2 F2 (37.8%) 3

NaH2PO4 -20 -20 Control -10 F2 (37.8%)0 -20 -25 -20 -15 -10 -5 0 5 10 -10 10 0 F1 (42.3%) 10 F1 (42.3%)

pH 6 NaF NaCl 10 NaBr 5 NaI 0 NaAcO

-5

NaNO3 F3 (11.1%) F3 F2 F2 (38.1%) -10 NaH2PO4 -15 10 Control -20 0 10 0 -10 -20 -15 -10 -5 0 5 10 -10 F1 (44.7%) F2 (38.1%) -20 -20 F1 (44.7%)

pH 7 NaF 5 NaCl NaBr

10 NaI 0

NaAcO F3 (8.5%) F3

NaNO3 F2 F2 (21.4%) 0 -5 NaH2PO4

-30 Control -20 15 -10 10 F1 (62.5%)0 5 -25 -5 0 5 10 0 10 -5 F1 (62.5%) F2 (21.4%)

10 pH 8 NaF 10 NaCl 5 NaBr 5 NaI

0 NaAcO F3 F3 (8.6%)

NaNO3 F2 F2 (28.7%) -5 -5 NaH2PO4

-10 Control 0 10 -10 F1 (56.4%) 5 -10 0 10 0 10 -5 F1 (56.4%) -10 F2 (28.7%)

NaF pH 9 10 15 NaCl

10 5 NaBr NaI 5 0 NaAcO 0 NaNO F3 F3 (12.7%) -5 3 -5 F2 F2 (35.8%) NaH2PO4 -10 -10 -20 Control -10 -15 0 10 F1 (45.9%)10 0 -20 -10 0 10 20 20 -10 F1 (45.9%) F2 (35.8%)

Figure 3.6. LDA canonical score plots describe the response of the 6-member sensors array to 7 sodium series of ion pairs (500µM, 200nL) and a control at five different pHs (5, 6, 7, 8 and 9). Left column: LDA canonical score plots with the first two factors; Right column: LDA canonical score plots with the first three factors. The first 3 factors were used aimed at describing at least the 85 % of the entire variance contained in the dataset. For each pH, the cross-validation routine shows ≥90 % correct classification accuracy.

Then, LDA was applied to evaluate the data which describing the array response to ion pairs that were composed of different cations yet with the same counter-anion at certain

pH to investigate the effect of cations on the discrimination capacity of the 6-member sensors array. Figure 3.7 shows an example which illustrating the LDA canonical score plots for the response of the 6-member sensors array to fluoride series of ion pairs at pH range from 5 to 9.

For each pH, a dataset corresponding to 5 ion pairs (NaF, KF, LiF, NH4F and TBAF) and a

control (10 trials each) was generated. The left column in figure3.7 shows LDA canonical

score plots for the first 2 factors and the right column shows the first 3 factors at five

different pH conditions. One can see that the fluoride series of ion pairs can be clearly

classified even only with the first two factors. 48

pH 5 NaF

4 10 KF

5 LiF 0 NH4F 0 TBAF

F3 (4.6%) F3 -4 F2 F2 (11.2%) -5 Control

10 -20 -10 -10 5 -30 -20 -10 0 10 20 30 0 0 F1 (81.5%)10 -5 F1 (81.5%) 20 -10 F2 (11.2%)

pH 6 10 NaF

10 5 KF

LiF 5

0 NH4F 0

F3 F3 (8.7%) TBAF F2 F2 (16.0%) -5 -5 Control 10 -20 -10 -10 5 -20 -10 0 10 0 0 F1 (68.6%) F1 (68.6%)10 -5 F2 (16.0%)

pH 7 8 NaF KF 4 5 LiF

0 NH4F

0 (8.5%) F3 TBAF -4

F2 F2 (17.4%) -5 Control -10 F1 (70.3%)0 10 5 -10 10 0 -10 0 10 -5 -10 F1 (70.3%) F2 (17.4%)

pH 8 10 NaF 5 KF

LiF 5 0 NH4F

TBAF 0 F3 (8.0%) -5 Control F2 F2 (12.9%)

10 -5 -10 5 -20 -10 0 -20 -10 0 10 0 F1 (74.0%) F1 (74.0%) 10 -5 F2 (12.9%)

pH 9 NaF 10 KF

5 LiF

NH4F

0 TBAF

F3 (7.9%) F3 Control

F2 F2 (18.9%) -5

-5 -10 0 0 -10 5 -10 0 10 F2 (18.9%) 10 F1 (66.3%) F1 (66.3%)

Figure 3.7. LDA canonical score plots describe the response of the 6-member sensors array to 5 fluoride series of ion pairs (500µM, 200nL) and a control at five different pHs (5, 6, 7, 8 and 9). Left column: LDA canonical score plots with the first two factors; Right column: LDA canonical score plots with the first there factors. The first 3 factors were used aimed at describing at least the 85 % of the total variance contained in the dataset. For each pH, the cross-validation routine shows ≥90 % correct classification accuracy.

In order to determine whether the data obtained at five different pHs could be used for their recognition and classification at a particular pH or for pH independent detection within the range of pH (5-9), a LDA was carried out including all 360 trials(10 trails each for 35 ion pairs plus a control). Figure 3.8 shows an example which depicting the LDA canonical score plots for the response of the 6-member sensors array to 35 ion pairs at pH 7. To our surprise, the cross-validation routine shows 96% correct classification. It turns out that this is the first sensors array which possess such a highly discrimination capacity for ion pairs.

Na+ K+ Li+ NH + TBA+ 10 4

F-

5 Cl-

Br-

0 I-

AcO- F3 (8.9%) F3

-5 - NO3

H PO - -10 2 4 20 -10 10 -5 Control 0 0 5 F1 (49.6%)-10 10 -20 15 F2 (22.5%)

Figure 3.8. LDA canonical score plots describe the response of the 6-member sensors array to 35 ion pairs and a control ((500µM, 200nL, 10 trails each) at pH 7. The first 3 factors were used aimed at describing at least the 80 % of the total variance contained in the dataset. The cross-validation routine shows 96 % correct classification.

Finally, the cross-validation routine (leave-one-out) of LDA was carried out to each sub-dataset to test the discrimination capacity of the array. The results (classification accuracy) of the cross-validation at the pH range (5-9) are shown in table 3.1 (7 anions),

table 3.2 (5 cations), and table 3.3 (35 ion pairs). The calculated classification accuracy of

the aqueous analytes was found to be high compared with those reported results. The

observed accuracy is a direct result of the signal variability in each of the dataset. Both

cations and anions were classified at the highest average accuracy at pH 6 and 7 (in both

cases 97%). Likewise, the ion pairs are best classified at pH 6 and 7. Combining the data

from several pHs or use of non-linear classification method such as artificial neural network

support-vector machines.21 may further improve the classification accuracy. In contrast to

other off-the-shelf systems and our preliminary data obtained from solution-based array

using the same indicators, the present array sensor is capable of distinguishing among ion

pairs at different pH level.20

Table 3.1. Classification accuracy for anions at different pH values obtained from LDA using leave-one-out cross-validation.

pH ------F Cl Br I AcO NO3 H2PO4 5 98% 97% 100% 93% 90% 92% 98%

6 100% 93% 97% 100% 97% 98% 97%

7 90% 98% 100% 98% 93% 100% 97%

8 95% 97% 100% 95% 92% 97% 95%

9 100% 100% 100% 95% 95% 97% 93%

Table 3.2. Classification accuracy for cations at different pH levels obtained from LDA using leave-one-out cross-validation. pH Li+ Na+ K+ NH4+ TBA+

5 100% 98% 90% 86% 94%

6 99% 96% 100% 100% 100%

7 95% 96% 93% 100% 100%

8 100% 93% 94% 100% 94%

9 98% 99% 93% 98% 98%

Table 3.3. Classification accuracy for ion pairs at different pH values obtained from LDA using leave-one-out cross validation. pH 5 6 7 8 9

Classification accuracy 89% 95% 96% 93% 93%

Due to the sophisticated molecular behavior of the polymer matrix-pH indicator material, it is difficult to assign the observed features to individual contributors. From the supramolecular perspective, the poly(ether-urethane) comprises both cation-binding (glycol- ether) and anion-binding (amide-type N-H) moieties, which display different affinity to different ions. It is known that the ionophore polymers take-up different ions with different affinity. From the signaling perspective, the pH indictors 3.1-3.6 are responding to the local pH equilibria in the material. Such local equilibria are affected by the pH of the analyte, pH product of the salts penetrating the uppermost polymer layers and possible ion-exchange processes between the H+/HO- of the hydrated polymer and the ionic analyte components. 53

This could explain the fact that each ion pair induced a unique response by the polymer/3.1-

3.6 sensor array.

3.6 Conclusion

In summary, we have shown that the simplest off-the-shelf chemosensors yield sensor

arrays capable of sensing ion pairs in water at a relatively wide range of pH (5-9), a feat generally observed only with structurally complex sensors. The six pH indicators 3.1-3.6 were doped into a poly(ether-urethane) matrix and used in an array to recognize and sense cations, anions and ion pairs. Specifically, the polymer/3.1-3.6 array comprising only six chemosensor elements was demonstrated to recognize five cations, seven anions and thirty- five ion pairs. Such a high discrimination capacity and recognition efficiency (6:35) with

≥93% accuracy for ion pairs recognition, to our best knowledge never get accomplished.

Even though the sensing was performed at a qualitative level, considering the low cost and wide availability of various polymer matrices and off-the-shelf pH indicators, this approach is likely to yield sensors with even higher discrimination capacity and, perhaps, be used also in quantitative determination of ion pairs. Such efforts are currently on the way in Dr.

Anzenbacher research laboratory.

3.7 Experimental Section

3.7.1 Chemicals and Solutions: Commercially available solvents and reagents were used as 54

received from chemical suppliers. Tetrahydrofuran was distilled from a K-Na alloy under argon. Sensors 3.1-3.5 were off-the-shelf available from the chemical stockroom of the

Center for Photochemical Sciences. Sensor 3.6 was originally synthesized by Pavel

Anzenbacher, Jr.

3.7.2 Preparation of Sub-microliter Sensors Array for Ion-pairs Sensing: The multi-well 10x8

(sub-microliter) chips were fabricated by ultrasonic drilling of microscope slides (a well

diameter: 1000 ±10µm, depth: 250 ±10µm). The sensor solutions (500µM) were prepared by

dissolving indictors 3.1-3.6 in a poly(ether-urethane) solution (4%, w/w), which was made

by dissolving poly(ether-urethane) in a mixture of solvents containing methanol and THF

(v/v: 0.5/9.5).

3.7.3 Ion-pairs Sensing Process in Water: In a typical sensor array, the sensor solution(200nL,

500µM) was spotted onto the wells of the multi-well chip using a Hamilton disperser with an overall 10µL capacity syringe , the anions (cations) were then added as aqueous solutions

(200nL, 500µM) of their corresponding salts at certain pH level to each well containing a sensor.

3.7.4 pH control experiment: . pH values of the analyte solutions were measured by Titrator

T50 (Mettler Toledo Co.). pH controlled experiments were carried out by adjusted with

NaOH (0.01 M) and HCl (0.01 M) aqueous solutions

3.7.5 Fluorescence Sensor Array Image Acquisition and Data Processing: Images from the sensor arrays were recorded using a Kodak Image Station 440CF. The scanned images (12 bit) are acquired with a resolution of 433x 441 pixels per inch and with grey levels over 1000

(12 second exposures). The sensor arrays are excited with a broadband UV lamp (300-400 nm, λmax=365 nm) and up to four channels were used for emission detection: (1) Blue: band- pass filter 380-500 nm λmax=435 nm, (2) Green: band pass filter 480-600 nm λmax=525 nm,

(3) Yellow: high-pass filter > 523 nm, (4) Red: high-pass filter > 590 nm. After acquiring the images, the integrated (non zero) grey pixel (n) value is calculated for each well of each channel by the software associated with the Image Station. Images of the sensor chip were recorded before (b) and after (a) the addition of an analyte and their final responses (R) were evaluated as follows:

a R ∑ n −= 1 b n n

3.7.6 Fluorescence Pseudo-color Image Acquisition: In order to generate a pseudo-color representation, images obtained using blue, green and red filters were merged in equal proportion using NIH ImageJ software.22

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CHAPTER IV. A FLUORIMETRIC SENSORS ARRAY FOR CARBOXYLIC DRUGS

DETECTION IN WATER AND URINE

4.1 Introduction

Growing concern over the impact of drugs abuse and side effect of drugs on human

health and public safety has lead to extensive interest in drugs detection for decades of years.1 Nevertheless, to detect carboxylic drugs in water and biological important yet highly

competitive milieu such as urine and saliva is still a challenging task due to the structure

similarity of the carboxylic drugs.2

In this work, we presented an attempt to use sensors array consisting of eight

chemosensors to detect carboxylic drugs both in water and urine. The 8-member sensors

array were prepared by casting the poly(ether)urethane/THF solution in which incorporated

with chemosensors into a multi-well microtiter plate. It is demonstrated that chemosensors

in the array displaying both colorimetric and fluorometric response outputs. We reasoned

that the dual-signal outputs are combined in an array can harness more discriminatory

information.

The targeted carboxylic drugs are showing in Figure 4.1. In a hope that enough

discriminatory data could be generated by the dual-signal output sensors array to

discriminate between fourteen carboxylate drugs: artesunate, diclofenac, flubiprofen,

ibuprofen, ketoprofen, L-alanine, L-thyroxine, L-tyrosine, mefenamic acid, mevalonic acid, naproxen, ritalinate, salicylate, sarcosine. 61

Cl HO O OH O O O H O H NH O O H Cl OH OH F O O H O Artesunate (D1) Diclofenac (D2) Flubiprofen (D3) Ibuprofen (D4)

O O I OH O H3C I OH OH OH HO H NH NH2 2 O I OH O H2N I O Ketoprofen (D5) L-Alanine (D6) L-Thyroxine (D7) L-Tyrosine (D8)

O OH CH O H 3 N CH3 HO O OH OH

HO OH O HN O

Mefenamic acid (D9) Mevalonic acid (D10) Naproxen (D11) Ritalinic acid (D12)

O OH O H OH N OH

Salicylic acid (D13) Sarcosine (D14)

Figure 4.1. Molecular structure of carboxylic drugs

4.2 Selection of Chemosensors for Preparation of Sensors Array

The sensor array is composed of eight chemosensors 4.1-4.8(Figure 4.2), forming an

8-member sensor array. Sensors 4.1-4.7 were originally synthesized by Nishiyabu following

the procedures described previously.3 Sensor 4.8 was synthesized by Zyryanov at

Anzenbacher Research laboratories.4

CN NC NC O N CN H R NH HN R =4.1, 4.2, 4.3 O . . . . H 4 4, 4 5, 4 6, 4 7 O CN N 4.1 4.2 4.3

meso-octamethyl calix[4]pyrrole N

CN NC N CN NH S NH N HN O F3C CF3 NH S NC O H N NH NC N S 4.4 . . 4.7 4. 4 5 4 6 8

Figure 4.2. Molecular structure of chemosensors contained in the sensors array for recognition of carboxylic drugs.

Chemosensors 4.1-4.7 are hydrogen-bonding based anion chamosensors which have a common receptor OMCP and substituted with an extended conjugated fluorophore. The change in fluorescence of chemosensors 4.1-4.7 originates in an intramolecular charge

transfer (ICT) due to the formation of anion-pyrrole hydrogen bond.5,6 As the anion attracts

the acidic proton involved in hydrogen bonding, the electronic density of the H-N bond shifts toward nitrogen and causes further polarization of the pyrrole electronic cloud. An acceptor attached through a conjugated bridge accommodates the excess partial charge, which in turn completing the partial charge transfer (ICT). In organic solvent, chemosensors 4.1-4.7 display a weak fluorescence (Φ4.1= 2.30%, Φ4.2= 0.29%, Φ4.3= 0.20%, Φ4.4= 0.15%, Φ4.5=

7 1.44%, Φ4.6= 3.34% and Φ4.7= 0.25%). However, chemosensors 4.1-4.7 display various

changes in their fluorescence in presence of anions. For instance, chemosensor 4.1

demonstrate fluorescence quenching property in presence of anions in DCM, while

chemosensor 4.3 display fluorescence turn-on and ratiometric response in dry DCM.7 63

In order to expand the response variability of the sensors array, a tripodal anion chemosensor 4.8 was also included in the sensors array. Chemosensor 4.8 displays an increase in fluorescence upon anion–receptor association.4 The fluorescence amplification originates in the binding of the anions to the thiourea moieties which results in limitation of the conformational freedom and improve the molecular rigidity, thus enhancing the electronic coupling between the chromophore and the hydrogen donors while restricting the vibrational and rotational modes, which would otherwise lead to nonradiative decay and weakening fluorescence output.7

The affinity constants of chemosensors 4.1-4.8 for anions4,8 are listed in Table 4.1. It clearly shows that the chemosensors display high binding constants toward carboxylates such as acetate and benzoate. This suggests that potentially the selected chemosensors are ideal candidates for sensing of carboxylic drug.

Binding constants [M-1] for sensors and anions

Anions Sensors

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

- F >1.0x108 2.1 x 106 2.5 x 106 1.0 x 107 1.0 x 107 1.6 x 106 1.6 x 105 3.8 x 106

- Cl 3.5 x 105 1.0 x 105 1.2 x 105 2.9 x 105 2.9 x 105 1.1 x 105 1.8 x 104 1.0 x 102

- AcO 2.6 x 106 4.2 x 105 4.1 x 105 5.8 x 105 1.1 x 106 2.8 x 105 9.9 x 104 1.0x 106

- 6 4 4 4 4 4 4 5 H2PO4 2.1 x 10 6.5 x 10 8.1 x 10 5.8 x 10 6.2 x 10 4.1 x 10 3.6 x 10 2.3 x 10

3- HPPi ND 3.0 x 105 ND 4.8 x 105 ND 2.5 x 105 5.4 x 104 1.0 x 104

- PhCOO 3.0 x 105 1.4 x 105 1.4 x 105 3.1 x 105 4.4 x 105 1.0 x 105 1.8 x 105 ND

Table 4.1. The binding constants for anions in the form of tetra-n-butylammonium salts were determined in MeCN (4.1-4.7) and DMSO (4.8), respectively. 64

4.3 Sensing of Carboxylic Drugs in Water

For the assessment of carboxylic drugs sensing, we tried the array consisting of chemosensors 4.1-4.8 embedded in a hydrophilic poly(ether)urethane (PEU, Tecophilic® SP-

60D-40)(Figure 4.3) in THF and solution-casting 200 nL of the chemosensor-PEU solution into a microwell array. The targeted carboxylic drugs include 7 NSAIDs (salicylic acid, ibuprofen, naproxen, diclofenac sodium, flubiprofen, ketoprofen, mefenamic acid) and 7 other type drugs (ritalinate, L-thyroxine, artesunate, sarcosine, L-alanine, L-tyrosine,

mevalonic acid) involved in various diseases treatment.9 For instance, ritalinate is a

metabolite of the attention deficit disorder drug, artesunate can be used to treat malaria, L-

alanine can be utilized in dosimetric measurements in radiotherapy, etc.10

Figure 4.3. Molecular structure of poly(ether)urethane used in sensing of carboxylic drugs

In order to test whether the sensor array can yield significant response in presence of

carboxylic drugs, we conducted the proof-of-concept experiment, in which, both the

chemosensors and drugs are in a relative high concentration (1mM and 5mM respectively).

As shows in Figure 4.4, it is clear that even inspected by naked-eye, each drug generated a

distinctive optical response pattern to a single sensor element. 65

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Control 4.1 4.1

4.2 4.2

4.3 4.3

4.4 4.4

4.5 4.5

4.6 4.6

4.7 4.7

4.8 4.8

Figure 4.4. Fluorescence responses of 4.1-4.8 sensors array(200nL, 1mM) to 14 carboxylic drugs (200nL, 5mM) in H2O at pH 8.5. Pseudo-color representation generated by superimposing of the equally weighed images corresponding to RGB channels.

Encouraged by the preliminary experiment result, we first attempted to discriminate the 14 drugs in certain environment (pH8.5) by utilizing the 8-members sensor array. Since we don’t really know how much channels are necessary to obtain adequate discriminatory information before we evaluate the efficiency of the sensor array. We harnessed the information from both the fluorimetric and colorimetric mode. Figure 4.5 shows the response pattern of the sensors array in presence of aqueous drugs solution at pH 8.5, which obtained by observing the relative intensity in 15 ex/em channels (11 fluorimetric and 4 colorimetric respectively). 66

Figure 4.5. Response pattern of the 4.1-4.8 sensors array (200nL, 500µM) in presence of different carboxylic drugs in H2O (200nL, 500µM, pH8.5).

As hypothesized, each drugs induced a distinctive luminescence change in the individual sensor element at 15 ex/em channels. Hence, a multidimensional response pattern can be created by organizing the change in the luminescence (relative intensity) into a matrix.

Inspection the response pattern validated that the sensors array possessed the potential capability to discriminate the targeted carboxylic drugs.

The sensor array consisting of 8-member sensor elements generates a signal output in the form of a multidimensional response vector (120-dimensiones=15 channels × 8 sensors).

The signal output comprises both fluorimetric and colorimetric part, which are 88 dimensions

(11channels × 8 sensors) and 32 dimensions (4channels × 8 sensors) respectively. To explore the discriminatory capability of the sensors array, the signal output was evaluated by utilizing the statistical multivariate analysis method - principal component analysis (PCA). 67

Here, the PCA of the dataset (10 trials for each carboxylic drugs) acquired from the

8-member sensors array requires 15 dimensions (PCs) out of 119 to describe 95% of the

discriminatory range (12% of all PCs). This attests to an extraordinarily high degree of

dispersion of the data yielded by the sensor array consisting of only eight sensors. This

discrimination power is relative high even contrast with those reported in literatures.20,21

Moreover, the PCA score plot (Figure 4.6) shows clear clustering of the data with only the first three PCs (representing 69.4% of variance) were plotted. The high dispersion level of the data shown by the PCA score plot can be attributed to the selectivity of the chemosensors and the strong supramolecular interaction (hydrogen-binding) between the chemosensors and certain carboxylic drugs. Meanwhile, the chemosensors also demonstrate a good extent of cross-reactivity as inferred from the distinct response.

Generally speaking, it is the synergetic effect of the selective yet cross-reactive feature of the chemosensors provides the good resolution (separation) of the clusters

(carboxylic drugs) in the PCA score plot. This in turn attests to the high performance of the sensors array. 68

L-Tyrosine 4 Sarcosine Ibuprofen Ketoprofen Ritalinate 0 L-Alanine Mevalonic Acid Mefenamic Acid Flubiprofen PC3 (10.4%) PC3 Salicylate -4 Diclofenac Naproxen Artesunate L-Thyroxine Control -12-8 -8 -4 0 PC1 (38.4%)4 12 8 4 8 12 -4 0 -12 -8 PC2 (20.7%)

Figure 4.6. PCA score plot of the first three principal components of statistical significance for 150 samples (200nL, 500 µM in H2O, pH8.5, 14 carboxylic + control, 10 trials each) produced by the 4.1-4.8 sensors array. Percentages on the axes account of variance to each PC axis for a total of 69.4% of variance.

In addition to the PCA analysis, the multidimensional response pattern was further evaluated by the LDA to explore the discriminatory power of the sensors array. LDA is a statistical approach widely used for supervised dimensionality reduction, which can yield better results in contrast to the unsupervised method (PCA). LDA provides the classification accuracy and good resolution by using a cross-validation (leave-one-out) routine. In which,

one observation (data point) was leave out of the dataset while using the rest of the data as a

training set to generate the linear discriminant (LD) function. Then the LD function is used

to place the excluded observation back into the correct cluster. This routine is carried out to 69 each data point in the dataset, the overall ability to correctly classify the observations describes the discriminatory power of the sensor array.

Figure 4.7 is the LDA graphical output which shows canonical score plots for the first

3 factors (14 carboxylic drugs plus a control) at pH 8.5. In this assay, three factors describe

89.4% of the total information (variance) contained in the dataset. This graphical representation shows clusters of similar data and demonstrates the quality and predictability of the sensor array.

L-Tyrosine Sarcosine Ibuprofen40 Ketoprofen Ritalinate L-Alanine Mevalonic0 Acid Mefenamic Acid Flubiprofen

F3 (7.0%) F3 Salicylate Diclofenac-40 Naproxen Artesunate L-Thyroxine Control-80

-200 -400-500 -300 -100 -200 -100 F2 (18.1%)0 0 100 F1 (64.2%)

Figure 4.7. LDA canonical score plot for the response of 4.1-4.8 sensors array to 14 carboxylic drugs in water. The first 3 factors were used in order to describe at least the 80% of the total variance. Data sets (14 drugs + control, 10 trials) recorded at pH8.5, the cross- validation routine shows 100 % correct classification.

The cross-validation routine shows 100% accuracy for the classification of all drugs at pH 8.5(Table 4.2). This discriminatory power is remarkable given the fact that some drugs yielded very similar response profile to the sensors array.

Control D1 D10 D11 D12 D13 D14 D2 D3 D4 D5 D6 D7 D8 D9 %correct Control 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 D1 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 100 D10 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 100 D11 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 100 D12 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 100 D13 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 100 D14 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 100 D2 0 0 0 0 0 0 0 10 0 0 0 0 0 0 0 100 D3 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 100 D4 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 100 D5 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 100 D6 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 100 D7 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 100 D8 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 100 D9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 100 Total 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 100

Table 4.2. LDA jackknifed classification matrix shows 100% classification accuracy for 150 samples (200nL, 500µM in H2O, pH8.5, 14 carboxylic drugs + control, 10 trials each).

4.4 Size Optimization of Sensors Array: Exploring the Simplest yet Effective Sensors Array

for Sensing of Carboxylic Drugs in Water

The ultimate goal of this experiment is to develop a simple yet effective analytical

device in real-world application. Thus, we attempted to obtain a minimal size of sensors array while maintaining the discriminatory capacity. In this study, we have utilized the method as mentioned at the introduction part of this dissertation to optimize the number of elements of the sensor array.

Contribution of sensors to PCs Classification accuracy

Sensors PC1 PC2 PC3 LDA

4.1 8.422696 9.583064 30.18041

4.2 12.90573 11.03596 12.75775 Array 4.3 15.62282 13.25586 6.647381 4.1 4.2 4.3 4.4 4.4 15.86277 9.722977 6.421873 100% 4.5 4.6 4.7 4.8 ○A 4.5 16.12257 9.812077 10.03808 4.6 8.068733 12.74866 17.21085 4.7 14.82251 14.3462 6.745627 Excluded 4.2, 4.3, 4.4, 4.6, 4.7 4.8 8.172172 19.49519 9.998027 4.1 0.289077 0.309787 0.488307 4.2 _ _ _

4.3 _ _ _ Array 4.4 _ _ _ 100% ○B 4.5 0.469032 0.290616 0.167054 4.1 4.5 4.8 4.6 _ _ _ 4.7 _ _ _ Excluded 4.1 4.8 0.241891 0.399597 0.344639 4.1 _ _ _ 4.2 _ _ _

4.3 _ _ _ 4.4 _ _ _ Array 100% ○C 4.5 0.662606 0.266261 _ 4.5 4.8 4.6 _ _ _ 4.7 _ _ _ Excluded 4.8 4.8 0.307341 0.606671 _ 4.1 _ _ _ 4.2 _ _ _

4.3 _ _ _ 4.4 _ _ _ Sensor ○D 4.5 1.000 1.000 _ 98% 4.6 _ _ _ 4.5 4.7 _ _ _ 4.8 _ _ _

Scheme 4.1. Schematic illustration of the optimization process for the 4.1-4.8 sensors array by using PCA and LDA. The simplest version of sensors array containing 4.5 and 4.8 shows 100% classification accuracy for 14 carboxylic drugs in water.

The schematic process for rational optimization for the size of the sensors array is showing in Scheme 4.1. From top to bottom: (A) PCA for the complete data set of sensors

(4.1-4.8) shows that the main contributors for the dispersion are 4.5, 4.8, and 4.1 on the PCs with statistical significance. (B) Chemosensors 4.2, 4.3, 4.4, 4.6 and 4.7 were excluded from the data set and carried out again with PCA. PCA shows that the main contributors were 4.5 and 4.8. (C) S1 was excluded from the data set and PCA was carried out using the remaining data set. PCA shows that the main contributor was 4.5.

Besides PCA analysis, LDA with cross-validation routine was also carried out for the data sets generated for all version of sensors array with 14 carboxylic drugs. As showing in

Scheme 4.1, it is clear that even with a sensors array containing just only 2 sensor (4.5and

4.8), LDA can predict the drugs classes with 100% classification accuracy. To our knowledge, the sensors array containing just two sensing elements might be the first sensors array reported for analytes that are able to discriminate as many analytes as seven times the number of sensing elements with 100% classification accuracy.

Interestingly, the size reduction methodology first excludes chemosensor 4.2, 4.3, 4.4,

4.6 and 4.7. These excluded chemosensors display less sensitivity towards benzoate and acetate than 4.1 and 4.5 in acetonitrile. Further reduction the size of the array reveals that 4.5 is a better contributor to the discriminatory capacity than 4.1, by watching the affinity constant Table closely we can see that the 4.5 possess the highest binding affinity towards benzoate and acetate. This behavior confirmed that the binding affinity plays a crucial role in the sensing events.

4.5 Discriminatory Capacity of a Single Sensor 4.5

From Scheme 4.1 it can be seen that 4.5 is the main contributor to the discriminatory capacity of the sensors array. We therefore decided to further investigate the source of the

“information” generated by 4.5.

Figure 4.8 shows the response of 4.5 to different carboxylic drugs in fifteen channels

(left), it’s clear that the differentiable response might be generated even with one single sensor (4.5). LDA with cross-validation routine was carried out to the data set generated by

4.5 and shows 98% classification accuracy. LDA canonical score plots shows that no evident data points overlap in the graphical output (Figure 4.10 right).

0.6 L-Tyrosine20 Sarcosine Ibuprofen 0.5 Ketoprofen Ritalinate10 L-Alanine 0.4 Ex(430)-Em(480) Ex(430)-Em(535) Mevalonic Acid Mefenamic Acid 0.3 Ex(430)-Em(600) Ex(470)-Em(535) Flubiprofen0 Ex(470)-Em(600) Salicylate 0.2 Ex(500)-Em(535) Diclofenac Ex(500)-Em(600) Ex(UV)-Em(440) (6.6%) F3 Naproxen Artesunate 0.1 Ex(UV)-Em(535) 10 Ex(UV)-Em(480) L-Thyroxine Ex(UV)-Em(600) Control 0.0 color-Em(440)

Relative Intensity[a.u.] color-Em(480) color-Em(535) -8020 -0.1 color-Em(600) -40 -20 -0.2 -10 0 0 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14Control 10 F2 (9.8%) F1 (75.7%)

Figure 4.8. Left: Response profiles of chemosensor 4.5 to all carboxylic drugs in 15 channels. Right: LDA canonical score plots for the response of 4.5 to 14 carboxylic drugs in H2O. Data sets (14 drugs + control, 10 trials each ) recorded at pH8.5.

Considering the fact that the environments in which the chemosensors bind to carboxylic anions in acetonitrile and the chemosensors bind to carboxylic drugs in solvated polymer matrix are pretty similar, the sensing performance of 4.5 does not come as a surprise given the fact that the 4.5 possess the best binding affinity towards benzoate and acetate. 74

4.6 Sensing of Carboxylic Drugs in Human Urine

The detection of carboxylic drugs in biological media such as human urine is

clinically essential because the levels of drugs reflect the state of human health to a great

extent.11,12 However, to detect carboxylic drugs in urine is a challenging problem since the

human urine contains several intrinsic highly competitive contents (microalbumin, chloride,

creatinine, phosphates, carbonates, sodium, potassium, etc.).13 Thus, in order to accomplish the detection in urine, the sensing elements should possess the great characteristics that good sensors are supposed to have, for example, highly sensitivity, binding affinity and selectivity toward the certain analytes.14,15

In this study, sensing elements based on strong hydrogen-bonding chemosensors are

ideal probes for carboxylic drugs sensing in urine since they display high binding affinity,

selectivity and sensitivity toward most of the important target anions (Table 4.1). Like the

carboxylic drugs sensing in water, we tried the array consisting of chemosensors 4.1-4.8

embedded in a hydrophilic poly(ether)urethane (PEU, Tecophilic® SP-60D-40) in THF and spotting 200 nL of the chemosensor-PEU solution into a microwell array. The targeted carboxylic drugs urine solution then spotted into each well which has contain a sensor.

Figure 4.9 shows the preliminary result of the sensors array respond to the carboxylic

drugs in urine. As predicted, due to its intrinsic anions content, the urine itself turned on the

fluorescence and creating a unique fluorescence response pattern. Even so, by the naked-eye observation, one still can watch the distinctive optical response pattern of the chemosensors in the array induced by the presence of the carboxylic drugs with urine. This testified that the sensors array possess the potential ability to detect the carboxylic drugs in competitive milieu (urine). 75

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Control 4.1 4.1

4.2 4.2

4.3 4.3

4.4 4.4

4.5 4.5

4.6 4.6

4.7 4.7

4.8 4.8

Figure 4.9. Fluorescence responses of 4.1-4.8 sensors array (200nL, 1mM) to14 carboxylic drugs (200nL, 5mM) in urine at pH 8.5. Pseudo-color representation generated by superimposing of the equally weighed images corresponding to RGB channels.

In order to avoid omitting important discriminatory information, we also harnessed

the response information from both the fluorimetric and colorimetric mode, which obtained

by observing the relative intensity in 15 ex/em channels (11 fluorimetric and 4 colorimetric

respectively). Then, the multidimensional statistical method-PCA was applied to evaluate the

response pattern which was created by organizing the relative intensity obtained in different

channels into a matrix

Here, the PCA of the dataset (10 trials for each carboxylic drugs) generated by the 8-

member sensors array requires 20 dimensions (PCs) out of 119 to describe 95% of the

discriminatory range (17% of all PCs). This discrimination power is substantially high in

contrast to those reported in literatures.16,17

The PCA score plot (Figure 4.10) shows clear clustering of the data with the first three PCs (representing 65.8% of variance). The high dispersion level of the data shown by the PCA score plot reflects the fact that the 8-member sensors array possesses high 76

discriminatory capacity to the carboxylic drugs in urine at pH8.5. This further attests the high

binding affinity and cross-reactivity of the chemosensors and the strong supramolecular interaction (hydrogen-binding) as well.

L-Tyrosine Sarcosine 6 Ibuprofen Ketoprofen Ritalinate L-Alanine Mevalonic Acid 0 Mefenamic Acid Flubiprofen Salicylate

PC3 (13.1%) PC3 Diclofenac Naproxen -6 Artesunate L-Thyroxine Control -16 -8 PC1 (34.6%) 12 0 6 8 0 -6 PC2 (18.0%)

Figure 4.10. PCA score plot of the first three principal components of statistical significance for 150 samples (200nL, 500 µM in urine, pH8.5, 14 carboxylic drugs + control, 10 trials each) produced by the 4.1-4.8 sensors array. Percentages on the axes account of variance to each PC axis for a total of 65.8% of variance.

LDA was also applied to evaluate the discriminatory capacity of the sensors array toward the carboxylic drugs in urine at pH8.5. Figure 4.11 shows LDA canonical score plots for the response of the 8-member sensors array to 14 carboxylic drugs and a control. In this 77 assay, three factors describe 82.0% of the total information (variance) contained in the dataset. This graphical output shows clear clusters of similar data and demonstrates the sensor array’s predictability.

80 L-Tyrosine Sarcosine Ibuprofen Ketoprofen 40 Ritalinate L-Alanine Mevalonic Acid Mefenamic Acid Flubiprofen 0 Salicylate

F3 F3 (7.2%) Diclofenac Naproxen Artesunate L-Thyroxine -40 Control

-300 -200 50 -100 0 0 F1 (53.0%) -50 100 -100 F2 (19.8%)

Figure 4.11. LDA canonical score plots for the response of 4.1-4.8 sensors array to 14 carboxylic drugs in urine. The first 3 factors were used in order to describe at least the 80% of the total variance. Data sets (14 drugs + control, 10 trials) recorded at pH8.5, the cross- validation routine shows 100 % correct classification.

LDA cross-validation routine (leave-one-out) shows 100% classification accuracy for

14 carboxylic drugs in urine at pH 8.5(Table 4.3). This attests that the sensors array made of the 8 selected chemosensors possess the ideal discriminatory capacity even in highly competitive milieu.

Table 4.3 LDA jackknifed classification matrix shows 100% classification accuracy for 150 samples (200nL, 500µM in urine, pH8.5, 14 carboxylic drugs + control, 10 trials each).

4.7 Size Optimization of Sensors Array: Exploring the Simplest yet Effective Sensors Array

for Carboxylic Drugs in Urine

In view of the incredible discriminatory capacity demonstrated by the particular

sensors array in both water and urine media, we decided to further explore the fundamental

reasons for the high discriminatory capacity so that one can gain the referential experience

for future detection in competitive media. In order to achieve this goal, we use the same

method which was utilized in the sensors array size optimization in water to find out the best

discriminatory capacity contributor.

Contribution of sensors to PCs Classification accuracy

Sensors PC1 PC2 PC3 LDA

4.1 9.312987 27.45932 9.75681

4.2 9.87796 20.37963 13.16101 Array 4.3 10.7026 11.74071 17.67401 4.1 4.2 4.3 4.4 4.4 12.64225 8.413448 10.03901 100% 4.5 4.6 4.7 4.8 ○A 4.5 15.86347 6.407807 15.27376 4.6 15.78596 6.590481 5.563997 4.7 0.797246 5.839776 22.50509 Excluded 4.2, 4.3, 4.4, 4.6, 4.8 4.8 14.84229 13.16883 6.026308 4.1 0.308725 0.658314 0.159222 4.2 _ _ _

4.3 _ _ _ Array 4.4 _ _ _ 100% 4.1 4.5 4.7 ○B 4.5 0.47876 0.183031 0.195027 4.6 _ _ _ 4.7 0.212516 0.158655 0.645752 Excluded 4.1 4.8 _ _ _ 4.1 0.404655 0.779901 _ 4.2 _ _ _

4.3 _ _ _ 4.4 _ _ _ Array 100% ○C 4.5 0.595345 0.220099 _ 4.5 4.7 4.6 _ _ _ 4.7 _ _ _ Excluded 4.7 4.8 _ _ _ 4.1 _ _ _ 4.2 _ _ _

4.3 _ _ _ 4.4 _ _ _ Sensor ○D 4.5 1.000 1.000 _ 99% 4.6 _ _ _ 4.5 4.7 _ _ _ 4.8 _ _ _

Scheme 4.2. Schematic illustration of the optimization process for the 4.1-4.8 sensors array by using PCA and LDA. The simplest version of sensors array containing 4.5 and 4.7 shows 100% classification accuracy for 14 carboxylic drugs in urine. 80

The schematic process for screening the best contributor to the discriminatory

capacity is showing as Scheme 4.2. From top to bottom: (A) PCA for the entire data set of

chemosensors (4.1-4.8) shows that the main contributors for the dispersion are 4.5, 4.7, and

4.1 on the PCs with statistical significance. (B) Sensors 4.2, 4.3, 4.4, 4.6 and 4.8 were

excluded from the data set and PCA was carried out again with the rest data set. PCA shows that the main contributors were 4.5 and 4.7. (C) S1 was excluded from the data set and PCA was carried out using the remaining data set. PCA shows that the main contributor was 4.5.

At the same time of running PCA, LDA with cross-validation (leave-one-out) routine was carried out for the data sets generated for all version of sensors array with 14 carboxylic drugs and one control. As showing in Scheme 4.2, it is clear that the sensors array which contains 2 chemosensors (4.5and 4.7) can predict the drugs classes with 100% classification accuracy. This is not surprise given the fact that the sensors array has the similar highly discriminatory capacity for carboxylic drugs in water environment. Further exploration reveals that 4.5 is the best contributor to the discriminatory capacity which can provides 99% classification accuracy alone.

4.8 Conclusion

The experiments in this chapter demonstrates that the hydrogen-bonding based chemosensors embedded in hydrophilic poly(ether)urethane (PEU, Tecophilic® SP-60D-40) films may be used for the fabrication of optical sensors array that could applied in the detection of carboxylic drugs both in water and in even complex biological milieu such as human urine. 81

Two proof-of-concept experiments were carried out to test the predictability of the sensors array consisting of eight chemosensors toward the carboxylic drugs in water and urine. The fluorescent response pattern reveals that even by naked-eye inspection, each drug generates a distinctive optical response to a single sensor element in the array. This indicated that the sensors array possess the desirable discriminatory capacity toward the targeted carboxylic drugs.

Multidimensional statistical analysis method PCA and LDA were utilized to evaluate the response patterns of the 8-bember sensors array induced by the presence of the carboxylic drugs. LDA cross-validation routine (leave-one-out) shows 100% classification accuracy for

14 carboxylic drugs both in water and urine at pH 8.5. By utilization of the sensors array size optimization method, we found out that 4.5 is the best contributor to the discriminatory capacity of the sensors array, which can individually provide 98% and 99% classification accuracy for carboxylic drugs in water and urine respectively. In addition, a simple sensors array consisting of 4.5 and 4.8 can provide 100% classification accuracy in water, and another simple sensors array consisting of 4.5 and 4.7 can provide 100% classification accuracy in human urine. The difference of anions content in water and urine reveals the reasons for the components diversity of the two simple sensors array. Since the dominant anion is the carboxylic drugs in aqueous solution, the chemosensors which have the highest binding affinity with carboxylic group are prone to provide more contribution to the discriminatory capacity. However, in carboxylic drugs urine solution, the urine intrinsically contains ignorable chloride content, as a result, the chloride-selective yet carboxylic cross- reactive chemosensor 4.7 is prone to provide more contribution to the discriminatory capacity. This behavior confirms that it’s the synergetic effect of the selectivity yet cross- 82

reactivity as well as the highly binding affinity features of the chemosensors provides the

good discriminatory capacity. Also, it attests that to combinatorially assess the sensor

materials could improve the sensor arrays’ performance.

In summary, the successful detection of carboxylic drugs in aqueous and urine

environments using optical sensors array approach provides a promising alternative for the

development of analytical platforms in real-world applications. The further studies

concentrating on the quantification and determination of the analytes in aqueous and

biological importance melieu is on the way of Dr. Anzenbacher’s research group.

4.9 Experimental Section

4.9.1 Chemicals and Solutions: Commercially available solvents and reagents were used as

received from chemical suppliers. Tetrahydrofuran was distilled from a K-Na alloy under argon. Sensors 4.1-4.7 were originally synthesized by Dr. Ryuhei Nishiyabu following the procedures described previously.3 4.8 was synthesized by Dr. Grigory V. Zyryanov at Dr.

Anzenbacher Research laboratories.4

4.9.2 Preparation of Sub-microliter Sensor Arrays for Carboxylic Drugs: The sensor

materials were prepared by incorporating sensors 4.1-4.8 into polyurethane matrices, which

were prepared by casting 200 nL of solutions containing the sensor (approx. 0.08% sensor in

polyurethane, w/w) in a THF solution of TecophilicTM SP-60D-40 (4 % w/w) onto a multi-

well (submicroliter) plate as reported before.

4.9.3 Solution pH Value measurement experiment: pH was adjusted in solution by adding

NaOH (0.01 M) or HCl (0.01 M utilizing a Titrator T50 (Mettler Toledo Co.) with an

accuracy of pH ±0.1.

4.9.4 Fluorescence Sensor Array Image Acquisition and Data Processing: Images from the

sensor arrays were recorded using a Kodak Image Station 440CF. The scanned images (12

bit) are acquired with a resolution of 433×441 pixels per inch and with grey levels over 1000

(12 second exposures). The sensor arrays are excited with a broadband UV lamp (300-400

nm, λmax=365 nm) and up to three channels were used for emission detection: (1) Blue: band- pass filter 380-500 nm λmax=435 nm, (2) Green: band pass filter 480-600 nm λmax=525 nm,

(3) Yellow: high-pass filter > 523 nm. In order to generate a false color representation, images obtained using blue, green and red (high-pass>580 nm) filters were merged in equal proportion using NIH ImageJ software.18 The RGB triplet is assigned to correspond with the

color of the filter used. After acquiring the images, the integrated (non zero) grey pixel (n)

value is calculated for each well of each channel. Images of the sensor chip were recorded

before (b) and after (a) the addition of an analyte. The final responses (R) were evaluated as

indicated in equation 1. a R n −= 1 ∑ n bn

4.10 References

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(b) Abahussain, E. A.; Ball, D. E.; Matowe, W. C. Med. Prin. Pract. 2006, 15, 352.

(d) Schwartz, R. H.; Silber, T. J.; Heyman, R. B.; Sheridan, M. J.; Estabrook, D. M. Arch

Pediatr Adolesc Med. 2003,157, 158

(2) (a) Pil, K.; Verstraete, A. Ther. Drug Monit. 2008, 30, 402.

(b) Feng, S.; Guo, L. M. Chem. Pap. 2008, 62, 350.

(3) (a)The synthesis of 3.1-3.7 was carried out by Dr. Ryuhei Nishiyabu at Dr. Anzenbacher research laboratories.

(b) Nishiyabu, R.; Anzenbacher, Jr., P. Org. Lett. 2006, 8, 359.

128, 11496.

(d) Nishiyabu, R.; Anzenbacher, Jr., P. J. Am. Chem. Soc. 2005, 127, 8270.

(4) Zyryanov, G. V.; Palacios, M.; Anzenbacher, Jr., P. Angew. Chem. Int. Ed. 2007, 46,

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(5) Anzenbacher, Jr., P. Top. Heterocycl. Chem. 2010, 24, 205.

(6) Anzenbacher, Jr., P. Top. Heterocycl. Chem. 2010, 24, 237.

(7) Principles of Fluorescence Spectroscopy, 2nd ed. Lakowicz, J. R. Ed.: Kluwer: New

York, 1999.

(8) The anion binding studies and fluorescence titration of chemosensors 4.1-4.7 were carried

out by Dr. Zhuo Wang at Dr. Anzenbacher research laboratories. 85

(9) (a) Shah, R.; Darne, B.; Atar, D.; Abadie, E.; Adams, K. F.; Zannad, F. Fundam. Clin.

Pharmacol. 2004, 18, 705.

(b) Brater, D. C. Am. J. Med. 1986, 80 (1A), 62.

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(b) Van, E. A.; van, S. M.; Steverberg, E. W.; et al. Arch Pediatr Adolesc Med. 1995, 149,

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(d) Roberts, W. E. Dermatol. Ther. 2004, 17, 196.

(e) Sekiya, I.; Morito, T.; Hara, K.; et al. PharmSciTech, 2010, 11, 154.

(11) Turley, S. M. Understanding Pharmacology for Health Professionals, 3nd ed.; Prentice

Hall: New Jersey, 2002.

(12) (a) Schmidt,R. F.; Thews, G. Human Physiology, 2nd ed.; Springer-Verlag, Berlin, 1989.

(b) Cohen, B. J. Medical Terminology: An Illustrated Guide, 4th ed.; Lippincott Williams &

Wilkins: Philadelphia, 2003.

(13) (a) Kirschbaum, B.; Sica, D.; Anderson, F. P. J. Lab. Clin. Med. 1999 133, 597.

(b) Wang, C.; Liu, X.Y.; Zheng, H. F.; Li, W.; Xue, X. H.; Ding, Y. Y. Zhongguo Jijiu

Yixue 2008, 28, 978.

594. 86

(d) Kamel, K. S.; Ethier, J. H.; Robert, M. A.; Robert A. B.; Halperin, M. L. Am. J.

Nephrol. 1990, 10, 89.

(14) Demchenko, A. P. Introduction to Fluorescence Sensing. Springer: Ukraine, 2009

(15) (a) Lakowicz, J. R. Principles of Fluorescence Spectroscopy, 3rd ed., Springer: New

York, 2006.

(b) Narayanaswamy, R.; Wolfbeis, O. S. Optical Sensors: Industrial, Environmental and

Diagnostic Applications. Springer Verlag: Berlin, 2004.

(16) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.;

Walt, D. R. Chem. Rev. 2000, 100, 2595.

(17) Gardner, J. W.; Bartlett, P. N. Electronic Noses: Principles and Applications, Oxford

University Press: New York, 1999.

(18) (a) Rasband, W. S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland,

USA, http://rsb.info.nih.gov/ij/, 1997-2009.

(b) Abramoff, M. D.; Magelhaes, P. J.; Ram, S. J. Biophotonics Int. 2004, 11, 36.

CHAPTER V. LEVERAGING MATERIAL PROPERTIES IN FLUORESCENCE

SENSORS ARRAY: A PRACTICAL APPROACH

5.1 Introduction

As has been stated in the introductory chapter I, the sensing performance of sensor

array is not only determined by the nature of the sensor elements contained in the array, but

also could be affected by the environment where the sensing event takes place.1 Based on this

hypothesis, we decided to further explore the potential impact of the nature of polymer

matrix on the performance of the sensor array. If successful, it might open an avenue to

develop the performance of sensor array by optimizing the sensing environment. In addition,

this method could also be beneficial for shortening the time and cutting down the

investments which are required for synthesis of large amount of probes.

We have prepared an array of optical sensors by using ten different poly(ether-

urethane) matrices with varying co-monomer proportions doped with one single fluorescent

probe to yield fingerprint-like pattern of responses in the presence of various anions. The

poly(ether-urethane) matrices contain different proportions of anion-binding urethane

moieties and different hydrophilicity given by the ratio between ethylene glycol ether and

butylene glycol ether. This diversity of hydration behavior provides different environment

polarity, in which the recognition and self-assembly processes display enough diversity to allow for unique response of the fluorescent probe to the analytes.2

In this study, the array sensor is shown to recognize eight different aqueous anions

(acetate, benzoate, fluoride, chloride, phosphate, pyrophosphate, hydrogen sulfide and

cyanide) and 8 urine samples with 100% classification accuracy. In addition, given the fact

that the utility of fluorescent sensing in quantitative studies is still a major concern,3 we 88

decided to further explore the applicability of this kind of sensing platform in real-world

problems. In this work, one proof-of-concept experiment attempted to test the potential for

quantitative sensing of anions was performed, we have tested the responses generated by the

individual sensor (PU-6/probe 5.1) to variant concentrations of non-steroidal anti- inflammatory drugs (NSAIDs), such as diclofenac and ibuprofen in human saliva. The fact that the PU-6/probe 5.1 shows a reasonable dynamic range and limit of detection (LOD) indicates the potential strength of these materials in quantitative sensing.

5.2 Selection of Chemosensor

The selected fluorescent probe 5.1 was synthesized in Dr. Anzenbacher’s research laboratories.4 As have depicted in previous chapter, 5.1 is an octamethylcalix[4]pyrrole

(OMCP) derivative capable of anion binding while displaying an anion-specific change in

fluorescence. The affinity constants of 5.1 for anions in dichloromethane (DCM) and acetonitrile (MeCN) recorded as part of previous studies5 in Dr. Anzenbacher’s laboratories

are listed in Table 5.1. It turns out that the selectivity of 5.1 toward different anions in both

- - - - - DCM and MeCN follows a similar trend: F >> AcO > BzO > Cl > H2Pi . However, the proportion of the binding constant magnitudes between different anions varies. For example, in DCM solvent 5.1 displays affinity twice higher for benzoate over chloride while in MeCN the benzoate/chloride affinities are almost equal. These data confirm that the supramolecular behavior of 5.1 depends on the media in which the recognition event takes place. In this study, we will show that this feature can be extended to polymer matrices and applied to prepare high discriminatory power sensor arrays for recognition of anions and human urine samples.

Table 5.1. Structure of the fluorescent probe 5.1 and the affinity constants (M-1) for aqueous anions at 22 °C from UV-Vis spectrometry titrations. a Tetra-n-butylammonium salts of anions were used. All errors are < 15 %. bThe isotherm showed biphasic behavior indicating multiple equilibria.

5.3 Selection of Polymer Matrices

Our previous works have shown that chemosensors embedded in hydrophilic PUs can

recognize various analytes including aqueous anions, cations, ion pairs and carboxylic

drugs.6 In the sensing system, PUs lend support to the chemosensors that are otherwise

insoluble in water but are compatible with the amphiphilic matrices capable of absorbing a

certain amount of water. Hydrophilic PUs absorb water and transport aqueous

electrolytes7,8,9,10 aided by a swelling process driven by high negative enthalpy of hydration.11,12 One of the advantages of PUs is that their water uptake can be tailored, for

example, by varying the composition of polyether segments based on polybutylene oxide

(PBO) to a more hydrophilic polyether, such as polyethylene oxide (PEO).13,14,15 The general

molecular skeleton of poly(ether)urethane used in this study is shown in Figure 5.1.The

amount of water absorbed by the PUs relative to the polyethylene oxide(PEO) content. 90

Polyethylene Oxide Urethane connectors Polybutylene Oxide (soft-hydrophilic) (hard-lipophilic) (soft lipophilic) O O O O O N N O n m H H

Figure 5.1. General molecular skeleton of Poly(ether)urethane. Three segments with varying ratio: Polyethylene Oxide(PEO), Urethane connectors, Polybutylene oxide(PBO) determine the overall hydrophilicity of the polymer.

1H NMR characterization was carried out to determine the ratio of PEO:PBO (Table

5.2) of the polymers as part of our previous work in Anzenbacher’s research group.16 The 10

PUs used in this study are 100-120 kDa polymers and are described by the PEO:PBU relative

ratio: 0.2 (PU1), 0.5 (PU2), 1.0 (PU3), 1.4 (PU4), 2.5 (PU5), 4.8 (PU6), 10.00 (PU7), 23.0

(PU8), 34.5 (PU10).

PUs PU1 PU2 PU3 PU4 PU5 PU6 PU7 PU8 PU9 PU10

PEO:PBO 0.2 0.5 1.0 1.4 2.5 4.8 10.0 23.0 27.0 34.5

Table 5.2. Ratio of PEO:PBO in ten poly(ether)urethane matrices.

5.4 Sensing of Anions in Water and Urine

To investigate the impact of the polymer composition on the sensing process, we

prepared a 10-member sensors array by embedding probe 5.1 in ten different matrices (PU1-

PU10) and casting these 10 sensing elements in the wells of microtiter plate. The targeted anions (acetate, benzoate, chloride, fluoride, hydrogen sulfide, cyanide, hydrogen phosphate, and hydrogen pyrophosphate) were then added as aqueous solution in their TBA form salts. 91

The fluorescence intensities before and after the addition of anions were recorded and

compared. Figure 5.2 shows how different polymeric matrices impact the magnitude of the

response of the probe to the anions.

2.0 F - Cl- 2- HPO4 -1) 0 HPPi-3 - 1.5 AcO HS- CN- BzO- 1.0

0.5

0.0 Yellow Chennel Relative Response (I/I

-0.5 PU-1 PU-2 PU-3 PU-4 PU-5 PU-6 PU-7 PU-8 PU-9 PU-10 Polyurethane Grades

Figure 5.2. Relative response (yellow channel) of the sensing films prepared using PU1- PU10 matrices and fluorescent probe 5.1. Anion samples: 200 nL, 500 µM in water, pH 8.5. Yellow channel corresponding to the increase in the maximum at 590 nm was used

By inspecting the responses profile (Figure 5.2). one can see that the polymer/probe

5.1 sensors display a parabola-shaped response curve with the highest value obtained with

PU6. At the left part of the “parabola”, the magnitude of responses increases with the increase of the soft PEO co-monomer and the water uptake ability increased up to PU-6

(PEO:PBO = 4.8). However, starting from PU7 (PEO:PBO ≥ 10) the sensor response decreases. This is presumably because at PEO:PBO>5.0 most of the polymer consists of the soft hydrophilic domains consisting mostly of water, in which the ions remain solvated and cannot effectively bind to the fluorescent probe 5.1 92

In order to obtain adequate discriminatory information, the response of the sensors array to anions in all channels was recorded, the average response of 10 repetitions of each measurement showing as heat map in Figure 5.3.

Figure 5.3. Heat map of responses recorded for all the PU-probe 5.1 sensing films in all four emission channels (Blue, Green, Yellow and Red) shows a high level of useful variance in the sensor output.

The observations obtained during the sensing experiments led us to formulating the following plausible mechanism shown in Figure 5.4. (A)The hydrophilic PUs swell and absorb water including the analyte anions when the sensing films exposed to aqueous analytes. During this process, the analyte is partitioned between the hydrophilic and hydrophobic domains of the PU film. (B)The hydrophilic segments of the polymer matrix partially remove the hydrate water from the anion, while partly stripping the analyte anions and cations. The resulting ‘partially naked’ anions undergo an effective enthalpy-driven recognition process of the analyte by the receptor.17 (C)Following the recognition event the remaining water is dried off at room temperature to fully develop the response of the sensing films to analytes. 93

Figure 5.4. Schematic representation of the sensing process: (A) The aqueous analyte is introduced onto the sensing film. (B) The “semi-naked” anion is recognized by the receptor during the water uptake (swelling). (C) The remaining water evaporates and the sensor film reaches an equilibrium.

5.5 Evaluation of Discriminatory Power of the Sensors Array

To assess whether the fingerprint-like responses pattern corresponding to the relative selectivity profiles of the individual sensing element made of polymeric matrices/probe 5.1 have enough discriminatory information for the classification of the eight anions, a linear 94

discriminant analysis (LDA) was carried out. The cross-validated routine (leave-one-out) of

LDA revealed that each sensing element is capable of discriminating among eight different anions (7 replicas each, total 56 samples) with over 90% of classification accuracy. This indicates that the discriminatory power of any single sensing element can differentiate between the eight anions with 90% accuracy. Outstandingly, each sensing element displays a different selectivity profile toward different anions (Figure 5.5, top). This feature is very important because if PU1-PU10 displays a very similar fingerprint, using multiple sensor elements in a sensors array would result in signal redundancy and introduce unexpected noise without increasing the discriminatory power of the array. Encouraged by the evident fingerprint-like differences in the responses displayed by all ten sensor elements, we arrayed all ten materials to generate a 10-member sensors array. LDA was then carried out to the overall responses matrix. The cross-validated routine (leave-one-out) of LDA demonstrates that the 10-member sensors array is capable of discriminating among eight different anions

(7 replicas each, total 56 samples, 500µM, 200nL, pH8.5) with 100% of classification

accuracy (Figure 5.5, bottom).

PU-1 2.0 PU-2 PU-3

-1) PU-4 0 PU-5 1.5 PU-6 PU-7 PU-8 PU-9 PU-10 1.0

0.5

0.0 Yellow Chennel Relative Response (I/I

-0.5 F Cl H2Pi HPPi AcO HS CN BzO Anions

40 Fluoride Chloride 30 H Phosphate H2 Pyrophosphate Acetate 20 H Sulfide Cyanide 10 Benzoate

0 F2 F2 (27.5%)

-10

-20

-30 -40 -30 -20 -10 0 10 20 30 40 F1 (39.9%) Figure 5.5. Top: Anion-induced 10-dimensional response profile generated by the changes in relative response (yellow channel) of 10-member sensors array (probe 5.1 embedded in 10 different PEU matrices). Bottom: LDA score plot of the first two factors describing circa 67% of the total variance for all eight anion samples (7 trials each). Cross-validated LDA (leave-one-out) shows 100% accurate classification of all 56 samples of 8 anions.

To investigate how the increasing of the number of sensing films promotes the array

discriminatory power, we have systematically increased the size of the sensor array and

plotted the LDA score plots respectively (Figure 5.6). The criterion for choice of the sensors

for the array is based on the overall intensity of the response (Figure 5.5, top). Obviously, the

sensing elements made of PU4-PU7 have the stronger responses among the 10 sensor

members. Also, PU4-PU7 sensors appeal to have better selectivity toward the analytes

(difference in response magnitude).

Panel I in Figure 5.6 shows the LDA score plot generated by the response of a single sensor element PU6 : probe 5.1, even though the 2D (F1vsF2) graph shows rather overlapped clusters the cross validated (leave-one-out) routine of LDA shows classification accuracy

>90%. In the second analysis (Panel II), we added the sensor element PU7: probe 5.1 to the array to make up a 2-member sensors array. In contrast to Panel I, the LDA score plot in

Panel II shows that the canonical space where the scores are plotted increases and better cluster resolution is achieved. A similar trend was observed when the size of the sensors array was doubled to four sensors (Panel III) (PU4-PU7), the cross validated (leave-one-out) routine of LDA shows classification with 100% accuracy and even better cluster resolution than Panel II. All three plots (Panel I, II, and III) display around 85% of the total variance of the system. Finally, when all ten sensors are assembled, the LDA canonical space expands and a full resolution can be observed by displaying just 67% of the variance of the system, while there is another three canonical factors describing ca. 32% for further differentiation.

However, 100% correct classification was already achieved in Panel III corresponding to the

4-sensor array. The fact that a single probe when embedded in four different polymer matrices can recognize eight different analytes with 100% accuracy attests to the significance 97

of the environmental effect of the matrix on the sensing process. From the LDA

eigenanalysis, we can estimate that there is a 30-fold increment in the total dispersion of the system, which translates in a bigger space response when going from array (I) to array (IV).

This is achieved through a very simple process of doping a single fluorescent probe into various polymer matrices.

F1 (58.0%) F1 (46.0%) Fluoride Chloride H Phosphate 30 I: 90% II: 98% H Pyrophosphate Acetate 20 H Sulfide Cyanide 10 Benzoate

F2 F2 (26.5%) -10 F2 (38.6%) -20

30 III: 100% IV

0 F2 (27.5%) F2 F2 (35.6%) -10

-20

-40 -30 -20 -10 0 10 20 30 -40 -30 -20 -10 0 10 20 30

F1 (46 9%) F1 (39 9%) Figure 5.6. LDA score plot of the first two factors of four sensor arrays corresponding to different number of sensing elements and the clustering of eight anion samples (7 trials each, 500 µM, 200 nL, pH 8.5). Panel I: Single sensor PU6 displays 90% cross-validated LDA (leave-one-out) classification accuracy of 56 samples tested in the array. Panel II: 2-member array (PU6+PU7) displays 98% accuracy. Panel III: 4-member array (PU4-PU7) displays 100% accuracy. Panel IV: 10-member array displays 100% accuracy.

The fact that four of these sensor films doped with a single fluorescent probe when

assembled in an array can recognize 8 aqueous anions is unprecedented in the field of 98

sensing of aqueous anions. Our observation suggests that in these sensor materials the probe

with its cross-reactivity pattern in combination with the PUs matrices provide complementary selectivity, which leads to very unique supramolecular behavior. From the perspective of analytical supramolecular chemistry, the field of sensor arrays could benefit

greatly from this finding. This is an inexpensive and convenient way to obtain more

information from sensor arrays as it provides an easy-to-perform method to increase the sizes of the arrays without any demand on the synthesis of new receptors and molecular probes.

5.6 Sensing of NSAIDS in Water and Saliva

The successful detection of eight anions by using this kind of sensing platform suggests potential applicability in real-world problem. Based on the performance of the sensing platform, we have developed calibration schemes for quantification of the analytes and the determination of the suitability of this kind of assays when challenged with competitive background (e.g. human saliva samples with different level of electrolytes).

Ibuprofen and diclofenac are cyclooxygenase inhibitors, thus usually used as active ingredient in the anti-inflammatory drugs.18 Whereas, for the sake of side effect induced by

the overdoses and potential negative impact on environment,19 the necessity get growing for

the quantitative test the contents of the drugs. In this study, we have tested the response of

the individual sensors to variant concentrations of non-steroidal anti-inflammatory drugs

(NSAIDs), such as ibuprofen and diclofenac in human saliva. Figure 5.7 and 5.8 shows calibration curves for the two NSAIDs in water and in saliva(10×diluted). The sensors display similar limit of detection (~100 nM) in either matrix. The selective yet cross- reactivity nature of calix[4]pyrrole (probe 5.1) based sensor is the main feature responsible 99 of the discriminatory power displayed by our sensor arrays. One drawback of the cross- rectivity is that the dynamic range of the sensor might be compromised when real analytical media contain an anionic background that is cross-reactive with the sensor array. Figure 5.7 and 5.8 shows that, although the sensitivity of the assay toward the analyte remains high, the dynamic range of operation in saliva reduced to a few µM compared to water where the calibration curve does not display saturation at the concentration range investigated.

Ibuprofen/Saliva Ibuprofen/Saliva Ibuprofen/H O 2 Ibuprofen/H2O

1.0 1.0

0.5 0.5 (Yellow Channel) (Green Channel) Normalized Response

0.0 0.0 0 100 200 0 100 200

0.6

0.3 0.3 (Yellow Channel) (Green Channel) Normalized Response

0.0 0.0

0 1 2 0 1 2 [Ibuprofen] µΜ [Ibuprofen] µΜ

Figure 5.7. Relative response (Left: green channel; Right: yellow Channel) of the sensing film prepared by using PU6 matrix and probe 5.1 to different concentrations of Ibuprofen.Top: Ibuprofen calibration curve in water and saliva at pH 7 at concentration 0-200µM. Bottom: Ibuprofen calibration curve in extreme diluted saliva. 100

Diclofenac/Saliva Diclofenac/Saliva Diclofenac/H O Diclofenac/H2O 2 1.0 1.0

0.5 0.5 (Green Channel) (Yellow Channel) Normalized Response

0.0 0.0

0 100 200 0 100 200 [Diclofenac] µΜ [Diclofenac] µΜ

0.2

0.1 0.1 (Yellow Channel) (Green Channel) Normalized Response

0.0 0.0

0 1 2 0 1 2 [Diclofenac] µΜ [Diclofenac] µΜ

Figure 5.8. Relative response (Left: green channel; Right: yellow Channel) of the sensing film prepared by using PU6 matrix and probe 5.1 to different concentrations of Diclofenac.Top: Diclofenac calibration curve in water and saliva at pH 7 at concentration 0-200µM. Bottom: Diclofenac calibration curve in extreme diluted saliva.

5.7 Sensing of Human Urine Samples

To further demonstrate the potential utility of the described sensor in multi-analyte

environment, an analysis of human urine samples was carried out. Urine is a multi-

electrolyte matrix containing phosphate, chloride, sulfate, bicarbonate, creatinine, sodium,

potassium and other dissolved electrolyte.20,21 In mammals the electrolyte contents are controlled by the kidney to maintain the electrolyte balance of the serum, thus, urinary electrolyte levels are of interest and could indicate systemic disease.22 In this study, we have 101 demonstrated the recognition of 8 urine samples by utilizing the described sensor. Among the urine samples, 7 of which obtained from healthy volunteers, the human urine samples were pre-treated by centrifuged and the supernatant layer were collected. In order to evaluate the discriminatory power of the described sensors array, artificial urine was also added to the urines pool. Figure 5.9 shows the LDA score plot for the sensors array responses to the eight urine samples, the corresponding leave-one-out routing shows 100% classification accuracy of the 8 urine samples.

AU: Phosphate only

Lower Higher

6: 180/79 1: 68/33 5: 201/35 2: 70/35 7: 268/48

3: 53/63 4: 123/10

Figure 5.9. Urine analysis using the PU1-PU10:probe 5.1 sensors array. LDA score plot of 8 urine samples show a clear clustering and pattern of correlation with total chloride /phosphate concentrations (meq/L).

102

Interestingly, the artificial urine cluster appeals to be far away from the 7 real human

urine samples in the LDA canonical space. This is probably because the artificial urine

contains only phosphate electrolyte, whereas, the real urine sample contains

chloride/phosphate/sulfate and other kind of electrolytes, which result in the 7 real urine

samples displaying a pattern following the increase in the total of chloride/phosphate

concentrations. Encouraged by this observation, we decided to further explore the overall discriminatory power of the established sensor array to all analytes including anions and urine samples. Figure 5.10 shows the LDA canonical score plot for the described sensor array responses to 8 anions and 8 urine samples. The LDA cross-validated (leave-one-out)

routine shows 100% classification accuracy for all 16 clusters.

40 Fluoride Chloride H Phosphate H Pyrophosphate 20 Acetate H Sulfide Cyanide Benzoate 0 Urine1 Urine2 Urine3

F3 (9.4%) F3 -20 Urine4 Urine5 Urine6 Urine7 -40 Urine8

-50 50 0 0 50 F1 (59.3%) -50 F2 (15.7%)

Figure 5.10. LDA canonical score plot for the response of established 10-member sensors array to 8 anions and urine samples. The LDA cross-validated (leave-one-out) shows 100% classification accuracy for all 16 clusters. 103

Besides Linear Discriminant Analysis (LDA), we also illustrated using hierarchical

clustering analysis (HCA) to explore the clustering of the data. In HCA dendrogram, the

distance between the clusters corresponds directly to similarities in the output data. Here, the

results of the HCA (Figure 5.11) shows also 100% correct classification for all analytes (16

clusters, 7 trails each). Interestingly, the HCA dendrogram demonstrates two main analyte

clusters: Anions on the left and urine samples on the right. One exception is the urine 8

(Artificial urine), which located in the anions area. When we take a close look at the contents

of the artificial urine, we find that the artificial urine contains only sodium phosphate dibasic,

but not sulfate or chloride. This might explain why the artificial urine cluster with the anion

clusters rather than the real urine samples. Together, these results indicate that the described

sensor array is capable of distinguishing multi-analytes in a simple manner.

Dendrogram (Ward Linkage, Euclidean Distance)

227.63

151.75 Distance

75.88

0.00 Urine1 Urine7 Urine4 Urine2 Urine3 Urine6 Urine5 Urine8 Acetate Fluoride Cyanide Chloride HSulfide Benzoate HPhosphate HPyrophosphate

Figure 5.11. HCA dendrogram obtained from Ward linkage showing the clustering and square Euclidean distance between the trials of anions and urine samples.

104

5.8 Conclusion

Preliminary experiments suggest that anion-sensitive yet cross-reactive chemosensor embedded in poly(ether)urethane matrices with various composition may be used as a new approach to develop the performance of sensor array capable of distinguishing anions. In the perspective of analytical supramolecular chemistry, the sensor arrays research field could benefit greatly from the findings in this study.23,24

In the described sensing system, the role of the fluorescent probe is to bind anions and generate signal output after the sensing event occurs, the primary role of the polymeric matrix is to internalize the analyte. This might be beneficial to the recognition process when the intrinsic selectivity of the fluorescent probe is insufficient to provide useful discriminatory information. The synergy of the receptor and the polymeric matrices generate unique fingerprint-like response patterns that can be utilized in sensor arrays to distinguish analytes.

In this study, the strategy of promote discriminatory power by increasing the number of sensor elements which were prepared by doping fluorescent probe 5.1 into different polymeric matrices is successfully achieved. This validates that we can achieve maximum discriminatory information by tuning the environment/matrix where the sensing event takes place. It also opens a way to increase the discriminatory power of sensor array by increasing the number of sensor elements using easily obtained materials, which can reduce the need to design and synthesis of new probes.

One proof-of-concept experiment was presented to illustrate the utility of the described sensor for quantitative detection. The fact that one single sensor element can detect 105

NSAIDs at extreme low concentration in competitive media is unprecedented. This suggests

that the established sensor possess the bright prospect in real-world application.

Finally, an experiment aimed at test the discriminatory power of the described sensor

array in multi-analytes environment was presented. The LDA and HCA results show 100%

classification accuracy of the sensor array toward eight anions and eight urine samples.

5.9 Experimental Section

5.9.1 Materials and Solutions: Commercially available solvents and reagents were used as

received from chemical suppliers. Tetrahydrofuran was distilled from a K-Na alloy under argon. Fluorescent probe 5.1 was originally synthesized by Dr. Ryuhei Nishiyabu following the procedures described previously. The poly(ether-urethane)s (PUs)were custom synthesized by Lubrizol Corp. (Wickliffe, Ohio) using the Company’s technology according to our requirements for proportion of the hydrophilic (polyethylene oxide, PEO) and lipophilic polybutylene oxide (PBO).

5.9.2 Fluorescent Titration Experiment: Fluorescent titration experiment of the fluorescent

probe 5.1 was carried out by Dr. Zhuo Wang and is included here to ease the understanding

of the impact of the polarity on the signal output.

5.9.3 Preparation of Sub-microliter Sensor Arrays for Anions Sensing: The sensor materials

were prepared by dissolving the fluorescent probe 5.1 (0.08%,w/w) in the polymer matrix

and casting the THF solution (200 nL) of the PUs (4 % w/w) into multi-well glass plates. In a 106

typical assay, the aqueous analyte solutions were added (200 nL, 500 µM) as their

tetrabutylammonium (TBA) salts at pH 8.5.

5.9.4 Solution pH Value Control Experiment: pH was adjusted in solution by adding NaOH

(0.01 M) or HCl (0.01 M utilizing a Titrator T50 (Mettler Toledo Co.) with an accuracy of

pH ±0.1.

5.9.5 Fluorescence Sensor Array Image Acquisition and Data Processing: Images from the

established sensor array were recorded using a Kodak Image Station 440CF. The scanned

images (12 bit) are acquired with a resolution of 433×441 pixels per inch and with grey

levels over 1000 (12 second exposures). The sensor arrays are excited with a broadband UV

lamp (300-400 nm, λmax=365 nm) and up to four channels were used for emission detection:

(1) Blue: band-pass filter 380-500 nm λmax=435 nm, (2) Green: band pass filter 480-600 nm

λmax=525 nm, (3) Yellow: high-pass filter > 523 nm, (4) Red: high-pass filter > 590 nm.

Images of the sensor chip were recorded before (b) and after (a) the addition of an analyte,

after acquiring the images, the integrated (non zero) grey pixel (n) value is calculated for

each well of each channel by the software associated with the Image Station 440CF. The

final responses (R) were evaluated as indicated in the following equation. a R ∑ n −= 1 b n n

107

5.10 References

(1) (a) Anzenbacher, Jr., P.; Liu, Y. L.; Kozelkova, M. E. Curr. Opin. Chem. Biol. 2010, 14,

693.

(b) Thomas, S. W. I.; Joly, G. D.; Swager, T. M. Chem. Rev. 2007, 107, 1339.

(2) (a) Sarkar, A.; Kaganove, S.; Dvornic, P.; Satoh, P. Polym News. 2005, 30, 370.

(b) Guan, Z. Bioinspired Supramolecular Design in Polymers for Advanced Mechanical

Properties, in Molecular Recognition and Polymers: Control of Polymer Structure and Self-

Assembly: Rotello, V. M.; Thayumanavan, S. Eds. John Wiley & Sons, Inc., Hoboken, NJ,

USA. 2008.

Light, M. E.; Gale, P. A. J. Am. Chem. Soc., 2006, 128, 12281.

(3) (a) Pinto, M. R.; Schanze, K, S. PNAS 2004, 101, 7505.

(b) Thaler, C.; Koushik, S. V.; Paul, S.; Blank, P.S.; Vogel, S. S. Biophysical Journal. 2005,

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2007, 52, 973.

(d) Li, X. J.; Li, P. C. H. Anal. Chem. 2005, 77, 4315.

(e) Danuser, G.; Waterman-Storer, C. M. Annu. Rev. Biophys. Biomol. Struct. 2006. 35, 361.

(4) (a)The synthesis of fluorescent probe 5.1 was originally carried out by Dr. Ryuhei

Nishiyabu at Dr. Anzenbacher research laboratories.

(b) Nishiyabu, R.; Anzenbacher, Jr., P. Org. Lett. 2006, 8, 359.

128, 11496. 108

(5) The anion binding studies of fluorescent probe 5.1 were carried out by Dr. Zhuo Wang at

Dr. Anzenbacher research laboratories.

(6) (a) Palacios, M. A.; Nishiyabu, R.; Marquez, M.; Anzenbacher, Jr., P. J. Am. Chem. Soc.

2007, 129, 7538.

(b) Zyryanov, G. V.; Palacios, M.; Anzenbacher, Jr., P. Angew. Chem. Int. Ed. 2007, 46,

7849.

Chem. Soc. 2008, 130, 10307.

(d) Wang, Z; Palacios, M. A.; Zyryanov, G.; Anzenbacher, Jr., P. Chem. Eur. J. 2008, 14,

8540.

(e) Wang, Z.; Palacios, M. A.; Anzenbacher, Jr., P. Anal. Chem. 2008, 80, 7451.

(f) Liu, Y; Palacios, M. A.; Anzenbacher, Jr., P. Chem. Commun. 2010, 46, 1860.

(7) Demchenko, A. P. J. Fluorescenc. 2010, 20, 1099.

(8) Dolmaire, N.; Espuche, E.; Mechin, F.; Pascault, J. -. J. Polym. Sci. Part B 2004, 42, 473.

(9) Lu, C.; Xu, Z.; Cui, J.; Zhang, R.; Qian, X. J. Org. Chem. 2007, 72, 3554.

(10) Mondal, S.; Hu, J. L. Des. Monomers Polym. 2006, 9, 527.

(11) Bouwstra, J. A.; Salomons-de Vries, M. A.; van Miltenburg, J. C.: Thermochim. Acta.

1995, 248, 319.

(12) Ferrer, G. G.; Paradas, M. M.; Ribelles, J. L. G.; Sanchez, M. S. Polymer 2004, 45, 6207.

(13) Chang, C. J.; Jaworski, J.; Nolan, E. M.; Sheng, M.; Lippard, S. J. Proc. Natl. Acad. Sci.

USA 2004, 101, 1129.

(14) Carol, P.; Sreejith, S.; Ajayaghosh, A. Chem. Asian J. 2007, 2, 338. 109

(15) Lomax, G. R. J. Mater. Chem. 2007, 17, 2775.

(16) The 1H NMR characterization of the polymers were carried out by Dr. Grigory V.

Zyryanov at Anzenbacher’s research laboratory.

(17) (a) Bouwstra, J. A.; Salomons-de Vries, M. A.; van Miltenburg, J. C.: Thermochim.

Acta 1995, 248, 319.

(b) Ferrer, G. G.; Paradas, M. M.; Ribelles, J. L. G.; Sanchez, M. S. Polymer 2004, 45, 6207.

(18) Salman, A. R. Am. J. Med. 1986, 80, 29.

(19) McElwee, N. E.; Veltri, J. C.; Bradford, D. C.; Rollins, D. E. Ann. Emerg. Med. 1990, 6,

657.

(20) (a) Batlle, D. C.; Hizon, M.; Cohen, E.; Gutterman, C.; Gupta, R. N. Engl. J. Med.

1988, 318, 594.

(b) Kirschbaum, B.; Sica, D.; Anderson, F. P. J. Lab. Clin. Med. 1999, 133, 597.

(21) Wang, C.; Liu, X. Y.; Zheng, H. F.; Li, W.; Xue, X. H.; Ding, Y. Y. Zhongguo Jijiu

Yixue 2008, 28, 978.

(22) (a) Giebisch, G. Annu. Rev. Physiol. 1962.24. 357.

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(23) Anslyn, E. V. J. Org. Chem. 2007, 72, 687.

(24) Analytical Methods in Supramolecular Chemistry. Schalley, C. A. Ed. Wiley-VCH:

Weinheim, Germany, 2007.

110

APPENDIX A: LIST OF ABBREVIATION AND SYMBOLES

ANOVA analysis of variance

ANN artificial neural networks

ATP adenosine triphosphate

BzO- benzoate

cm centimeter

DMSO dimethyl sulfoxide

EDG electron-donating group eq. molar equivalent

EtOH ethanol

EWG electron-withdrawing group

FRET Foster resonant energy transfer

GCMS gas chromatography coupled with mass-spectrometry

HCA hierarchical clustering analysis

HOMO highest occupied molecular orbital

IC-ICP ion chromatography coupled with inductively coupled plasma

ICT internal charge transfer

Ka association constant

LDA linear discriminant analysis

LoD limit of detection

LUMO lowest unoccupied molecular orbital

m meter

M molar (mol/liter)

µM micromolar (10-6 M) 111

MeCN acetonitrile

MeOH methanol nm nanometer

NMR nuclear magnetic resonance

NSAID Non-steroidal anti-inflammatory drug

OMCP meso-octamethylcalix[4]pyrrole

PCA principal component analysis

PET photo-induced electron transfer

PEU polyetherurethane

PPi hydrogen pyrophosphate

ppm parts per million

PLS-DA partial least squares discriminant analysis

PU polyurethane

RGB red, green and blue

SICA scale intensity consequence analysis

SOM self organizing maps

SVM support vector machines

UV-vis ultraviolet-visible 112

APPENDIX B: SUPPLEMENTARY DATA FOR CHAPTER III

Examples of response pattern: the response pattern of sensor array containing six off-the- shelf chemosensors responds to sodium series anions at pH 6.

Fluoride Chloride

0.25 0.2 0.7 0.15 blue 0.6 0.1 green 0.5 0.05 yellow 0.4 red 0 0.3 S1 S2 S3 S4 S5 S6 blue -0.05 0.2

green Intensity[a.u.] Relative 0.1 yellow -0.1 0 red -0.15 Relative Intensity[a.u.] Relative -0.1 S1 S2 S3 S4 S5 S6 -0.2

Bromide Iodide

0.4 0.1 blue 0.3 blue green 0.05 0.2 green yellow 0.1 yellow 0 red 0 red S1 S2 S3 S4 S5 S6 S1 S2 S3 S4 S5 S6 -0.05 -0.1 -0.2 -0.1 -0.3 Relative Intensity[a.u.] Relative Relative Intensity[a.u.] Relative -0.15 -0.4 -0.5

Acetate Nitrate

0.15 0.9 0.8 0.1 0.7 blue 0.6 green 0.05 0.5 yellow 0.4 blue red 0.3 green 0 0.2 yellow S1 S2 S3 S4 S5 S6 0.1 -0.05 0 red

Relative Intensity[a.u.] Relative -0.1 S1 S2 S3 S4 S5 S6 -0.2 Intensity[a.u.] Relative -0.1

-0.15

113

Dihydrogen phosphate

0.25 0.2 0.15 blue 0.1 gree 0.05 yell 0 red -0.05 S1 S2 S3 S4 S5 S6 -0.1 Relative Intensity[a.u.] Relative -0.15 -0.2

Examples of response pattern: the response pattern of sensor array containing six off-the- shelf chemosensors responds to for potassium series anions at pH 6

Fluoride Chloride

1.4 0.25 1.2 0.2 1 0.8 0.15 0.6 blue 0.1 0.4 green blue 0.2 yellow green red 0.05 0 yellow Relative Intensity[a.u.] Relative red -0.2 S1 S2 S3 S4 S5 S6 Intensity[a.u.] Relative 0 S1 S2 S3 S4 S5 S6 -0.4 -0.05

Bromide Iodide

0.6 0.1 0.5 blue 0.4 0.05 green 0.3 yellow blue 0.2 green 0 red yellow S1 S2 S3 S4 S5 S6 0.1 red -0.05 0 -0.1 S1 S2 S3 S4 S5 S6 -0.1 -0.2 Relative Intensity[a.u.] Relative Relative Intensity [a.u.] Intensity Relative -0.3 -0.15 -0.4

114

Acetate Nitrate

1.2 0.2 1 0.15 0.8 0.1 0.6 blue blue 0.4 green green 0.05 yellow 0.2 yellow red 0 red 0 S1 S2 S3 S4 S5 S6 Relative Intensity[a.u.] Relative S1 S2 S3 S4 S5 S6 -0.2 Intensity[a.u.] Relative -0.05 -0.4 -0.1

Dihydrogen phosphate

0.25 0.2 0.15 blue 0.1 green 0.05 yellow 0 red -0.05 S1 S2 S3 S4 S5 S6 -0.1 -0.15 Relative Intensity[a.u.] Relative -0.2 -0.25

Examples of response pattern: the response pattern of sensor array containing six off-the- shelf chemosensors responds to for tetrabutylammonium series anions at pH 9

Fluoride Chloride

blue 0.15 0.2 blue green 0.1 green 0.1 yellow 0.05 yellow 0 red 0 red -0.1 S1 S2 S3 S4 S5 S6 -0.05 S1 S2 S3 S4 S5 S6 -0.2 -0.1 -0.15 -0.3 -0.2 -0.4 Relative Intensity[a.u.] Relative -0.25 Intensity[a.u.] Relative -0.5 -0.3 -0.6

115

Bromide Iodide

blue blue 0.2 green 0.1 green 0.1 yellow 0 yellow S1 S2 S3 S4 S5 S6 red 0 red -0.1 -0.1 S1 S2 S3 S4 S5 S6 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 -0.5 -0.5 Relative Intensity[a.u.] -0.6 Relative Intensity[a.u.] -0.6 -0.7 -0.7

Acetate Nitrate

0.2 0.4 blue 0.1 green 0.3 blue 0 yellow 0.2 green red -0.1 S1 S2 S3 S4 S5 S6 0.1 yellow -0.2 0 red S1 S2 S3 S4 S5 S6 -0.3 -0.1 -0.4 -0.2 Relative Intensity[a.u.] Relative

Relative Intensity[a.u.] Relative -0.5 -0.3 -0.6 -0.4

Dihydrogen phosphate

0.2

0.15

0.1 blue green 0.05 yello 0 red S1 S2 S3 S4 S5 S6 -0.05

Relative Intensity[a.u.] Relative -0.1 -0.15

116

Example of linear discriminant analysis (LDA) for Ion-pairs:

Linear discriminant analysis of response arising from sensor array in the presence of ion- pairs at pH 7

pH 7

0 F2 (23.4%) -5

-10 -20 -10 0 10 20 30 F1 (49.1%)

117

APPENDIX C: SUPPLEMENTARY DATA FOR CHAPTER IV

Examples of response pattern: the response of sensor array containing 8 hydrogen bonding based chemosensors in the presence of 14 carboxylic drugs in water at pH 8.5

Artesunate

2 Ex(430)-Em(480) Ex(430)-Em(535) 1 .5 Ex(430)-Em(600) Ex(470)-Em(535) 1 Ex(470)-Em(600) Ex(500)-Em(535) 0 .5 Ex(500)-Em(600) Ex(0)-Em(440) 0 Ex(0)-Em(535) Ex(0)-Em(480) Ex(0)-Em(600) -0 .5 color-Em(440) color-Em(480) -1 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

Diclofenac

1.2 Ex(430)-Em(480) Ex(430)-Em(535) 1 Ex(430)-Em(600) 0.8 Ex(470)-Em(535) 0.6 Ex(470)-Em(600) Ex(500)-Em(535) 0.4 Ex(500)-Em(600) 0.2 Ex(0)-Em(440) Ex(0)-Em(535) 0 Ex(0)-Em(480) -0.2 Ex(0)-Em(600) -0.4 color-Em(440) color-Em(480) -0.6 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

Flubiprofen

1 Ex(430)-Em(480) Ex(430)-Em(535) 0.8 Ex(430)-Em(600) 0.6 Ex(470)-Em(535) Ex(470)-Em(600) 0.4 Ex(500)-Em(535) 0.2 Ex(500)-Em(600) Ex(0)-Em(440) 0 Ex(0)-Em(535) Ex(0)-Em(480) -0.2 Ex(0)-Em(600) -0.4 color-Em(440) color-Em(480) -0.6 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

118

Ibuprofen

Ketoprofen

1 .4 Ex(430)-Em(480) 1 .2 Ex(430)-Em(535) 1 Ex(430)-Em(600) Ex(470)-Em(535) 0 .8 Ex(470)-Em(600) 0 .6 Ex(500)-Em(535) 0 .4 Ex(500)-Em(600) Ex(0)-Em(440) 0 .2 Ex(0)-Em(535) 0 Ex(0)-Em(480) -0 .2 Ex(0)-Em(600) -0 .4 color-Em(440) color-Em(480) -0 .6 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

L-Alanine

0.5 Ex(430)-Em(480) Ex(430)-Em(535) 0.4 Ex(430)-Em(600) 0.3 Ex(470)-Em(535) Ex(470)-Em(600) 0.2 Ex(500)-Em(535) 0.1 Ex(500)-Em(600) Ex(0)-Em(440) 0 Ex(0)-Em(535) Ex(0)-Em(480) -0.1 Ex(0)-Em(600) -0.2 color-Em(440) color-Em(480) -0.3 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

119

L-Thyroxine

1.4 Ex(430)-Em(480) 1.2 Ex(430)-Em(535) Ex(430)-Em(600) 1 Ex(470)-Em(535) 0.8 Ex(470)-Em(600) 0.6 Ex(500)-Em(535) 0.4 Ex(500)-Em(600) Ex(0)-Em(440) 0.2 Ex(0)-Em(535) 0 Ex(0)-Em(480) -0.2 Ex(0)-Em(600) color-Em(440) -0.4 color-Em(480) -0.6 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

L-Tyrosine

1 Ex(430)-Em(480) Ex(430)-Em(535) 0.8 Ex(430)-Em(600) 0.6 Ex(470)-Em(535) Ex(470)-Em(600) 0.4 Ex(500)-Em(535) Ex(500)-Em(600) 0.2 Ex(0)-Em(440) 0 Ex(0)-Em(535) Ex(0)-Em(480) -0.2 Ex(0)-Em(600) -0.4 color-Em(440) color-Em(480) -0.6 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

Mefenamic acid

2 Ex(430)-Em(480) Ex(430)-Em(535) 1.5 Ex(430)-Em(600) Ex(470)-Em(535) 1 Ex(470)-Em(600) Ex(500)-Em(535) Ex(500)-Em(600) 0.5 Ex(0)-Em(440) Ex(0)-Em(535) 0 Ex(0)-Em(480) Ex(0)-Em(600) -0.5 color-Em(440) color-Em(480) -1 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

120

Mevalonic Acid

Naproxen

2 Ex(430)-Em(480) Ex(430)-Em(535) 1.5 Ex(430)-Em(600) Ex(470)-Em(535) 1 Ex(470)-Em(600) Ex(500)-Em(535) 0.5 Ex(500)-Em(600) Ex(0)-Em(440) Ex(0)-Em(535) 0 Ex(0)-Em(480) Ex(0)-Em(600) -0.5 color-Em(440) color-Em(480) -1 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

Ritalinic Acid

0.5 Ex(430)-Em(480) Ex(430)-Em(535) 0.4 Ex(430)-Em(600) 0.3 Ex(470)-Em(535) 0.2 Ex(470)-Em(600) Ex(500)-Em(535) 0.1 Ex(500)-Em(600) 0 Ex(0)-Em(440) Ex(0)-Em(535) -0.1 Ex(0)-Em(480) -0.2 Ex(0)-Em(600) -0.3 color-Em(440) color-Em(480) -0.4 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

121

Salicylic Acid

1 Ex(430)-Em(480) 0.8 Ex(430)-Em(535) Ex(430)-Em(600) 0.6 Ex(470)-Em(535) Ex(470)-Em(600) 0.4 Ex(500)-Em(535) 0.2 Ex(500)-Em(600) Ex(0)-Em(440) 0 Ex(0)-Em(535) Ex(0)-Em(480) -0.2 Ex(0)-Em(600) color-Em(440) -0.4 color-Em(480) -0.6 color-Em(535) color-Em(600) S1 S2 S3 S4 S5 S6 S7 S8

Sarcosine

0.4 Ex(430)-Em(480) Ex(430)-Em(535) 0.3 Ex(430)-Em(600) 0.2 Ex(470)-Em(535) Ex(470)-Em(600) 0.1 Ex(500)-Em(535) 0 Ex(500)-Em(600) Ex(0)-Em(440) -0.1 Ex(0)-Em(535) Ex(0)-Em(480) -0.2 Ex(0)-Em(600) -0.3 color-Em(440) color-Em(480) -0.4 color-Em(535) S1 S2 S3 S4 S5 S6 S7 S8 color-Em(600)

122

Examples of pseudo-color representation of fluorescence output of sensor array in the presence of carboxylic drugs

Drugs in water at non-pH control environment

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Control

Drugs in urine at non-pH control environment

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 Control S1

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APPENDIX D: SUPPLEMENTARY DATA FOR CHAPTER V

The response profiles of sensor array for different anions studied in all channels (BGYR)

Blue Green 1.6 0.6 Yellow Acetate Benzoate Red 1.4 0.5 1.2

-1) 0.4 0 1.0 0.3 0.8 0.2 0.6 0.1 0.4 0.0 0.2 Relative Intensity (I/I 0.0 -0.1

-0.2 -0.2

2.0 0.3 1.8 Cyanide Chloride 1.6 -1) 0 0.2 1.4

1.2 0.1 1.0 0.8 0.0

0.6 -0.1

Relative Intensity (I/I 0.4 0.2 -0.2 0.0 -0.2 -0.3

0.6 1.0 Hydrogen Sulfide Fluoride 0.5 0.8 -1) 0 0.4 0.6 0.3

0.4 0.2 0.2 0.1 Relative Intensity (I/I

0.0 0.0

-0.1 -0.2

1.4 1.4 1.2 Hydrogen Phosphate Hydrogen Pyrophosphate 1.2 -1)

0 1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4 0.2 0.2 Relative Intensity (I/I 0.0 0.0 -0.2 -0.2 PU-1 PU-2 PU-3 PU-4 PU-5 PU-6 PU-7 PU-8 PU-9 PU-10 PU-1 PU-2 PU-3 PU-4 PU-5 PU-6 PU-7 PU-8 PU-9 PU-10

124

Complete analysis of electrolytes in urine samples:

The electrolytes data of human urine samples provided by Wood County hospital.

Urine # Sodium Potassium Cations Chloride Phosphate Anions Cations Creatinine µ-album pH (mEq/L) (mEq/L) (mEq/L) (mEq/L) (mg/dL) +anions (mg/dL) (mg/L)

1 56 24.3 80.3 68 30-33.0 101 181.3 56.7 10 6.5 2 69 22.1 91.1 70 34.8 103.8 194.9 24.1 10 7.1 3 60 28.4 90 53 62.8 115.8 205.8 13.1 10 6.8 4 103 36.2 139.2 123 6.7-10 129.7 268.9 64.6 6 7.1 5 189 44.5 233.5 201 35.1 236.1 469.6 148 9 6.5 6 139 55.3 194.3 180 78.5 258.5 452.8 70.9 8 6.5 7 195 124.2 319.2 264 47.7 311.7 630.9 40.1 7 7.1 8 AU’s main components contain: Water; Sodium phosphate Dibasic: 5%; Vitamin C; Glucose: 1.0%; Albumin: 0.2%; Artificial Tegosept M: 0. 1%; Alizarin Yellow: 0.0032%; Thymol:<0.001%. U

PCA 3D plot shows the clustering of all anions and urine clusters

PCA

Fluoride Chloride H Phosphate 2 H Pyrophosphate Acetate H Sulfide Cyanide Benzoate 0 Urine1 Urine2

Urine3 PC3 (8.1%) PC3 Urine4 Urine5 -2 Urine6 Urine7 Urine8

-8 -4 0 0 PC1 (69.3%)4 -4 8 PC2 (17.5%)

125

Complete LDA score plot for the 10 sensing elements sensor array demonstrating the discrimination performance.

F1 (39.9%)

Anions

F1 (39.9%) F1 Acetate Benzoate Chloride

Cyanide Fluoride Hydrogen Phosphate

Hydrogen Pyrophosphate

F2 (27.0%) F2 Hydrogen Sulfide

F3 (23.4%) F3

F4 (6.4%) F4 F5 (1.7%) F5 (1.7%) F5

F1 (39.9%) F2 (27.0%) F3 (23.4%) F4 (6.4%) F5 (1.7%)