1 DEVELOPMENT OF SENSORS FOR MONITORING OXYGEN AND FREE RADICALS IN PLANT PHYSIOLOGY By PRACHEE CHATURVEDI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 20 14
2 2014 Prachee Chaturvedi
3 To my parents and my husband
4 ACKNOWLEDGMENTS I would like to gratefully and sincerely thank Dr. Eric S McLamore for his guidance, understanding and patience. I would also like to thank all of the members of the McLamore research group, especially Stephanie Burrs, Mashashige Taguchi and Diana Venegas for their support and lifelong friendship. I would also like to thank Dr. Bernie Hauser for his assistance and guidance during my graduate career. I would like to thank the Agricultural and Biological Department at University of Florida. Additionally, I would like to thanks members of my doctoral committee, Dr Melanie Correll, Dr Pratap Pullammanappallil and Dr Treavor Boyer, for their input, valuable discussions and accessibility. Finally, and most importantly, I would like to thank my husband Ritwik. His support, encouragement, quiet patience and love were the bedrock which helped me get through my graduate career. Thank you for everything. I thank my parents, L.K.Chaturvedi and Rekha Chaturvedi, for their unwavering faith and confidence in my abilitie s and in me. It was under their watchful eye that I gained so much drive and an ability to tackle challenges head on.
5 TABLE OF CONTENTS Page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION AND OVERVIEW ................................ ................................ ... 14 1.1 Background and Motivation ................................ ................................ .............. 14 1.1.1 Importance of Oxygen and Reactive Oxygen Species in Plants .............. 14 1.1.2 Tools for M onitoring Oxygen and Reactive Oxygen Species in P lants .... 18 1.2 Goals and Objectives ................................ ................................ ........................ 19 2 REVIEW OF CURRENT AND EMERGING TECHNOLOGIES FOR MONITORING OXYGEN AND REACTIVE OXYGEN SPECIES TRANSPORT IN PLANTS ................................ ................................ .................. 23 2.1 Fundamental Principles of Sensors ................................ ................................ .. 23 2.2 Review of Current and Emerging Technologies for Non Invasive Quantification of Physiological Oxygen Transport in Plants ................................ 27 2.2.1 Pr obes ................................ ................................ ................................ ..... 27 2.2.2 Microprobes ................................ ................................ ............................. 29 2.2.3 Microrespirometry ................................ ................................ .................... 2 9 2.2.4 S elf Referencing Flux Sensors ................................ ................................ 30 2.3 Current and Emerging Technologies for Non Invasive Quantification of ROS Transport ................................ ................................ ................................ ............. 31 2.3.1 Indirect NO Assays ................................ ................................ .................. 31 2.3.2 Fluorescent Probes for Imaging NO ................................ ........................ 32 2.3.3 Electron Paramagnetic Resonance ................................ ......................... 32 2.3.4 Amperometric Electrodes ................................ ................................ ........ 33 2.3.5 Hydrogen Peroxide Sensing ................................ ................................ .... 35 22.214.171.124 Metal hexacyanoferrates ................................ ................................ 35 126.96.36.199 Heme proteins ................................ ................................ ................ 35 188.8.131.52 Metals and metal oxides ................................ ................................ 36 3 DEVELOPMENT OF A MULTIPLEXING FIBER OPTIC MICROSENSOR SYSTEM ................................ ................................ ................................ ............. 43 3.1 Basic Optoelectronics ................................ ................................ ....................... 44 3.2 Fiber Optic Sensors ................................ ................................ .......................... 47
6 3.3 Materials and Methods ................................ ................................ ...................... 49 3.3.1 Sensor Fabrication ................................ ................................ .................. 49 3.3.2 Working Principle ................................ ................................ .................... 51 3.3.3 Sensor Calibration ................................ ................................ ................... 51 3.3.4 Pr ofiling O xygen Gradients in Soils ................................ ......................... 52 3.3.5 Canola Seed Oxygen under Elevated Ambient Oxygen Condition .......... 53 3.3.6 Statistics ................................ ................................ ................................ .. 55 3.4 Results an d Discussion ................................ ................................ ..................... 55 3.4.1 Sensor Calibration ................................ ................................ ................... 55 3.4.2 Profiling Oxygen Gradients in Soils ................................ ......................... 57 3.4.3 Measuring Oilseed (Brassica napus) Oxygen Levels .............................. 58 3.5 Conclusion ................................ ................................ ................................ ........ 60 4 EFFECTS OF HIGH TEMPERATURE AND HIGH OXYGEN PARTIAL PRESSURE ON SEED OXYGEN LEVELS IN GLYCIN E MAX L. MERR (SOYBEAN) ................................ ................................ ................................ ........ 67 4.1. Climate Change as an Abiotic Stress ................................ ............................... 67 4.2 Methodology ................................ ................................ ................................ ..... 68 4.2.1 Chamber Conditions ................................ ................................ ................ 68 4.2.2 Self Referencing Oxygen Flux Studies ................................ .................... 70 4.2.3 Statistics ................................ ................................ ................................ .. 72 4.3 Results and Discussion ................................ ................................ ..................... 73 4.4 Conclusion ................................ ................................ ................................ ........ 76 5 GRAPHENE METAL NANOCOMPOSITES FOR SENSING OXYGEN AND NITROGEN RADICALS ................................ ................................ ...................... 84 5.1 Background ................................ ................................ ................................ ....... 84 5.1.1 Relevance of Oxygen and Nitrogen Radicals in Biology .......................... 84 5.1.2 Tools for Monitoring Radicals ................................ ................................ .. 85 5.2 Methods ................................ ................................ ................................ ............ 88 5.2.1 Chemicals and Reagents ................................ ................................ ........ 88 5.2.2 Sensor Fabrication ................................ ................................ .................. 88 5.2.3 Sensor Characterization ................................ ................................ .......... 90 5. 2.4 Imaging of Nanomaterials ................................ ................................ ........ 91 5.2.5 Statistics ................................ ................................ ................................ .. 91 5.3 Results and Discussion ................................ ................................ ..................... 92 5.4 Co nclusion ................................ ................................ ................................ ........ 96 6 CONCLUSIONS AND FUTURE DIRECTIONS ................................ ................ 104 APPENDIX A SUPPLEMENTARY FIGURES ................................ ................................ ......... 105 B ROS ACCUMULATION IN ARABIDOPSIS THALIANA OVULES AND SEED ABORTION ................................ ................................ ................................ ....... 108
7 C DEVELOPMENT OF MUX ................................ ................................ ................ 112 LIST OF REFERENCES ................................ ................................ ............................. 118 BIOGRAPHICAL SKETCH ................................ ................................ ..................... .. 138
8 LIST OF TABLES Table page 4 1 Temperature regimes used for the experiments (curtsey: L. Zhang, L. H. Allen, Jr., K. J. Boote and B. A. Hauser) ................................ ............................. 83 5 1 Summary of performance for recent H 2 O 2 sensors using similar nanocomposites ( NR = value not reported in manuscript) ................................ 103
9 LIST OF FIGURES Figure page 1 1 Photophosphorylation produces ATP using light activated proteins that produce an electrochemical H + gradient and shuttle electrons to ATPase. ........ 20 1 2 Oxidative phosphorylation produces ATP via proteins that produce an electrochemical H + gradient and shuttle electrons to ATPase. ........................... 20 2 1 Conceptual physical model describing working scheme of sensor.. ................... 37 2 2 Calibration and Limit of Detection of sensors. ................................ .................... 37 2 3 Sensitivity and Selectivity of sensors.. ................................ ................................ 38 2 4 Response time for sensors is commonly determined using the t 95 method developed by IUPAC. ................................ ................................ ......................... 38 2 5 Due to the reversible recognition transduction acquisition scheme, all sensors have little or no hysteresis, which can be quantitatively determined as the percent change in signal output in the presence and absence of the target analyte. ................................ ................................ ................................ ..... 39 2 6 Frequency modulated excitation of an oxygen quenched luminescent dye (platinum tetrakis pentafluorophenyl porphyrin).. ................................ ................ 39 2 7 Conceptual relationship between analysis time and spatial scale for oxygen sensors .. ................................ ................................ ................................ ............. 40 2 8 Conceptual diagram showing the working mechanism for self referencing microsensors.. ................................ ................................ ................................ .... 41 3 1 Jablonski Energy Diagram describing fluorescence ................................ ........... 62 3 2 Phase modulated excitation ................................ ................................ ............... 62 3 3 PtTFPP molecule and an artistic rendering of the molecule together with titanium dioxide used as a photocatalyst (McLamore and Porterfield 2011) ....... 63 3 4 Schematic diagram of fiber optic sensor ................................ ............................. 63 3 5 Schematic of a fiber optic cable coated with an oxyg en sensitive dye such as PtTFPP.. ................................ ................................ ................................ ............. 63 3 6 Conceptual design of MUX. ................................ ................................ ............... 64 3 7 Representative absorption/emission spectra for micro optrodes.. ...................... 64
10 3 8 Calibration and lateral positioning. ................................ ................................ ...... 65 3 9 Design of experiment and results of oxygen profiling in soil.. ............................. 65 3 10 Design of experiment and results of oxyg en profiling in Brassica napus. ........... 66 3 11 Oxygen Concentration in the cold stressed and control plants at Dap 30 and DAP 34 ................................ ................................ ................................ ............... 66 4 1 Controlled plant growth facility. ................................ ................................ ........... 77 4 2 Design of experiment for oxygen profiling in soybean. ................................ ....... 77 4 3 Basic hardware for SR system ................................ ................................ ........... 78 4 4 Process flow for self referencing system (McLamore et al. 2010) ...................... 78 4 5 Oxygen concentration in G.max seeds with varying temperature (n=3). ............ 79 4 6 Results of oxygen profiling in sobean seeds. ................................ ..................... 79 4 7 R esults of oxygen profiling in soybean seed using microsensor. ....................... 80 4 8 Oxygen concentration in soybean seeds.. ................................ .......................... 81 4 9 Oxygen concentration in the cross sectional area inside the seed during light conditions. Micro sensor was moved 40 m at each step ................................ .. 82 4 10 Oxygen concentration inside a seed in the USDA greenhouse measured for 24 hours (a smoothing algorithm (moving average, n=7) was used to process the data). ................................ ................................ ................................ ............ 82 5 1 Conceptual schematic of nCe RGO nPt hybrid nanocomposites for measuring ROS and RNS. ................................ ................................ ................. 98 5 2 Material characterization of graphene metal hybrid nanocomposites.. ............... 99 5 3 Electrochemical performance of metal graphene hybrid nanocomposites. ...... 100 5 4 Non linear regression on the one step change to determine steady state response using exponential rise to maximum/ single, 3 parameter in SigmaPlot 12.0 ................................ ................................ ................................ 101 5 5 Current versus time plot for OPD electroplating on 2m diameter platinum microelectrode modified with platinum black and MWCNT s ............................. 101 5 6 Electrochemical performance of Free radical sensor towards H2O2, NO and O2 ................................ ................................ ................................ .................. 102
11 5 7 Selectivity of hybrid nanocomposite sensor towards AA and K 4 Fe(CN) 6 .......... 102 A 1 Photograph of microoptrode inserted into a Brassica napus silique. ................ 105 A 2 Experimental design for hyperoxia in Brassica napus ................................ ..... 105 A 3 Conceptual schematic of nCe RGO nPt nanocomposites for biosensing applications.. ................................ ................................ ................................ ..... 106 A 4 Effect of temperature (panels a b) and salinity (panels c d ) on optrode B 1 Results from four different plant types used for experiments. ........................... 109 B 2 Columbia background A. thaliana siliques 37 days after sowing. ..................... 110 B 3 Representative real time plot of NO efflux measurement from a EMB ovule .... 110 B 4 Average NO flux for wild type and EMB mutant A. thaliana ovules under salt stressed and unstressed condition (n=1 for each test) ................................ ..... 111 C 1 Schematic of photomultiplier tube used to produce measured current ............. 112 C 2 Screenshot of DAQ in MUX software. ................................ .............................. 113 C 3 Screenshot of motion control system in MUX software. ................................ ... 114 C 4 Screenshot of sensor output in MUX software. ................................ ................. 115 C 5 Screenshot of multiplexing output in MUX software. ................................ ........ 116 C 6 Screenshot of calibration screen in MUX software. ................................ .......... 117 C 7 Screenshot of sensor diagnostics and troubleshooting in MUX software. ........ 117
12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DEVELOPMENT OF SENSORS FOR MONITORING OXYGEN AND FREE RADICALS IN PLANT PHYSIOLOGY By Prachee Chaturvedi May 201 4 Chair: Eric S McLamore Major: Agricultural and Biological Engineering Oxygen plays a critical role in the physiology of photosynthetic organisms, including bioenergetics, metabolism, develop ment, and stress response. Oxygen levels affect photosynthesis, respiration, and alternative oxidase pathways. Likewise, the metabolic rate of spatially distinct plant cells (and therefore oxygen flux) is known to be affected by biotic stress (e.g., herbiv ory) and environmental stress (e.g., salt/nutrient stress). During aerobic metabolism, cells produce reactive oxygen species (ROS) as a by product Plants also produce ROS during adaptation to stress (e.g., abscisic acid (ABA) mediated stress responses). I f stress conditions are prolonged, ROS levels surpass the capacity of detoxifying mechanisms within the cell, resulting in oxidative damage. While stress response pathways such as ABA mediated mechanisms have been well characterized (e.g., water stress, in hibited shoot growth, synthesis of storage proteins in seeds), the connection between ROS production, oxygen metabolism and stress response remains unknown. In part, this is because details of oxygen transport at the interface of cell(s) and the surroundin g microenvironment remains nebulous. The overall goal of this research was to develop oxygen and Free radical sensors for studying stress signaling in plants. Recent developments in nanomaterials and data
13 acquisition systems were integrated to develop real time, non invasive oxygen and Free radical sensors. The availability of these sensors for plant physiologists is an exciting opportunity to probe the functional realm of cells and tissues in ways that were not previously possible.
14 CHAPTER 1 INTRODUCTION AND OVERVIEW 1.1 Background and Motivation 1.1.1 Importance of Oxygen and Reactive Oxygen Species in Plants Plants have evolved adaptive mechanisms that allow them to grow in a wide range of environmental conditions. The flux of key molecules across the cell/tissue membrane changes during stress conditions. Oxygen and ROS are important signaling molecules used i n many stress response pathways, and the production adenosine triphosphate (ATP). ATP is a molecule that transports and stores chemical energy with in cells. In plants, ATP is produced by both respiration and photosynthesis. Photophosph orylation (as shown in F igure 1 1 ) is the production of ATP using energy from sunlight. Water molecules break down to free protons and oxygen. Electrons released from this reaction (2e ) are transferred to water plastoquinone oxidoreductase ( also known as photosystem II). Li ght energy (photons) absorbed by chlorophyll pigments cause a series of reactions that eventually transfer the energy to a protein called plastocyanin ferredoxin oxidoreductase (also known as photosystem I). These electrons are eventually transferred to AT P synthase, where the electrochemical proton gradient phosphorylates adenosine diphosphate (ADP) to ATP. In this pathway, electrons flow in two different manners: cyclic electron flow, and non cyclic electron flow. During cyclic electron flow, neither oxyg en nor NADPH is produced, during noncyclic electron flow, splitting of water molecules produces oxygen, ATP and NADPH. Oxidative phosphorylation takes place in mitochondria, and is a metabolic pathway used to produce ATP from sugars (Fig ure 1 2), using ox ygen acts as the electron
15 acceptor. During oxidation/reduction (redox) reactions, electrons are transferred between membrane donor to acceptor molecules (see Figure 1 2 below; showing the electron transport chain, or ETC). As electrons are transported thro ugh the ETC, transmembrane proteins facilitate buildup of protons on the inner membrane produces an electrochemical potential. These ETC proteins are generally referred to as Complex I, II, III, and IV. The membrane potential developed by complex I through IV is used by ATP synthase to phosphorylate adenosine diphosphate (ADP) to ATP. ATP synthase also pumps protons back across the membrane. If the respiration rates exceed cells demand for energy (high ATP or low ADP), the reduction level in the mitochondri on will be high and activate the alternate oxidase (AOX). AOX is an electron overflow pathway which does not pump protons, which results in transfer of electrons without generating a transmembrane potential. The drop in free energy is dissipated as heat (Juszczuk and Rychter 2003) As shown in Figure 1 2, oxygen is necessary in carrying out photosynthesis and respiration. Oxygen levels in the environment are directly correlated with global temperature as well as carbon dioxide levels. Based on a range of scenarios, the Intergovernmental Panel on Climate Change (IPCC) has predicted (Prentice et al 2001) that global carbon dioxide levels will increase to between 485 and 1000 ppm (Williams et al. 2007) by the year 2100. This increase will be accompanied by a rise in global mean temperature in the range of 1.4 to 5.8 o C (3.6 o C mean) (Washington and Meehl 1984) as simulated by atmospheric general circulation models (Cubasch et al 2001). Such increases in global mean temperature are expected to have adverse impacts on reproductive processes and grain yield of the most important seed producing crops
16 (Allen and Boote, 2000) in many regions of the world. Rising temperature reduces oxygen solubility in seeds. The limited oxygen availability inside ovules may adversely affect seed set and growth. Measuring oxygen in seeds at different developmental stages under field conditions will provide insight into the effect(s) of simulated climate change on metabolism. To date the connection between oxygen and synthesis of ROS in plants during environmental stress is unknown. ROS can be defined as chemically reactive molecules containing oxygen. Examples of ROS include superoxide anion, oxygen radicals, hydrogen & organic peroxides and nitric oxide (NO). ROS are natural occurring by products that form as a result of me tabolic reactions in response to various stimuli. The major site for intracellular ROS synthesis is mitochondria, where ROS are produced during endogenous and continuous physiological processes under aerobic conditions (Fleury et al. 2002) The endoplasmic reticulum and nuclear membrane are other sites where ROS is in cells. ROS are generated by two mechanisms: enzymatic pathways and non enzymatic pathways. Among enzymatic pathways, xanthine oxidase plays an imp ortant role (Blokhina et al. 2003) Other enzymes that play a role in ROS production are nitric oxide synthase, oxalate oxidase, amine oxidases, and lipoxygenase. The primary pathway for non en zymatic ROS production involves breakdown of NADH. Accumulation of ROS within cells causes oxidative stress, which can be lethal. Accumulation of ROS also inhibits mitochondrial electron transport, leading to ATP depletion, which induces Ca 2+ influx. To b alance ROS production, plant cells regulate photosynthetic and respiratory oxygen (Bhattacharjee 2005) transduce the ROS signal into appropriate cellular responses by using redox sensitive
17 proteins (RSP). RSP can be found in reversible redox forms, which change form depending on the redox state of the cell. For example, RSP are involved in cytochrome b6f complex redox signaling in the photosynthesis electron transport chain (Bhattacharjee 2005) Figure 1 3 show s the major environmental factors known to induce ROS production, and the effect(s) of ROS production on plants. Hydrogen peroxide (H 2 O 2 ) is a ROS that selectively induces up regulation of defense genes during stress (Bhattacharjee 2005) H 2 O 2 i nitiates signal transduction pathways during abiotic and biotic stress, and is a key factor in mediating programmed cell death (Knight et al. 1996) H 2 O 2 is also involved in pathogenesis (microbiocidal), mediating excessive linking of cell wall polymers and inducing signal cascades which lead to synthesis of RSP. Sustained H 2 O 2 levels in cells induce transcriptional stress response (hormones) such as C 2 H 4 hypersensitive response genes, jasmonic acid signaling genes and salicylic acid. Plant hormones and ROS work in conjunction during stress response. For example, abscisic acid (ABA) is associated with abiotic stress responses and linked to ROS pathways (Figure 1 4). Nitric oxide is a gaseous free radical that exists in three interchangeable forms, NO radical, NO + nitrosonium cation, and nitroxyl radical NO In vertebrates, NO is a key signaling molecule involved in a number of processes such as homeostasis, host defense, and development (Jeseta et al. 2012; Nathan 1992) As shown in Figure 1 5, differen t enzymes catalyze the production of NO. Production of NO from L arginine is catalyzed by NO synthase. NO reductase produces NO from NO 2 and xanthine oxidase produces NO from NO 2 Oxygen is also involved in the form of ROS in the xanthine oxidase pathway
18 In plants, NO signaling has been linked to germination, growth rate of pollen tubes, stomatal movement, programmed cell death (Planchet and Kaiser 2006) NO has also been suggested as a signaling and disease resistant molecule (Zeidler et al. 2004) particularly under stress conditions (Neill et al. 2002) For example, many studies have investigated the role of NO in seed development using the model organism Arabidopsis thaliana Reduction in plant fer tility often results from ovule abortion and embryo senescence (Guo and Crawford 2005) In A. thaliana environmental conditions were identified t hat induced 94% of the developing ovules to either undergo stress induced ovule abortion or embryo senescence (Mugnai et al. 2012; Wang et al. 2010) Studies have shown that NO may act as a defense signalin g molecule (Delledonne et al. 1998) and NO has been shown to increase germination rate in A. thaliana seeds. There is some evidence (Neill et al. 2003) for biological production of NO in plants via nitric oxide synthase, nitrate reductase and other non enzymatic sources. However, there is controversy regarding the endogenous source of NO in plants, as the genes responsible for producing NO have yet to be identified. 1.1.2 Tools for M o nitoring O xygen and Reactive Oxygen Species in P lants Real time measurement of analyte concentration and flux provides critical information for understanding physiology. This research focused on developing sensors and state of art data collection systems for measuring critical markers of plant health (oxygen or ROS). These sensors were demonstrated by monitoring oxygen or ROS in developing plants ( Glycine max L. merr and Brassica napus). Additionally, depth profiles of oxygen were measured in soil near plant roots ( B. napus ). There are many techniques used for measuring oxygen and ROS in plant physiology studies (for a detailed review, see Chapte r 2). Techniques that can provide
19 real time measurement of key metabolites, hormones and signaling molecules in plant cells and tissues provides critical information for understanding plant physiology. There is a need to develop new sensors for measuring k ey metabolites such as oxygen and ROS near developing plant tissues The sensors and systems used in this study are not limited to the demonstrated applications, but rather provide a platform for future sensor development and application in life sciences. With slight modification, the sensors developed here can be used to measure other biologically significant molecules. 1.2 Goals and Objectives The overall goal of this research was to develop sensors for measuring oxygen and reactive oxygen in plants. The objectives for accomplishing this goal were: 1. Review current and emerging technologies for measuring oxygen and ROS to identify gaps in current technology. 2. Develop of a multiplexing fiber optic microsensor system. 3. Determine the effects of high temperature and high oxygen partial pressure on seed oxygen levels in Glycine max L. Merr (soybean). 4. Develop electrochemical reactive oxygen sensors using a nanoceria platinum graphene nanocomposite electrode and demonstrate using a model biological system. The remai nder of this dissertation is divided into four Chapter s based on the objectives above. Chapters two through four each contain a section on describing the background, methodology, results and conclusion.
20 Figure 1 1 Photophosphorylation produces ATP usin g light activated proteins that produce an electrochemical H + gradient and shuttle electrons to ATPase. Figure 1 2. Oxidative phosphorylation produces ATP via proteins that produce an electrochemical H + gradient and shuttle electrons to ATPase.
21 Figure 1 3 General activators of ROS synthesis in p lant cells (adapted from (Kotchoni and Gachomo 2006) A and B indicate the events triggered by ROS in plant cells exposed to abiotic stresses and exposed to pathogens and pathogen elicitors respectively.
22 Figure 1 4 Simplified ROS signaling pathway involving H2O2 and ABA in plants (Bhattacharjee 2005) ABA increases the production of cytosolic Ca2+ and subsequently causes stomatal closure. ABA production and cytosolic Ca 2+ production is mediated by H 2 O 2 Figure 1 5 Comm on nitric oxide pathways (adapted from (Neill et al. 2003)
23 CHAPTER 2 REVIEW OF CURRENT AND EMERGING TECHNOLOGIES FOR MONITORING OXYGEN AND REACTIVE OXYGEN SPECIES TRANSPORT IN PLANTS 2 .1 Fundamental Principles of Sensors In biological systems, real time measurement of analyte concentration and flux provides critical information for understanding the dynamics and physiology of the metabolite. Since commercial sensor systems are not capab le of providing high spatial resolution data, data collection systems for measuring metabolite transport near roots, pods, ovules, flowers and nodules is required. To understand how to develop the tools needed to study living tissues, we must first underst and the fundamental working principle of sensors. The working mechanism for all sensors involves three steps: i) recognition, ii) transduction, and iii) acquisition (or RTA, for short). A simple analogy for describing the 2 1 ). In this analogy, the recognition element (or key) has a specific selectivity for the analyte (or lock). Interaction of the recognition element with the analyte forms an intermediate complex (i.e., key in lock). Recognit ion elements with similar properties may interact with the analyte, but this interaction does not result in the formation of an intermediate complex (i.e., only one key fits in the lock). Formation of the intermediate complex is a reversible process that does not necessarily alter the analyte or the recognition element (or produce a measurable signal). Under favorable conditions, work will be performed (i.e., transduction will occur) and the analyte will be altered (i.e., the lock will be opened). Ideally, transduction should leave the recognition element intact for future interactions with other target analyte(s).
24 In 1912, Michaelis and Menten (M M) used a similar lock and key mechanism to build a mathematical model describing the catalytic activity of en zymes; reviewed in detail by (Dowd and Riggs 1965) Under physiological conditions, enzymatic cata lysis is a reversible process with little to no hysteresis, a defined response time, diffusion limitation at low substrate concentrations, saturation at high substrate concentrations, and a quantifiable sensitivity for the bonding of the s ubstrate in the b inding pocket [ Equation ( 1 1 ) ] M M kinetics may also be used to understand the RTA mechanism of sensors. The binding of an immobilized recognition molecule and an analyte causes the reversible formation of an intermediate complex (where the forward and backward reaction rates described by kinetic constants K 1 and K 1 respectively; Equation ( 1 1 ) ). After transduction, the tertiary structure of the recognition element remains intact and is available to bind with other analyte molecules (the reaction rate of this is described by the kinetic constant K 2 ). Combining the M M kinetic model and the lock and key physical model (Figure 2 1 ), Equation ( 1 1 ) can be used to describe working RTA scheme for sensors. Equation ( 1 1 ) Where [R]= recognition element, [A]=target analyte, [RA]=recognition analyte complex, [B]=byproduct, K 1 =forward binding kinetic constant, K 1 =reverse binding kinetic constant, and K 2 = complex/byproduct kinetic constant. This highly selective, reversible scheme produces a change in the analyte with a defined sensitivity and response time. Due to the reversible nature of this scheme, acquisition of the quantitative change must be larger than the minimum value which can
25 be measured using the acquisition scheme (known as the lower limit of detection, or LLOD). Similarly, the recognition element can be saturated if there is too large of a concentration of analyte present to cause a quantitative change in the output (known as the upper ULOD). The M M model ensures that sensor designs will have the following performance characteristics: 1) Quantitative sensitivity 2) Defined selectivity 3) Defined response time 4) Defined sensing (operating) range (with upper and lower dete ction limit) 5) Little or no hysteresis (reversible) Due to the relatively slow rate of analyte diffusion compared to the rapid reaction rate during recognition (i.e., K 2 >>K 1 and K 2 >>K 1 ), the relationship between analyte concentration and signal has a si gmoid shape Figure 2 2 ). Using Equation ( 1 1 ) as a model, the lower shoulder of this curve is due to the limiting rate of analyte diffusion to the sensor, while the upper shoulder is due to saturation of active recognition sites. Ideally, the limiting step for sensors is interaction between the analyte and recognition element, where the probability of a null effect (described by the constant K 1 ) is very low (i.e., K 2 >>K 1 =K 1 ). If accurate data can be collected describing each shoulder of the sigmoid calibr ation curve (at least three data points in the zero order portion of the curve and three points in the first order portion of the curve), the most accurate method for determining the lower and upper LOD is the tangential method (Diamond 1998) However, if insufficient data exists, the lower LOD can be determined using a statistical (Coleman and Vanatta
26 2009) A very importa nt feature of sensors is that the number of recognition elements in the device is constant. Degradation of recognition element or addition of recognition element must be accounted for to ensure accurate sensor output. All sensors are capable of quantitati vely determining analyte concentration within a specific range. This quantitative sensitivity (Figure 2 3 A) is the gold standard for meet the needs of hypothesis driven rese arch questions (Chaki and Vijayamohanan 2002) Sensor sensitivity within the operating range is typically described by a linear curve, and is calcul ated by determining the slope of the response analyte curve. In the presence of interferents, sensitivity often decreases, which is typically reported as a percent change in the linear calibration slope relative to the slope in the absence of the interfere nt (Figure 2 3 B). Response time for sensors is commonly determined using the t 95 method developed by IUPAC. The t 95 is the time required by the signal to achieve 95% of the steady state response. A final defining characteristic of all sensors is the lac k of significant hysteresis. This feature is one of the most important features of a sensor relative to other devices (e.g., detectors, assays). A lack of hysteresis facilitates experimentalists to collect highly accurate quantitative data concerning tempo ral dynamics of systems. These principles are used to develop all sensors in this research. Chapter 2 is further divided in two aims: 1) Development of multiplexing fiber optic microsensor system for measuring oxygen, and 2) Development of electrochemical ROS microsensors. The sensors developed in further sections have specific RTA scheme.
27 For each sensor the five 5 point characterization scheme described in section 2.1 was performed. 2.2 Review of Current and Emerging Technologies for Non Invasive Quantification of Physiological Oxygen Transport in Plants Chaturvedi et al (2013) provide a comprehensive review of the state of the art in oxygen sensing. These techniques include but are not limited to polarography, electron paramagnetic resonance oximetry, photoacoustic spectroscopy, and anthraquinone amperometry, lab on chip devices, self referencing microsensors, nanosensors, fluorescent microassays, and planar foils. These technologies have been used in biomedical, environmental and agricultural studies of oxygen transport where there is no need for high spatial resolution (Cloutier et al. 2009, Criddle et al. 1990, Gupta et al. 2009, Lamboursain et al. 2002, Verslues et al. 1998). In Chapter 2 developments relevant to fabrication of fiber optic oxygen sensors are briefly reviewed. This includes probes, microprobes, microrespirometry, and self referencing flux sensors. 2.2.1 Probes Most recent applications of oxygen sensors in plant physiology utilized a polarographic (Clark type) metal electrode. Clark electrode s are easy to construct, and usually contain a polarizable metal such as platinum connected to Ag/AgCl reference electrode immersed in electrolyte behind a Teflon membrane. A major drawback for Clark type electrodes is the consumption of oxygen at the sens or tip, sometimes causing anomal ous readings. In the early 1990 s, optical oxygen sensors were developed to eliminate inherent problems with polarographic electrodes including stir sensitivity and drift (Krihak and Shahriari 1996; Kuhl and Jorgensen 1992; L ee and Okura 1997) These optical probes (or optrodes) contain immobilized oxygen quenched
28 luminophore which is interrogated using an excitation laser connected to a photo detector Once excited luminescent dyes transfer energy to oxygen this process is known as fluorescence quenching (Kuhl and Jorgensen 1992, Lee and Okura 1997) Quenching is described by the Stern Volmer Equation (Carraway et al. 1991) and is measured using either intensity based techniques or lifetime based techniques. Intensity based techniques monitor emission amplitude at a fixed light wavelength, where the quantum yield or luminescence efficiency depends on the probability of a su fficient number of outer orbital electrons associated with the dye molecule populating approach is somewhat unreliable. Current optrodes resolve this problem by instead moni toring the lifetime of the excited luminophore, which is the time valence electrons remain in the excited state before emission. Lifetime based measurement improves sensitivity and reduces photo bleaching of the immobilized dye molecules, increasing the lo ngevity of the sensor. The lifetime technique uses a frequency modulated excitation signal commonly a sine wave. Phase shifts in emission signal are measured at a fixed wavelength. Figure 2 6 illustrates the behaviour of the luminescent oxygen quenched d ye platinum tetrakis pentafluorophenyl porphyrin (PtTPFPP). The maximum phase shift (approximately 60) during calibration in phosphate buffered solution (PBS) occurs at an oxygen concentration of zero. According to the Stern Volmer relationship, the measu red phase shift asymptotically approaches a value of ~20 as oxygen concentration increases. The relationship between oxygen and phase shift is nonlinear, but can be approximated by a linear plot from 0 to 21% oxygen, and also from 21 to 32% oxygen (Chatni and
29 Porterfield 2009; McLamore et al. 2010b) Thus, calibration is carried out in deionized water (DI), nitrogen purged DI, and oxygen saturated DI. 2.2.2 Microprobes Electrochemical (Clark type) microsensors provide high spatial resolution and real time temporal resolution (Dodds et al. 1999 Ober and Sharp 1996) These microelectrodes are commonly purchased or prepared using insulated Pt/Ir microelectrodes (Borisjuk et al. 2007, Land et al. 1999, Rolletschek et al. 2009) Optical probes (i.e., micro opt rodes) have also been constructed using the principles described in the previous section. A glass fiber optic cable is tapered using a CO 2 laser based heat source to diameters between 5 10 m (McLamore et al. 2010b) Oxygen micro optrodes have many advantages over electrochemical microelectrodes, including: improved sensitivity/selectivity, no oxygen consumption at sensor tip, faster response en in liquid and/or air. This technology has recently been further enhanced by developing optrode systems that do not require a reference electrode (Chatni et al. 2009a) and also by enhancing sensitivity with the addition of catalytic nanomaterials to the sensing membrane (Chatni et al. 2009b) Although these improvements have increased performance of microsensors, low signal to noise often remains a challenge (see section on Self referencing microsensors for technological solutions to this problem). 2.2. 3 Microrespirometry Oxygen microassays permit rapid multiplexing analysis of cell suspensions ( Alderman et al. 2004 Kratasyuk et al. 2001, Kpper et al. 2004, O'Rior dan et al. 2000, Serrano et al. 2007) Kocincova et al. (Kocincov et al. 2008) recently d eveloped a fluorescent microassay for continuous measurement of H + and oxygen. Excitation (X) is
30 modulated sinusoidally with a 470 nm LED and emission (M) is continuously monitored at 540 nm. Microrespirometry has recently been standardized for simultaneous oxygen and pH measurement by companies such as Seahorse Bioscience among others. Most c ommercial microrespirometry assays use a plunger system to seal the cell/tissue suspension (sample volumes are approximately 7 L) while monitoring oxygen and H + over relatively short periods (5 10 minutes) (Brand and Nicholls 2011) These microrespirometers are capable of auto injection of pharmacological agents (e.g., potassium cyanide, dichlorophenyl dimethylurea, salicylic hydroxyamic acid) during exp erimentation and provide temperature control (Queval and Noctor 2007) Pharmacological agents have been used to inhibit/activate aerobic oxygen transport, but could also be used to inhibit alternative oxidase activity or photophosphorylation. Tschiersch et al (Tschiersch et al. 2011) describe another application of optical sensing in microrespirometry. A planar optrode was constructed for non invasive monitoring of surface oxygen concentration near developing plant tissues. This technique uses a luminescen t planar optrode and an image processing system to report two dimensional oxygen distribution at the tissue surface in real time. This non invasive technique is valuable for developing oxygen maps in hydroponic or aeroponic solutions and possibly leaves, b ut cannot be used in soils. 2.2.4 S elf Referencing Flux S ensors McLamore et al (McLamore et al. 2010b) developed a non invasive technique for measuring physiological oxygen transport at the root rhizosphere interface using a micro optrode. This self referencing (SR) microsensor technique converts traditional micro/nanosensors with low signal to noise ratio into dynamic flux sensors by filtering out signals not associated with biologically active transport. Measurement and
31 amplification of differential concentration signals significantly improves sensitivity, selectivity, and signal to noise ratio (Newman et al. 2012 Porterfield 2007, Shabala et al. 2012) 2.3 Current and Emerging Technologies for Non Invasive Quantification of ROS Transport NO is highly reactive with a half life of 0.5 to 5 seconds, depending on whether hemoglobin is present (Hakim et al. 1996) at nanomolar (and even picomolar) concentrations in living systems. To be useful for measuring NO in living systems, technologies must have five characteristics: i) quantitative sensitivity, ii) high degree of selectivity, iii) rapid response time (< 1 sec), iv) low limit of detection (< 1 M), v) little or no signal hysteresis. A number of reviews on NO sensing provide detailed summaries of current (i.e., c onventional) technologies (Taha 2003, Davies and Zhang 2008, Privett et al. 2010). Examples include spectroscopic methods (e.g., chemiluminescense and UV visible spectroscopy), indirect methods such as colorimetric assays, fluorescent probes, spin trap electro paramagnetic resonance spectroscopy, and electrochemical (amperometric) electrodes. 2. 3.1 Indirect NO A ssays A variety of colorimetric assays have been used to measure NO. The Griess reaction (Sun et al 2003) is used in analysis of biological samples like plasma, urine, and CSF saliva and cell culture media. In this assay nitrite is firs t treated with diazotizing reagent. This intermediate then reacts with a coupling agent, N naphthyl ethylenediamine (NED), to form a stable azo compound. The product emits an intense non spectral color ( purple) making this assay highly sensitive down to N O levels as low
32 as 0.5 M. Kelm et al (1990) describe a photometric method based on the catalytic conversion of oxyhemoglobin to methemoglobin in the presence of NO. Wallace and Woodman (1996) developed a bioassay based on inhibition of platelet aggregati on, but this indirect technique was extremely cumbersome and never gained much traction. 2.3.2 Fluorescent Probes for I maging NO Various fluorescent probes have been developed for measuring int racellular NO (Gunasekar et al. 1995, Nakatsubo et al. 1998, H u et al. 2008). Fluorescent reporters synthesized with dichlorofluorescin (DCF) are based on an oxidative mechanism, which does not impart selectivity over other intracellular ROS. Diaminofluoresceins (DMF) have improved specificity over DCF, but the fluor escence is pH sensitive and thus only produces qualitative results if the local pH is known. Hu et al (2008) demonstrated use of the highly selective and non pH sensitive fluorescent probe vicinal diamino benzoacridine (VDABA) for imaging NO in macrophage s. As discussed in the review by McQuade and Lippard (2010), recent increased interest into the role of reactive nitrogen species as signaling agents in biology has spawned a concerted effort towards creating NO sensitive fluorophores that are biocompatibi le, rapid, and highly selective. 2.3.3 Electron Paramagnetic R esonance Electron paramagnetic resonance (EPR) is based on the absorption of electromagnetic radiation by unpaired electrons placed in a magnetic field (Wennmalm et al. 1990, Ahmad and Kuppusamy 2010). EPR spectroscopy has been used to detect NO and other paramagnetic species in a variety of biological systems (Hogg 2010, Kleschyov et al. 2007, Hong et al. 2009, Fujii and Yoshimura 2000). As described in detail by H ogg (2010), physiological NO levels are below the detectable limit of EPR. To
33 be added. These probes must form an EPR detectable product or otherwise exploit the unique che mistry of NO in combination with the spin probe. The detection limit of spin trap EPR is approximately 1nM, which is well within the physiological range of NO in biological systems (Archer, 1993). While spin trap EPR is indeed useful for studying NO, the t echnique is cumbersome and requires bulky instrumentation that is not transferrable to field conditions or in vivo monitoring outside of the lab. A few attempts to make handheld EPR instruments were initiated (Yamanaka et al. 1991, Armstrong et al. 2008), but to date these technologies have not matured beyond the proof of concept phase for use to detect NO in biological samples. 2.3.4 Amperometric E lectrodes As discussed in detail by Davies and Zhang (2008), all of the techniques above suffer from poor sen sitivity, slow response time, and in some cases (such as EPR) the need for expensive/laborious equipment. Additionally, only a few incremental improvements have been made on spectroscopic methods and colorimetric assays since the 1990 s. The primary barrier for these technologies is an inherently slow response time and poor detection limit. Sensors for measuring NO in vivo must be extremely fast and highly sensitive with superb selectivity. For these reasons, electrochemical (amperometric) electrodes have be en the predominant type of sensor developed for NO detection. The first amperometric NO electrode was developed by Shibuki (1990). Since this groundbreaking discovery, a host of amperometric NO sensors have been developed using carbon fibers, glassy carbon gold, and platinum (electrodes coated with catalytic nanomaterials are covered in a separate section) (B edioui et al 1994, Casero et al. 2001, Lee et al. 2004, Levine and Iacovitti 2003, Malinski and Taha 1992). Most NO
34 sensors are been developed by func tionalizing electrodes with Nafion and/or o phenylenediamine (OPD). Nafion is a polysulfonated Teflon that carries an intrinsic negative charge used to repel electrochemically active anions (e.g., nitrate, nitrite, and ascorbate). OPD is imparts selectivit y to NO by size exclusion of neutral charged interferents such as electrochemically active catecholamines (Porterfield et al. 2001). As discussed in the review by Privett et al. can be divided into three categor ies: i) electrolyte filled micropipettes covered with a thin gas permeable rubber membrane, ii) solid state permiselective NO electrodes, and iii) solid catalytic electrodes. Micropipette sensors are based on the diffusion of low molecular weight molecules such as NO through a gas permeable membrane (Clark 1956, Shibuki 1990). These types of sensors are more selective for NO than nitrite or nitrate, but the construction of these sensors is complex and the micropipettes are fragile. Additionally, biological fouling of the membrane by proteins, bacteria or exopolymeric substances significantly reduces performance. Solid state permiselective NO electrodes are easier to construct than micropipettes, which has led to a more widespread use. In this type of sensor the working electrode is covered with a hydrophobic membrane that is permeable to NO (Ciszewski and Milczarek 2003). While the sensors were useful, the membranes were not selective over nitrite, dopamine or acetaminophen. Solid state catalytic electrodes were developed in an attempt to enhance selectivity by incorporation of mediators such as metalloporphyrins and metal phthalocyanines. Inclusion of catalytic nanomaterials (e.g. platinum nanoclusters, carbon nanotubes) on the surface of amperometric sens ors has been shown to enhance the
35 electroactive surface area. Increased electroactive surface area in turn increases the sensitivity of the sensor. As described by (Claussen et al. 2011) single walled carbon nanotubes based g lutamate biosensors with Pt/Pd nano particles are more sensitive than conventional glutamate biosensors. To date, no catalytic nanomaterials have been used to enhance NO microelectordes. Chapter 5 describes the development and application of nanomaterial m odified electrodes for measuring ROS. 2.3.5 Hydrogen P eroxide Sensing In biological samples, detection of H 2 O 2 is important because of its well known cytotoxic effects, role as a signaling molecule in regulating diverse biological processes such as immun e cell activation, vascular remodeling apoptosis, stomatal closure and root growth. There are various materials which are being used for electro catalytic H 2 O 2 sensing, some of them are as mentioned below (Chen et al. 2012, Chen et al. 2013). 2.3 5 .1 Meta l hexacyanoferrates Ferric hexacyanoferrate, also known as Perssian blue, is capable of catalyzing the reduction of H 2 O 2 at low potentials This provides selectivity over ascorbic acid, uric acid and para acetylaminophenol because of polycrystal structure which allows only small molecules to pass. Copper, nickel, cobalt, chromium, vanadium, ruthenium and manganese hexacyanoferrates are also used in H 2 O 2 sensing. These metals are more electrochemical stability over wide range of pH but lower capability of e lectro catalytic reduction for H 2 O 2. 2.3 5 .2 Heme proteins Metalloprotein porphyrin with iron in the center such as horseradish peroxidase (HRP), catalase (CAT), cytochrome c (Cyt c), hemoglobin (Hb), microperoxidase (MP) and myoglobin (Mb)can easily undergo redox reactions under wide range of potentials.
36 Long ele ctron transfer distance presents a challenge in construction of optimized sensors using heme proteins. But these heme proteins combined with nano materials (CNTs and graphene) presents a promising solution by tunneling mechanism. 2.3 5 .3 Metals and metal oxides Transition metals and their compounds are good catalysts because of their ability to change oxidation numbers and adsorb/activate substances on their surface. Nano sized metals give added advantage because of enhanced mass transport, high effective surface area, and size controlled electrical, chemical and optical properties and effective utilization of expensive materials. In medical studies, r are earth metal oxides are being investigated for their ability to scavenge reactive oxygen species (Hirst et al. 2009). In particular, cerium oxide (CeO 2 ) is emerging as a biocompatible metal oxide that can serve to scavenge ROS in living cells. The scavenging activity of CeO 2 is due to fast transition between Ce(III) and Ce(IV), changing the oxygen vacancies at the surface of the nanocomposites structure and forming n type hybrids (Joung et al. 2011).
37 Figure 2 1 Conceptual physical model describing working scheme of sensor. The result of molecular interaction(s) between the target analyte and recog nition element is temporary (reversible) formation of an intermediate complex. If transduction occurs, the state of the target analyte is significantly changed, producing a byproduct. Figure 2 2 Calibration and Limit of Detection of sensors. A) Typical calibration curve of a sensor, depicting lower LOD, upper LOD, and linear operating range. B) Determination of lower and/or upper LOD using the sigmoidal method (Diamond 1998)
38 Figure 2 3 Sensitivity and Selectivity of sensors. A) Sensitivity of sensors within the operating range is typically linear, and can be estimated using the slope of the response analyte curve. Depending on the governing physics of the process in Figure 8, nonlinear approximations can also be used. B) In t he presence of interferents, sensitivity often decreases due to non specific binding of the interferent with the recognition element. This is reported by a percentage of the linear calibration slope in the absence of the interferent. Figure 2 4 Respons e time for sensors is commonly determined using the t 95 method developed by IUPAC.
39 Figure 2 5 Due to the reversible recognition transduction acquisition scheme, all sensors have little or no hysteresis, which can be quantitatively determined as the per cent change in signal output in the presence and absence of the target analyte. Figure 2 6 Frequency modulated excitation of an oxygen quenched luminescent dye (platinum tetrakis pentafluorophenyl porphyrin). Excitation light is 470 nm and emission wavelength is 640nm.Oxygen concentration is a function of phase shifts in measured excitation/emission signals (also
40 Figure 2 7 Conceptual relationship between analysis time and spatial scale for oxygen sensors. Microrespir ometry and self referencing sensors provide oxygen measurement at the millimeter to micron scale with response times ranging from seconds to minutes. Nanosensors and biochip devices facilitate experimentation at the sub cellular level with response times f rom milliseconds to se conds. Nanosensor image adapted from Buck, S.M., Xu, H., Brasuel, M., Philbert, M.A., Kopelman, R., 2004. Nanoscale probes encapsulated by biologically localized embedding (PEBBLEs) for ion sensing and imaging in live cells (Page 43, Figure 2). Talanta 63(1), 41 59 ). These technologies are reviewed in detail by Chaturvedi et al. (2013).
41 Figure 2 8 Conceptual diagram showing the working mechanism for self m of the surface of a root is oscillated in the direction of biophysical transport (normal to the tangent plane of the tissue/cell surface). Flux motors allow for surface morphology (e.g., root curvature) to be accounted for, providing real time measurement of form function relationships in a wide range of studies.
42 Figure 2 9 Schematic diagrams of various NO sensors. A) E lectrolyte filled. B) Solid permselective. C ) Solid catalytic N O sensors (adapted from Privett, B.J., Shin, J.H., Schoenfisch, M.H., 2010. Electrochemical nitric oxide sensors for physiological measurements (Page 1927, Figure 1) Chemical Society Reviews 39(6), 1925 1935 )
43 CHAPTER 3 DEVELOPMENT OF A MULTIPLEXING FIB ER OPTIC MICROSENSOR SYSTEM The accurate and rapid measurement of physiological oxygen transport is vital for understanding spatially and temporally dynamic metabolisms and stress signalling in cells and tissues. C haturvedi et al. (2013) and Rolletschek et al. (2009) reviewed the available techniques for measuring oxygen in plant physiology research. With a few exceptions, these technologies have been limited to laboratory studies. Most field studies historically have used either polarographic sensors sen sors ( Armstrong and Wright 1976, Armstrong et al. 2009, Cloutier et al. 2009, Dodds et al. 1999, Kpper et al. 2004, Lamboursain et al. 2002, Mancuso et al., 2000, Paterson et al. 2008, Shimamura et al. 2010, van Dongen et al. 2003, Visscher et al. 2005, V erslues et al. 1998, Xu et al 2006) or fiber optic sensors ( Chatni and Porterfield 2009, Kuhl et al. 1992, McLamore et al. 2010 a McLamore and Porte rfield 2011, Porterfield et al. 2006, Porterfield and Smith 2000). Rolletscheck et al. (2009) used electroc hemical (Clark type) microsensors to measure seed oxygen levels in vitro Although these microsensors did provide high spatial resolution and rapid measurement, a major drawback of Clark electrodes is the consumption of oxygen at the sensor tip, which is known to cause anomalous readings. Additionally, there are no reports of a multiplexing Clark microelectrode system that can be used in field studies to date. Optical O 2 microsensors (i.e., micro optrodes) have demonstrated inc reased performance over electrochemical sensors (McLamore et al. 2010b) Advantages of optical O 2 microsensors include: improved sensitivity/selectivity,
44 no O 2 fabrication, and ability to measure O 2 in liquid and/or air. This technology has recently been further enhanc ed by developing optrode systems which do not require a reference electrode (Chatni and Porterfield 2009) which is important for measurement in small, confined sp aces. 3.1 Basic Optoelectronics Fluorescence sensors are based on monitoring light emission after absorption of excitation light. If the emission is from the electronically excited singlet state, the phenomenon is called fluorescence. This phenomenon can be described by Jablonski Energy Diagram. Outer orbital electrons are excited by photons and temporarily absorb external energy. As electrons are excited they go to a higher energy state. As this energy state is not stable, electrons release energy in the form of heat by inter particle collision and vibration (i.e., relaxation). When the electrons return to the ground state, all absorbed energy is released as either heat, or in the case of fluorescence, energy is released is in the form of light. The emitt ed light energy is at a lower energy state (higher wavelength) than the excitation light due to the energe lost during relaxation and interparticle coll ision. For example, in Figure 3 1 blue light is used to excite outer orbital electrons, and red light is emitted as the electrons return to ground state. In the presence of molecules with excited triplet states like oxygen, iodide, chlorine, or acrylamide (known as quenching molecules), the emitted photon is absorbed by the quencher. Quenched fluorescence c an be defined by the Stern Volmer Equation [ Equation ( 3 1 ) ] E quation ( 3 1 ) describes the kinetics of a photo physical intermolecular deactivation process such as quenching of
45 fluorescence or phosphorescence. If the intensity (rate of fluorescence emission) wi thout a quencher molecule present is denoted by I 0 intensity with a quencher by I f quencher rate coefficient by K s fluorescence lifetime without a quencher present by 0 and concentration of the quencher by [Q] then: Equation ( 3 1 ) Fluorescence can be measured by two methods: i) an Intensity based measurements, and ii) a lifetime based measurement. In intensity based measurement, the intensity of transmitted light is compared with intensity of incident light to calculate the concentration of the quencher. Intensity mode measurements are a probabilistic technique that depends entirely on the concentration of excited dye molecules for accurate measurement. Intensity based measurements can be subject to error due to autofluorescence and/or photobleaching. Photobleaching occurs when a fluorophore permanently loses the ability to fluoresce due to photon induced chemical dama ge and covalent modification. The interaction of fluorophore molecule with another molecule, during the transition from excited state to ground state, can produce irreversible covalent modifications. The molecular structure of the fluorophore and local env ironment can affect the average number of excitation and emission cycles. Due to these inherent problems with signal drift, lifetime mode measurements are preferred over intensity mode. The time taken by the photon to travel from ground state to excited s tate and back to ground state is known as the fluorescence lifetime. Lifetime
46 measurements are relatively insensitive to subtle changes in the concentration of dye molecules, or measurement in complex solutions (compared to intensity mode measurments). Thu s, lifetime measurements are more precise and accurate than intensity; assuming hardware are capable of resolving the transient signal and the lifetime of the dye is longer than the minimum acquisition time for current commercial photomultiplier tubes (app roximately 10 ns). With the use of photocatalysts such as metal oxides (Chatni and Porterfi eld 2009; McLamore et al. 2010b) Chatni et al. 2010) the performance of the system can be improved. Photobleaching is reduced in the lifetime based measurements due to only periodic exposure of the dye to maximum excitation intensity (McLamore et al. 2010b) One of the caveats of lifetime measurement is that the technique requires more sophisticated instrumentation than intensity based measurements. In place of using linear incident light, a sinusoidal (or cosine ) wave is used to excite t he dye molecules (Chatni et al. 2009b ). If the incident light is in the form of a sine wave then the transmitted light will also be in the same form. The emission can be easily measured using a standard PMT, and the time lag (or p hase angle) between the two calculated. This phase angle can be correlated to life time, and ultimately concentration of an activator or quencher molecule. Assuming the amplitude of the incident light and transmitted light are A 0 and A( 0 ), respectively, angle is , then Equation ( 3 2 )
47 Fluorescence is shown by many molecules, but due to relatively short life time, and the constraint of standard optoelectronic systems to measure this rapid shift, there is little interference by these molecules for dyes with a relatively long lifetime. The fluorophore molecule used in this application was platinum (II) meso tetra pentafluorophenyl porphine (PtTFPP ) (Figure 3 3 ). The life ti me of this molecule is 100 300 s, which is well within the range for detection using commercially available photomultiplier tubes. 3.2 Fiber Optic S ensors Fiber optic oxygen microsensors were fabricated based on the fundamental optical principles describ ed in the previous section using silicon via internal reflection due to interaction with an opaque layer around the fiber a thin polystyrene film (which has a refractive index nearly one order of magnitude lower than glass). Cladding is covered with an outer polyvinyl chloride (PVC) layer to increase durability. When incident light comes into contact with the cladding, photo ns preferentially travel down the length of the fiber (Fig ure 3 4 ). Light which comes into contact with the fiber end interacts with the local external environment and travels in the fiber optic cable may be coated with an oxygen sensitive dye to prepare an optical electrode (optrode) (McLamore et al. 2010a) Optrodes improve performance over polarographic sensors ( McLamore, et al. 2010, Wolfbeis 2004). When comparing these two tec hnologies, optrodes exhibit improved sensitivity/selectivity, no oxygen consumption at the sensor tip,
48 measure oxygen in liquid and/or air. The most common type of oxygen optrod es are fabricated by immobilizing a luminescent dye on the tip of a fiber optic cable. Frequency modulated excitation is used to measure quenching of luminescent lifetime by oxygen Lifetime mode optrodes are preferred over intensity based optrodes due to less noise and drift, as well as an elimination of calibration shifts associated with photobleaching (Chatni et al. 2009a, Chatni and Porterfield 2009, McLamore et al. 2010). Optrode performance can be enhanced by including photocatalytic nanomaterials (e. g., metal oxides) in the sensing membrane to improve sensitivity (Chatni, et al. 2009b). To further improve optrode technology, reference free systems have recently been developed by Chatni and Porterfield (2009). To date, current, commercial systems developed for optrodic sensing of oxygen are single channel systems. Although these technologies are invaluable for basic field studies, many experiments require simultaneously monitoring in multiple locations (instead of at a single point) and improved spatial resolution. Recently, planar sensor foils have emerged as a technology to f ill this gap (Tschiersch et al. 2011, 2012). A luminescent planar foil adheres to the sample via a thin film of deionized water between th e sample and the foil. A camera acquires fluorescent data that is easily converted into two dimensional oxygen concentrations along the surface of the foil. Planar sensor foils have high resolution for 2D mapping, but the technique is limited to measuring oxygen levels at the surface, and thus cannot be used to profile three dimensional
49 samples. There is a need for simple, field capable technologies for three dimensional oxygen mapping. C hapter 3 describes the development of a multiplexing fiber optic oxyge n microsensor system (MUX) designed to conduct high temporal resolution experiments in field studies. The 10 channel system was tested using a variety of applications. The applications include monitoring of oxygen in: developing soybean seeds, lithifying m icrobial mats during diel cycling (not shown here, but available in Chaturvedi et al. in review ), oil seeds exposed to hyperoxia and soil profiles of roots during diel cycling. These experiments were chosen to demonstrate the ability of the oxygen system t o operate in a wide variety of in situ and in vitro conditions without any additional modifications; from seawater to developing plant tissues. 3.3 Materials and Methods 3.3.1 Sensor F abrication Fiber optic microsensors were prepared using previously published methods ( Chatni et al. 2009a, Chatni and Porterfield 2009, McLamore et al. 2010). Briefly, 2 m long X 140 m thick multi mode fiber optic cables (Thor Lab Inc, Newton, NJ) were cut using a FBC 007 diamond blade fiber cleaver (Corning, Inc. Cornin g, NY). Fibers were examined using an inspection microscope to ensure the cleaving was flat and there were no cracks. Approximately 3.81 cm of the outer PVC jacket and 5 mm of the polymer cladding were removed using micro surgical blades and tweezers (Worl d Precision Instruments, Sarasota, FL). The tip of the optical fiber was coated with a solution containing polystyrene, chloroform, titanium dioxide and an oxygen
50 quenched luminescent dye. The dye used for these experiments was platinum tetrakis pentafluor ophenyl porphine (PtTFPP) (Frontier scientific, Inc. Logan, UT). To prepare approximately 20 fibers, 96 mg of polystyrene beads (Sigma Aldrich, St. Louis, MO) were vortex mixed (Vortex Genie, Bohemia, NY) with 1.15 g chloroform (Fisher Scientific, Waltham, MA) for 30 min in a sealed glass vial. Titanium dioxide (45 mg; Fisher Scientific) and PtTFPP (5 mg) were mixed into the solution and vortex mixed for 30 seconds. The solution was sealed immediately to avoid evaporation of the chloroform. To coat optical fiber, a cleaved/stripped fiber optic cable (described above) was positioned under a dissecting microscope using manual linear actuators. The fiber optic cable was positioned in the focal plane together with a glass capillary dipped in the dye mixture. Th e fiber was inserted into the dye cocktail bubbles formed at the tip of the glass capillary tube. The sensing membrane was inspected for uniformity under the dissecting microscope, and fibers with a dye ure 3 2 shows a conceptual schematic of the frequency modulated principle for micro optrodes. Coated fibers were subsequently inserted into standard 26 gauge needle to facilitate the penetration into tissues and soft materials and protect the sensing element ( Fig ure 3 5 ). For penetration into soil and lithifying microbial mats, coated fibers were inserted into a custom 26 gauge needle with 20 m side ports (Fig ure 3 5) for insertion into soil and other dense media purchased from Hamilton Co. (Reno, NV). Frequen cy modulated emission light (M 648 nm ) travels up the fiber optic to a photomultiplier tube. Inset: structure of PtTFPP dye. b) Schematic of micro
51 optrode inserted into a standard 26 gauge needle to allow for penetration into tissues and soft materials c) Micro optrode inserted into a custom 26 gauge needle with 20 m holes for penetration into soil and lithifying microbial mats. 3.3.2 Working P rinciple The optical system is based on the excitation of PtTFPP with a 400 nm LED, transmission of emission sign al through the fiber optic core, and conversion of this signal at 645 nm to a voltage using a photomultiplier tube (Fig ure 3 6 ). A conceptual schematic of the hardware for multiplex oxygen monitoring is shown in Figure 3 6 The main components of the MUX i ncluded a linear stepper motor, motor encoders, power supply, cooling system, A/D hardware, and an integrated optics system ( Figure 3 6 ). The integrated optical system (InOS) contained an LED for excitation, a dichroic mirror, band pass filters (B390 for b lue and O 56 for red from Hoya Corp., Santa Clara, CA), a 10X objective (0.25 N A), and a photomultiplier tube The InOS was positioned over an individual ST connector by the stepper motor, and emission was read from PtTFPP functionalized fibers connected to the unit. A beam splitter/dichroic mirror, and bandpass filters within the InOS facilitated measurement of emission at 645 nm. The focal length, from ST connector to objective lens, was constant for all channels (1.2 cm). 3.3.3 Sensor C alibration Senso r calibration was performed using known concentrations of dissolved oxygen in deionized water, growth media, or seawater; nitrogen purged water (0 kPa), water exposed to air (21 kPa), and oxygenated water (32 kPa) (McLamore et al. 2010). Calibration of Pt TFPP fiber optic sensors was linear between 0 to 21 kPa oxygen, and also between 21 to 32 kPa oxygen (McLamore et al. 2010).
52 Hence, sensitivity of the sensors was determined by either calibrating the sensors in water exposed to air (21 kPa oxygen ) and nitr ogen purged water (0 kPa oxygen ), or water exposed to air and oxygen purged water (32 kPa). Response time (t 95 ) of the sensor was calculated by averaging the 95% steady state value of three measurements over the linear range (0 21 kPa or 21 32 kPa). To determine sensor hysteresis, sensor response was first measured in one of the calibration solutions until steady state response was obtained (less than 5% deviation). The sensor was immediately inserted into one of the other calibration solutions. Finally, the sensor was returned to the original calibration solution until a steady state response was obtained. The hys teresis was determined by calculating the percent change in steady state signal for the sensor before and after the calibration solution was changed. After calibration, all sensors were connected to the MUX using ST connectors and the lateral positioning of the InOS was adjusted using the stepper motor until the maximum emission signal was obtained for each channel. 3.3.4 Profiling O xygen Gradients in S oils A five day study was conducted for monitoring oxygen in soil using a photo per iod of 17 hrs. The experiments were conducted in a controlled plant growth facility (Conviron Inc., Winnipeg, Manitoba, Canada). Air temperature respectively. During the photoperio d, photosynthetic photon flux was maintained at 450 mol/m 2 s at canopy height (measured using a parabolic aluminized reflector (PAR) light meter). The chambers were kept at 55% relative humidity (RH); monitored using a RH/Temperature Monitor (Fisher Scien tific, Waltham,
53 MA) 5 L pots (dimensions: 20 cm x 16 cm (diameter X depth) were filled with 4 L of potting soil (Scotts Miracle Gro Co., Marysville, OH). Pots were placed in a shallow pan and sub irrigated every other day. Oil plant ( Brassica napus ) seeds were planted at a depth of 5.08 cm. Two soil bores were made with a cylindrical ruler to obtain the appropriate depth (one 8 cm deep and one 12 cm deep). Micro optrodes were inserted into the soil bores and oxygen was continuously monitored for five days All sensors were calibrated before and after each individual experiment and sensor hysteresis was calculated. All measurements were made in triplicate. 3.3.5 Canola Seed Oxygen under Elevated Ambient Oxygen Condition Two sets of Brassica napus plants we re grown in controlled environmental growth chambers and monitored for seed respiration. One set was grown at NASA Kennedy Space Center, FL and the other set was grown in the Agricultural and Biological Engineering Department at the University of FL. Canol a ( Brassica napus L. cv. Westar) seeds were imbibed and germinated in small cells with 2 to 3 seeds per cell. After the second true leaf was visible, seedlings were transplanted to 8.5 inch plastic pots with Fafard 3B mix ( Scotts Miracle Gro Co.) at a final planting density of 1 plant per pot (pot dimensions: 20 cm diameter x 16 cm depth) Plants were grown in a controlled environmental chamber maintained under the following conditions: a photoperiod of 14 hrs (from 3 am to 5 pm), day/night temper 400 mol/mol air carbon dioxide concentration. During the photoperiod, photosynthetic photon flux was maintained at 450 mol m 2 s 1 at plant canopy level (measured using a PAR light meter). Car bon dioxide was provided using an
54 external carbon dioxide tank purchased from Airgas, Inc. Canola ( Brassica napus L. cv. Westar) seeds were provided by Dr. Lanfang Levine who received from a canola oil manufacturing company in Canada. Watering/nutrient sc hedule every week. The watering schedule for young plants was 0.5 L of de ionized Once plants att ained this height, they were irrigated with 1L of DI water per day. Once the plants reached a height of 30.84 cm, the irrigation rate was increased and stored in a sealed black co ntainer. Sensors in metal needles were carefully inserted into Canola seeds using a bright LED to ensure the loc ation (see supplemental Figure A 1). Sensor output amplitude and phase angle were recorded using MUX software. Emission amplitude was used as a n indicator of the quality of the sensor, and phase angle was used for the purpose of calculating oxygen. During all experiments, measurements were taken during the seed developmental stage of 28 to 34 days after pollination (DAP). Seed oxygen was first m easured in control plants, and then in siliques placed in 21 kPa oxygen. The MUX system was used to rapidly assess seed oxygen levels in 28 day old B. napus siliques exposed to hyperoxia and non lethal freezing events. In situ hyper oxygen treatment was ca rried out by slipping disposable syringe barrels (10 ml capacity) over the developing seed pods (total of 18 siliques per treatment) and attaching the Luer end of the syringe barrel to a man ifold (see supplemental Figure A 2). Siliques were flushed with a small flow
55 of oxygen for about 40 hr prior to the oxygen measurement. Each silique was inserted inside an individual barrel, and flow rates were set to maintain either 21 kPa or 45 kPa oxygen. A mixture of pure oxygen and air was used to achieve the desired oxygen partial pressure. The flow rate on individual barrels was 200 to 300 mL/min. Phase angle was recorded within 5 min of inserting a sensor to avoid leakage. Measurements were taken for 3 4 seeds (i.e., ovules) and the average oxygen concentration was calculated using the arithmetic mean of the measured oxygen values in this initial 5 min interval. 3.3.6 Statistics Each experiment was conducted in triplicate. Significance was determined using a paired t test in the open source programming language R with a 95% confidence level. All error bars represent standard deviation of arithmetic mean. 3.4 Results and Discussion 3.4.1 Sensor C alibration The absorption spectrum of the PtTFPP/TiO 2 polystyrene membrane was measur ed with a fiber optic spectrophotometer (Fig ure 3 7 ). Absorption bands were observed at 400 nm, 500 nm, and 548 nm, which is similar to previous studies (Lee and Okura 1997 ; Chatni and Porterfield, 2009). When excited in the Soret band (400 nm) optrodes displayed strong phosphorescence at 6484 nm ( Figure 3 7 ). As predicted by the Stern Volmer Equation the emission intensity was significantly lower in the presence of oxygen The average K SV value based on the data in Figure 3 7 was 0.115.003 kPa 1
56 The average sensitivity of the micro optrodes was 2.690.31 deg kPa 1 oxygen between de aerated (0 kPa oxygen ) and deionized water at 23 C ( Fig ure 3 8 ). These values were similar to values reported by Chatni and Porterfield (2009) and McLamore et al. (2010 b ). For supersaturated solutions, the optrodes were less sensitive than the values reported above due to the rapid quenching of luminescence by oxygen Sensors calibrated between 21 kPa and 32 kPa oxygen had an average sensitivity of 0.910.13 deg kPa 1 oxygen (Fig ure 3 8 ). The measured hysteresis in calibration solutions was +0.50.4% change in baseline signal The lateral position of the InOS was adjusted using the computer controlled stepper motors to pro perly align the objective over the ST connector. This positioning was conducted at 0 kPa and 21 kPa. A representative plot of the lateral InOS position versus measured phase angle is depicted in Figure 3 8 For each calibration, the location of the objecti ve lens was changed by 0.005 cm. until the maximum phase angle was measured. For the example in Figure 3 8 the optimum position was 1.26 cm from the base of the instrument. It is important that the objective is properly aligned so that the maximum numbers of photons are detected for each sensor. During calibration, the alignment procedure was repeated if sensor sensitivity was relatively low. After calibration and proper InOS alignment, the MUX was demonstrated by measuring spatially resolved real time ox ygen concentration in marine biology and agricultural applications. These demonstrations represent challenging environments for minimally invasive oxygen sensing, as optrodes are used in seawater, plant tissue, and agricultural soils.
57 3.4.2 Profiling O xygen Gradients in S oils Three fiber optic sensors were used to continuously measure oxygen in well watered soil during growth of 60 day old Brassica napus ( Figure 3 9 ). Real time oxygen concentration data was collected at three different soil depths duri ng five days of successive monitoring (Figure 3 9 ). Results demonstrated that the MUX was clearly capable of detecting changes in local soil metabolism. The average time required for establishment of steady state oxygen during day night transition (134.5 min) was significantly longer than night day transition (31.5 min) ( Figure 3 9 ). For all data, as depth increased, the oxygen concentration decreased. The average oxygen concentration for all sensors during simulated day and night conditions are shown in Figure 3 9 During daytime, oxygen concentration decreased in a linear fashion with depth. E quation ( 3 3 ) describing this behavior was: Oxygen = = 0.5*(depth) + 22 kPa (R 2 = 0.97) Equation ( 3 3 ) During nighttime conditions, oxygen concentration decreased, but the trend was not as linear (R 2 = 0.86) when compared to daytime conditions. Stress conditions adversely affect plant and microbial growth in soil. Conditions such as water logging and root hypoxia can be detrimental to oxygen transport. If excessive water is present in the soil, the flow of oxygen to plant roots significantly decreases. Prolonged exposure to water logged soil can cause plant death as well. Development of the MUX, which measures real time oxygen profiles in soils, provide es sential information on root physiology. For plants without aerenchyma, soil flooding or waterlogging causes rapid oxygen depletion and CO 2 buildup in soils, which is especially deleterious. In the United
58 States in 2011, natural flooding caused approximatel y $3.1 billion in losses due to irreversible agricultural crop damages (Voesenek and Bailey Serres, 2013). Low oxygen availability during soil flooding (i.e., waterlogging) adversely affects root metabolism, often leading to a drop in cytosolic pH, low ad enylate energy charge, and a decline in protein synthesis. Virolainen et al. (2002) report that low oxygen concentration near roots leads to an increase in mitochondrial matrix Ca 2+ concentration and the release of cytochrome c from mitochondria; which pro motes programmed cell death in most systems. Furthermore, flooding impedes proper transport of nutrients and gases between roots and the rhizosphere, which can also lead to plant death. Plants have a multitude of stress response pathways for adapting to lo w soil oxygen (Loreti et al. 2003). To date, most of our knowledge on flooding tolerance derives from laboratory experiments under controlled conditions. The MUX provides opportunity for field and environmental studies to determine how similar laboratory c onditions mimic those actually present in agronomic fields and natural environments. This work should lead to identifying plants that acclimate to short term flooding or water logging in different soil types and environments. The MUX system developed here allows researchers and farmers to properly monitor plant and microbe growth in the soil using oxygen as an indicator and take corrective measures if needed. 3.4.3 Measuring Oilseed (Brassica napus) Oxygen L evels During normal development of many oil seeds (such as soybean, canola, and flax), embryos develop from a green photosynthetic tissue to a mature, desiccated and dormant form which is devoid of chlorophyll (Goldberg et al. 1994). In canola, various environme ntal conditions can lead to green seed at
59 maturity, resulting in low quality oil that is expensive to p rocess (Daun, 2003; Ward et al. 1995). For instance, an untimely brief non lethal freezing/frost results in green seeds at maturity. No amount of time or crop management is able to mitigate the green seed problem, which presents a great economic loss due to the green tint and bad taste in the final Canola oil product. Figure 3 10 shows a photograph of a micro optrode inserted into a Brassica seed. The av erage oxygen concentration in the three conditions was not statistically different between 45 kPa and 21 kpa point (p = 0.92) or between control and 21 kPa point (p = 0.72). Figure 3 10 show plot s of average oxygen concentration in seeds of their siliques were exposed to elevated external oxygen. Fig ure 3 11 shows the oxygen concentration within seeds that experienced 3 hr non lethal freezing at oxygen was higher than that in control plants (5.60.23 kPa vs 1.780 .52kPa; p= 0.005). The result unambiguously demonstrated not only the suitability of the sensor system for oily seeds, but also that the seed oxygen was affected by its surrounding oxygen tension. There has been a great deal of research in this area to und erstand the mechanism of green seed formation and subsequently devise agricultural mitigation strategies. Several hypotheses have been proposed, but to date, the biochemical processes leading to green seed formation are still elusive. Bonham Smith et al. (2006) showed that embryo abscisic acid (ABA) concentration declines precipitously (compared to the non stressed control) following a non lethal freezing event, with the greatest change occurring in seeds
60 stressed at a time that provok ed the greatest retention of chlorophyll at maturity (32 DAP). During the past few years, collaboration between Dr. Mary Musgrave and Dr. Lanfang Levine found that cold stress leads to an increase in oxygen levels in the silique space between seeds (unpubl ished data). It is also known that the gaseous environment (oxygen tension in this case) around the seed affects ABA accumulation within seeds and has a cascading effect on other chemical pathways. Drs. Musgrave and Levine hypothesized that the altered ga seous microenvironment around the seed during sensitive developmental stages is the immediate stressor changing the normal seed developmental course. In order to prove this hypothesis, it is imperative to know the oxygen concentration within seeds that hav e underwent the stress. To meet this need, the MUX system was successfully used to monitor Brassica napus seed oxygen levels during external hyper oxygen tension and cold stress. While the details of these results are still under investigation, the importa nt point here is that the MUX clearly demonstrates the ability to provide minimally invasive oxygen measurements which can be combined with other molecular techniques to investigate the biological mechanisms associated with green seed formation (and other similar developmental problems in seeds). 3.5 Conclusion A multiplexing fiber optic oxygen microsensor system was designed and used to conduct real time in vivo experiments. The hardware and design of the system are described such that the device can be re created by any lab with basic optical equipment and programming skills. The ten channel system was used to monitor form function relationships in a variety of agricultural and
61 environmental applications, including : marine microbialites, agricultural soils and developing oil seeds. The multiplexing system provides the unique capability of minimally invasive monitoring of oxygen with sub millimeter spatial resolution. This important tool meets a critical technology need for biologists performing field exper iments.
62 Figure 3 1 Jablonski Energy Diagram describing fluorescence Figure 3 2 Phase modulated excitation
63 Figure 3 3 PtTFPP molecule and an artistic rendering of the molecule together with titanium dioxide used as a photocatalyst (McLamore and Porterfield 2011) Figure 3 4 Schematic diagram of fiber optic sensor Figure 3 5 Schematic of a fiber optic cable coated wit h an oxygen sensitive dye such as PtTFPP. Frequency modulated excitation light (X 400 nm ) travels down the fiber optic core and interacts with PtTFPP on the tip of the fiber optic cable.
64 Figure 3 6 Conceptual design of MUX. A ) Conceptual schematic of multiplexing fiber optic microsensor system (MUX). The optical unit is positioned over ST connector s during individual measurement. B ) Exploded view of integrated optical system, depicting the LED for excitation, dichroic mirror, filters (B390 and O 56), a 10X objective, and a photomultiplier tube. Figure 3 7. Representative absorption/emission spectra for micro optrodes. A ) Absorption spectra of PtTFPP dye. Absorption bands were observed at 400 nm, 500 nm, and 548 nm. B ) Lumine scence spectra of the sensing membrane after excitation at 400 nm. The peak emission (648 nm) was measured in a nitrogen environment (0 kPa oxygen ); air (21 kPa oxygen ) and pure oxygen (100 kPa oxygen ). Emission peaks in the presence of oxygen were signifi cantly lower as predicted by the Stern Volmer Equation
65 Figure 3 8. Calibration and lateral positioning. A ) Representative sensor calibration in deionized water at 23C. Sensor response was linear between 1 kPa and 21 kPa oxygen as well as between 21 kPa and 32 kPa oxygen B ) Representative plot showing the lateral positioning of the InOS over an ST connector. Positioning is shown for an optrode in 0 kPa oxygen (nitrogen gas) and 21 kPa oxygen (air) Figure 3 9. Design of exper iment and results of oxygen profiling in soil. A ) Conceptual schematic of diel soil profile experiments monitoring oxygen gradients during Brassica napus growth. Sensors were placed at the surface, and at 8 and 12 cm from the surface level. B ) Five day con tinuous monitoring reveals the real time oxygen concentration measu red at various in soil depths. C ) Average oxygen concentration at various depths during simulated day and night conditions. Oxygen concentration decreases with increasing depth due to respi ratory activity by roots and aerobic bacteria. During night time, respiratory activity increases at all depths.
66 Figure 3 10 Design of experiment and results of oxygen profiling in Brassica napus. A) Photograph of sensor inserted into a Brassica seed during measurement of seed oxygen. B ) Oxygen concentration in seeds at DAP 30 exposed to hyperoxic microenvironment. (c) Oxygen concentration in seeds at DAP 32 exposed to hyperoxic microenvironment Figure 3 11. Oxygen Concentration in the cold stressed and control plants at Dap 30 and DAP 34
67 CHAPTER 4 EFFECTS OF HIGH TEMPERATURE AND HIGH OXYGEN PARTIAL PRESSURE ON SEED OXYGEN LEVELS IN GLYCINE MAX L. MERR (SOYBEAN) Although oxidative and photo pho sphorylation are vital to metabolism, the pathways also produce reactive oxygen species (ROS) such as superoxide and hydrogen peroxide ( H 2 O 2 ). Production of ROS leads to formation of free radicals, which damage cells. Abiotic stress can lead to formation o f excess ROS. As described by the Arrheniu s law [ Equation ( 4 1 ) ] the rate of reaction increases with increasing temperature. Equation ( 4 1 ) Where k is rate of reaction, A is Arrhenius constant, E a is activation energy, R is ideal gas constant and T is temperature. Increased temperature is an abiotic stress that affects the rate of phosphorylation. 4.1. Climate Change as an Abiotic S tress Based on a ran ge of scenarios, the Intergovernmental Panel on Climate Change (IPCC) has predicted (Prentice et al. 2001) that carbon dioxide levels will increase to between 485 and 1000 ppm (Williams et al. 2007) by the year 2100. This increase will be accompanied by a rise in global mean temperature in the range of 1.4 to 5.8 o C (3.6 o C mean) (Washington and Meehl 1984) as simulated by atmospheric general circulation models (Cubasch et al. 2001). Such increases in global mean temperature are expected to have adverse impacts on r eproductive processes and grain yield of the most important seed producing crops (Allen and Boote, 2000) in many regions of the world. Rising temperature reduces oxygen solubility in seeds. The limited oxygen availability inside ovules may adversely affec t seed set and growth. Measuring
68 oxygen in seeds at different developmental stages under field conditions using the MUX hardware developed in Chapter 3 will provide insight into the effect(s) of simulated climate change on metabolism. 4.2 Methodology 4.2. 1 Chamber C onditions A USDA ARS controlled plant growth facility situated at the University of Florida, Gainesville was used for these studies. Plants were grown in 8 greenhouse rooms within the structure, each room having dimensions of 2m X 1m X 1.5m. The structure was built on an aluminum frame with clear polyethylene ter ephthalate plastic film (Figure 4 1 ). Insulated soil lysimeters were set up 0.6 m deep in the soil to measure the evapotranspiration released by the plants. The 4 chambers on the left sid e were kept at ambient oxygen partial pressure (21 kPa), whereas the other 4 chambers on the right side were maintained at a higher (32 kPa) oxygen partial pressure. A mixture of oxygen and nitrogen gas was used to supply oxygen to the rooms at 32 kPa. All the gases were supplied by Air gas, Inc. The concentration of carbon dioxide was maintained at 700 ppm in all the chambers. The chambers were kept at 55% relative humidity (RH) during the day and 70% RH during the night. All the chambers were sunlit, no e xternal lighting was used. Air temperature inside the A group of 13 researchers from agronomy, biology, and agricultural and biological engineering departments at the University of Florida and USDA ARS (Gainesville, FL) were involved in the project.
69 Experimental conditions and temperature settings were adjusted to ac count for seasonal variations. During experiment 1 and 2, 68 plants were grown under 30/22 C (day/night) conditions in 2 rooms; one room with 21 kPa oxygen, and other with 32 kPa oxygen. During experiment 3, 47 plants were grown under 30/22 C conditions in 2 rooms, one with 21 kPa ox ygen partial pressure and other with 32 kPa oxygen partial pressure. The Table 4 1 described the changes that were made in temperature during the experiments. Glycine max L. Merr. was used as a model plant for this study. Plants were of Maverick cultivar, which was supplied by the USDA. Four seeds were sown in 5 Liter pots (dimensions: 20 cm x 20 cm (diameter X depth) filled with 4 Liters of potting soil ( Miracle Gro Potting Mix ). After emergence, plants were divided to one per pot. Pots were placed in a s hallow pan and sub irrigated as needed. Plants were all well watered, through the bottom of the pots. Plants were not water stressed during the experiments. Micronutrient solution was added 30 days after sowing (DAS). Figure 4 2 below shows the setup durin g the experiment (MUX and computer inside the plant growth chamber). MUX was placed inside a temperature controlled box, which kept the temperature inside the box around 20 components inside the MUX. Sensors were kept for inside the chamber for at least 30 min so as to avoid any false reading due to temperature difference between sensors and plant tissue. After setting MUX and computer, the sensors were connected to individual channel and wash ed using DI water to remove any dirt particles on the sensor tip surface. For calibration, phase angle measured by
70 the sensor was plotted against the oxygen partial pressure. Due to linear correlation of dye molecule with oxygen partial pressure, two point calibration was performed for chambers at 21 kPa oxygen partial pressure and three point calibration for 32 kPa oxygen partial pressure chambers. For the two point calibration, chamber air was considered as a point measured at 21 kPa oxygen partial pressu re. Nitrogen gas was used for the 0 kPa oxygen partial pressure data point in both the calibrations. For three point calibration 21 kPa and 32 kPa oxygen partial pressure chambers were use d as the second and third calibration point. All calibration data po ints were measured at the same temperature (also inside the chamber) to avoid any effect caused by change in temperature or any other external condition. Calibrated sensors (in metal needles) were carefully inserted into G. max seeds using a bright LED as transmission light to help locate the desired seed area (Figure 4 2). Figure 4 2 shows a close up photograph of the sensor inside a G. max seed after the insertion. During measurement, sensor output amplitude and phase angle w ere recorded on the computer via custom MUX software. Emission amplitude was used as an indicator of the quality of the sensor, whereas phase angle was used for the purpose of calculating oxygen. As described in Chapter 2, amplitude (intensity) was used as an indirect indicator of signal to noise ratio, where phase angle was used as a direct indicator of oxygen quenching. 4.2.2 Self Referencing Oxygen Flux Studies Self referencing (SR) microsensors were used to non invasively measure oxygen flux at the su rface of developing seeds. This noise filtering technique has been used to measure oxygen flux in a number of biological systems (reviewed
71 by McLamore and Porterfield, 2011). The SR microsensor technique converts static micro/nanosensors with otherwise low signal to noise ratios into dynamic sensor is oscillated between two locations which ar concentration at location x (C x ). Flux (J) is calculated using known values of the molecular diffusion constant (D; for oxygen, this value is 2.1 X 10 5 cm 2 sec 1 ) and E quation ( 4 2 ) : Equation ( 4 2 ) McLamore and Porterfield (2011) highlight the use of self referencing microsensors in plant root studies. The technique improves the signal to noise ratio by two orders of magnitude over conventional microsensor measurements, and can be used to directly measure flux in real time. SR sensors were recently used to measure O 2 flux along the surface of Glycine max Zea mays and Phaseolus vulgaris roots (McLamore et al. 2010b) Numerous reviews highlight SR microsensors used to study pollen tube physiology (Holdaway Clarke and Hepler 2003) ion transporters in roots (Newman 2001) a nd oscillatory transport at the root rhizosphere interface (Shabala et al. 2006) Hardware for self referencing (SR) was described by (Porterfield 2007) and McLamor e and Porterfield (2011). Briefly, the system consists of a vibration isolation table with Faraday cage (Technical Manufacturing Co., Peabody.MA), camera and sensor(s) are mounted on a head stage and controlled by a motion
72 control system (MCS) (Fig ure 4 3 ) Dissected s iliques were placed under the camera/zoomscope. Automated Scanning Electrode Technique (ASET) software was used for data acquisition and control functions (Science Wares, Falmouth, MA). A/D boards with DC video/data acquisition systems were supplied by Applicable Electronics,Inc. (Sandwich, MA) (Fig ure 4 4 ). Calibrated oxygen micro seed surface using the compu ter controlled motor system (F igure B4 ). After measuring baseline oxygen flux, pharmacological agents (atrazine or potassium cyanide) were added to the dish and mixed. For all studies, seed length was measured as an indicator of developmental stage. Seeds oxygen was measured at variou s developmental stages starting R3 stage (Fehr et al. 1971) According to the classification by Fehr et al. (1971), R3 stage seeds are defined as <75% coverage of the seed envelope by the seed. 4.2.3 Statistics All experiments were designed with four replications. Combined analysis test or ANO VA using open source programming language R. with a 95% confidence level. Sensors were calibrated and tested before inserting them in the seeds, and calibration values were averaged for 60 seconds of continuous recording at 1 kHz. Oxygen measurements were recorded for each seed (ovule) on a pod. Average data represents the calculated oxygen concentration in all ovules for at least four
73 replicate pods (three seeds are generally present in one pod). Average measured length of seed was calculated for at least four pods. Phase angle was recorded within 5 min of inserting a sensor into a pod to avoid gas leakage across the testa. If the probe was damaged during the insertion (sudden drop in amplitude or phase angle, refer to Chapter 2), the sensor was discarded. For the post processing of data, a moving average algorithm will be used. The moving average is a post processing data smoothing technique used to analyze the central tendency of a data set over a long period of time. A moving average is calculated to fil ter short term noise events and thus identify relatively long term trends (Hosid et al. 2004) As described in Chapters 2 test or ANOVA. Average NO flux will be calculated using the arithmetic mean of at least 12 minutes of continuous recording at 1 kHz. After addition of pharmacological agents, a peak response will be measured (Porterfield, 2001). Following establishment of a post exposure steady state NO flux average NO flux will be calculated using the arithmetic mean of at least 12 minutes of continuous recording at 1 kHz. 4.3 Results and Discussion Figure 4 5 shows a plot of oxygen concentration in developing G. max ovules as a function of growth temperatu re. As is evident from the plot, oxygen concentration in the ovules decreases with increase in temperature. This was test (paired t
74 between point 1 for 21% and 32%, P value=0.0544; betwe en point 2 P value= 0.326 and between point 3 for 21% and 32%, P value= 0.270. Figure 4 6 shows a plot of oxygen concentration in developing G. max ovules at different developmental states as a function of growth temperature. Oxygen concentration in the ov ules decreased with increases in temperature at the R3 stage, while for R4/R5 seeds the oxygen concentration in the ovules did not significantly change with increases in temperature. These data confirm that developmental stage is a key factor in metabolism under all conditions tested, and that there is a significant shift in net respiration once the seed is more than 75% full (i.e., after the shift form R3 to R4/R5 stage). To study these metabolic fluxes in more detail, SR oxygen microsensors were used to s tudy the oxygen flux across the seed wall during exposure to variou s lighting conditions. Figure 4 7A below shows a photograph of the oxygen microsensor near the surface of a soybean seed. The concentration duri ng sensor oscillation ( Figure 4 7B ) and oxyge n flux ( Figure 4 7C ) are shown during light and dark conditions. The microsensor was oscillated at a frequency of 30 Hz and an excursion distance of 100 m. For all the light experiments, PAR of incident light was 650 mol/m 2 s. Average oxygen flux durin g light and dark conditions was 33.87.5 pmol/cm 2 sec (efflux) and (influx) 55.74.9 pmol/cm 2 sec respectively. The magnitude of the flux and the time required for shifting from efflux to influx was similar to values reported by McLamore et al. (2011) for G. max roots. Figure 4 8 A D show oxygen concentration and flux during light and dark conditions after addition of 2.7 M atrazine solution (panel a b) and 14.2 M KCN
75 (panel c d). Atrazine is a known inhibititor of photosynthesis; once absorbed by the pla nt, atrazine is translocated upward and accumulates in the growing tips and the new leaves of the plant. However, to date there are no real time results as to the effect of atrazine on the effect of oxygen flux at the seed surface (some results exist for o xygen concentration, but this does not account for the directional movement of oxygen at the seed surface). Average oxygen flux during light and dark conditions with atrazine solution was 56.99.3 pmol/cm 2 sec (efflux) and 36.96.2 pmol/cm 2 sec (influx) re spectively. After adding atrazine, oxygen evolution (lights on) decreased at a rate of 12.9%. However, atrazine had no significant effect on respiratory activity (lights off). Average oxygen flux during light and dark conditions with KCN reduced significan tly; 26.34.728 pmol/cm 2 s (efflux) and 35.587.221 pmol/cm 2 s (influx), respectively. How KCN inhibits oxygen consumption by mitochondria by uncoupling complex IV from cytochrome tranpsort. The data in panel d indicate that 14.2 M KCN caused an uncouplin g effect observed in other plant tissues (McLamore et al. 2010b), but was not a high enough concentration to kill the seed within the time period of the study Figure 4 9 below shows the oxygen concentration inside crosssection of the seed during light con dition. The seed coat was sealed with 1.6% guargum solution in water. Micro sensor was moved 40 m at each step. The seeds were taken from the plants which were grown with 12 hour photoperiod with 30C/22C temperature during day and night respectively. Figure 4 10 shows a representative plot of oxygen concentration inside an ovule during a 24 hr diel cycle. For this representative plot the average concentration at night is 60 3.08 % higher than the average concentration
76 during the day. Automatic tempera ture compensation by the MUX corrected for any thermal artifacts. This trend is the opposite of what was expected based only on respiration and photosynthesis in the developing tissue. This trend may be due to the activity of alternative oxidase, although further studies are needed to investigate this trend. 4.4 Conclusion In Chapter 4 the MUX system developed in Chapter 3 was used to measure the oxygen concentration in the soybean seeds under different environmental conditions. The seed abortion increase s with increase in environmental temperature increase. Changes in oxygen concentration inside the seeds give us some insight about the relation between climate change and its effect on plants. The experiments can be stepping stone to our understanding of c limate change and its effects on plants.
77 Figure 4 1 .Controlled plant growth facility. A ) Photograp h. B) Schematic of USDA ARS controlled plant growth facility situated at the University of Florida in Gainesville, FL. Figure 4 2 Design of experiment for oxygen profiling in soybean. A ) Experimental setup for monitoring oxygen in developing G. max seeds. B ) Photograph of multiplexing system and computer with MUX hardware during the measurement C ) a bright LED (transmission light) was used to locate the desired area for inserting the sensor in G. max seeds
78 Figure 4 3 Basic hardware for SR system Figure 4 4 Process flow for self refe rencing system (McLamore et al. 2010)
79 Figure 4 5 Oxygen concentration in G.max seeds with varying temperature (n=3). Temperature had a significant effect on average oxygen concentration in all seeds. However, there was no significant difference between plants grown at 21% oxygen relative to plants grown at 32% oxygen. Error bars rep resnent the standard deviation of the arithmetic mean. Figure 4 6 Results of oxygen profiling in sobean seeds. A ) Average oxygen concentration in seeds at R3 stage (<75% full) for two oxygen partial pressures (21 kPa and 32 kPa). B ) Average oxygen concentration in R5/R6 seeds (>75% full) stage at two oxygen partial pressure (21 kPa and 32 kPa) rooms.
80 Figure 4 7 R esults of oxygen profiling in soybean seed using microsensor. A ) Oxygen microsensor nea r the surface of soybean seed. B ) Oxygen concentr ation in the seed during light and dark conditions. Oxygen concentration is higher near the surface. C ) Oxygen flux in the seed during light and dark conditions. Postive values indicate oxygen efflux, and negative values indicate oxygen influx.
81 Figure 4 8 Oxygen concentration in soybean seeds. A ) Oxygen concentration in the seed during light and dark conditions with 2 .7 M atrazine solution added, B ) Oxygen flux in the seed during light and dark conditions with 2 .7 M atrazine solution added. C ) Oxygen c oncentration in the seed during light and dark conditions with 14.2 M KCN solution added D ) Oxygen flux in the seed during light and dark conditions with 14.2 M KCN solution added. Postive values indicate oxygen efflux, and negative values indicate oxyg en influx.
82 Figure 4 9 Oxygen concentration in the cross sectional area inside the seed during light conditions. Micro sensor was moved 40 m at each step Figure 4 10 Oxygen concentration inside a seed in the USDA greenhouse measured for 24 hours (a smoothing algorithm (moving average, n=7) was used to process the data).
83 Table 4 1 Temperature regimes used for the experiments (curtsey: L. Zhang, L. H. Allen, Jr., K. J. Boote and B. A. Hauser) Expt # Temperature Experiment period Temperature change start date C Days (DAS) Expt 1 May to Sep 2011 1 30/22 113 6/8 (23) 2 34/26 113 6/8 (23) 3 38/30 129 6/8 (23) 4 42/34 113* 6/8 (23) Expt 2 Sep to Dec 2011 1 30/22 97 11/2 (48) 2 34/26 97 11/2 (48) 3 38/30 97 11/2 (48) 4 42/34 97 11/2 (48) Expt 2 Sep to Dec 2011 1 30/22 91 3/8 (9) 2 38/30 91 3/8 (9) 3 42/34 91 3/8 (9) 4 38/30 91 3/14 (15) 5 42/34 91 3/14 (15) 6 38/30 91 3/22 (23) 7 42/34 91 3/22 (23) 8 38/30 91 3/31 (32) 9 42/34 91 3/31 (32) 10 38/30 91 4/16 (48) 11 42/34 91 4/16 (48)
84 CHAPTER 5 GRAPHENE METAL NANOCOMPOSITES FOR SENSING OXYGEN AND NITROGEN RADICALS 5.1 Background Reactive oxygen species (ROS) and reactive nitrogen species (RNS) (collectively referred to as radicals for brevity) are highly reactive molecules containing oxygen, or nitrogen, respectively. ROS include superoxide anion (O 2 ), 2 O 2 ) and organic peroxides ( OOR) ; RNS in clude nitric oxide radical (NO), nitrosonium cation (NO + ), or nitroxyl radical (NO ). 5.1.1 Relevan ce of Oxygen and Nitrogen Radicals in B iology ROS and RNS (collectively referred to as radicals for brevity) are important signaling molecules used in many s tress response pathways and bioenergetics. Radicals are natural occurring by products that form as a result of metabolic reactions in response to various stimuli. The major site for intracellular radical synthesis is mitochondria, where radicals are produc ed during endogenous and continuous physiological processes under aerobic conditions (Fleury et al. 2002). Xanthine oxidase, nitric oxide synthase, oxalate oxidase, amine oxidases, and lipoxygenase are proteins that play important roles in production of ra dicals; the primary pathway for non enzymatic radical production involves breakdown of NADH. Accumulation of radicals within cells causes oxidative stress, which can be lethal due to inhibition of mitochondrial electron transport. To balance radical produc tion, plant cells regulate photosynthetic and
85 radicals as a signal into appropriate cellular responses by using redox sensitive prote ins (RSP) (Bhattacharjee et al. 2005). For ex ample, H 2 O 2 selectively induces upregulation of defense genes during stress, initiates signal transduction pathways during abiotic and biotic stress, and is a key factor in mediating programmed cell death (Knight et al. 1996, Bhattacharjee 2005). H 2 O 2 is also involved in pathogenesis (microbiocidal), mediating excessive linking of cell wall polymers and inducing signal cascades which lead to synthesis of RSP. Sustained H 2 O 2 levels in cells induce transcriptional stress response (hormones) such as C 2 H 4 hypersensitive response genes, jasmonic acid signaling genes and salicylic acid. In vertebrates, NO is a key signaling molecule involved in a number of processes such as homeostasis, host defense, and development (Jeseta et al. 2012, Nathan 1992). In plants, NO signaling has been linked to germination, growth rate of pollen tubes, stomatal movement, programmed cell death (Planchet and Kaiser 2006), and as a signaling and disease resistant molecule under stress conditions (Neill et al. 2002, Zeidler et al. 2004, Guo and Crawford 2005). In bacteria, NO plays a unique role in toxin biosynthesis, regulation of recovery from radiation damage, nitration of different metabolites, and pl ant pathogenicity (Crane et al. 2010, Sudhamsu and Crane 2009). 5.1.2 Tools for Monitoring R adicals Real time measurement of analyte concentration and flux provides critical information for understanding physiology. Most radicals have a half life of 0.5 to 5 seconds (although this can be prolonged when there a re organics and/or biomolecules pre sent in solution) (Hakim et al. 1996). In living systems, ROS and
86 RON are present at nanomolar (and even picomolar) concentrations. The high reactivity, together with the low concentration in solution makes measurement of ROS or RON very challenging. As reviewed by a number of authors, there a wide range of technologies for monitoring ROS and/or RNS in biological systems (Tarpey and Fridovich, 2001; Taha, 2003; Davies and Zhang, 2008; Privett et al. 2010). Examples include spectroscopic methods (e.g., chemiluminescense and UV visible spectroscopy), indirect methods such as colorimetric assays, fluorescent probes, spin trap electroparamagnetic resonance spectroscopy, and electrochemical (amperometric) electrodes. To be usefu l for measuring NO in living systems, technologies must have five characteristics: i) quantitative sensitivity, ii) high degree of selectivity, iii) rapid response time (< 1 sec), iv) low limit of detection (< 1 M), v) little or no signal hysteresis. To d ate, electrochemical probes persist as the leading technology for meeting these sensor performance criteria in complex solutions (Davies and Zhang, 2008). In the last 3 years, a number of researchers have created graphene metal hybrid nanocomposites that could be useful for development of electrochemical probes used to measure ROS and RNS. Functionalization of graphene with metal oxides helps to maintain the interplanar spacing of the graphene sheets, preventing aggregation into gra phitic structures (Huang et al. 2012 ). Various forms of nanoscale CeO 2 (known as nanoceria) have recently been shown to significantly improve electro and photocatalysis (Nyk et al. 2008, Neil et al. 2011). The catalytic activity of CeO 2 is due to fast transition between Ce(III) a nd Ce(IV), changing the oxygen vacancies at the surface of the nanocomposites structure and forming n typ e hybrids (Joung et al. 2011).
87 A few recent papers have demonstrated development of nanoceria graphene nanocomposites. Jiang et al. (2013) demonstrate d tunable synthesis of ceria nanocrystal/graphene hybrid nanostructures (including nanorods, nanoparticles, and nanocubes). The material demonstrated excellent photocatalytic and electrocatalytic effects. Wang et al. (2011 ) demonstrated use of nano CeO 2 Gr composites for high efficiency batteries; the material showed extremely high specific capacitance, power density, and behaves as a super capacitor. Jiang et al. (2012) synthesized crystalline CeO 2 nanocubes hybridized with graphene sheets using a hydrothe rmal method. The nanocomposites was immobilized on a glassy carbon electrode and used to detect uric acid and ascorbic acid in PBS. Wang et al. (2011 ) synthesized CeO 2 graphene nanocomposites using stimuli responsive polymers. The properties of the nanocom posite can be tuned by controlling thermal and pH response in the polymer structures. C hapter 5 demonstrates use of nCe Gr nPt nanocomposite as a platform for developing electrochemical biosensors based on oxidase enzymes. The simple recipe can be reproduced at room temperature using common/inexpensive materials, and produces a catalyst platform for fabrication of high quality biosensors that demonstrate excellent electroactive surface area, sensitivity, response time, and limit of detection. The pl atform is demonstrated for development of hydrogen peroxide, glucose and xanthine biosensors (Fig ure 5 1 ). C hapter 5 reports a new graphene nanoceria hybrid nanocomposite sensor for detecting ROS or RON. The nanocomposite sensor was tested in
88 laboratory c onditions, and then applied in model biological systems, as well as for monitoring a plasma based reactor used for wastewater treatment. 5.2 Methods 5.2.1 Chemicals and R eagents Single layer graphene oxide (SLGO) was obtained from ACS Materials; L ascorbi c acid and methanol were purchased from Fisher Scientific; cerium (IV) oxide (nanoparticles dispersion, <25 nm particle size, 10 wt% in H 2 O), ascorbic acid, chloroplatinic acid 8 wt % procured from Sigma Aldrich ; hydrogen peroxide (35%), Nafion and o phe nylenediamine was acquired from Acros organics; potassium ferrocyanide trihydrate (K 3 Fe(CN) 6 ) was purchased from EMD chemicals. 5.2.2 Sensor F abrication Prior to analysis, Pt/Ir working electrodes (BASI MF 2013, 1.6 mm diameter, 7.5 cm length, 6 mm shaft diameter, CTFE plastic body) were polished alumina slurry (Buehler , USA). After polishing, all ele ctrodes were ultrasonicated in deionized water for 15 minutes. Platinum nanoparticles (nPt) were first electrodeposited on Pt/Ir microelectrodes by plating at 10 mV for 90 seconds in a solution of 0.002% lead acetate and 0.72% chloroplatinic acid. The Pt/ Ir electrode was connected to the anode lead, and a platinum wire was connected to the cathode lead (Shi et al. 2011) Platinum was electroplated according to the following reactions: At cathode
89 H 3 O) 2 PtCl 6 n H 2 O PtCl 4 + 2 HCl + ( n + 2) H 2 O PtCl 4 PtCl 2 + Cl 2 PtCl 2 Pt++ + Cl 2 At anode Pt+Pt++ > nano platinum Equation ( 5 1 ) A RGO/nanoceria solution was prepared by mixing 1 mL of cerium (IV) oxide nanoparticle suspension (mean nanoparticle size = 12 nm) with 2 m g of SLGO powder and 8 mg of L ascorbic acid. The solution was ultrasonicated for suspension was spin coated at 2600 rpm on nPt modified electrodes and then air dried at room temp erature. Finally, another layer of nPt was electrodeposited on the nCe RGO composite by sonoelectrodeposition at 10 V for 30 seconds. Electrodes functionalized with graphene metal hybrid nanocomposite were coated in OPD and Nafion as previously described (Friedemann et al. 1996; Koehler et al. 2008; Porte rfield et al. 2001) Electrodes were coated with Nafion procedure was repeated twice so as to provide the electrode 2 layers of Nafion over the graphene metal nanocomposite. Probes were then electrolyt ically functionalized with an OPD membrane using a solution of 5mM OPD in 0.1mM ascorbic acid and 100mM PBS (pH 7.4). OPD functionalization was performed at a constant +900mV potential until a stable current was obtained (less than 2% variability) based on the methods used by Porterfield et al. (2001). An Ag/AgCl
90 electrodes submersed in in 3M KCl was used as the counter electrode to complete the circuit during OPD deposition. 5.2.3 Sensor C haracterization Electrochemical characterization was performed using a 3 electrode cell stand (C 3, BASi, West Lafayette, IN) as described in Chapter 4. DC potential amperometry (DCPA) was conducted in PBS at a working potential of +500 mV versus an Ag/AgCl reference electrode with a sampling rate of 1 kHz. The working sol ution was polarized for 1 hour; the current output was measured at constant potential while successively injecting analyte (H 2 O 2 ) in the stirred solution (450 rpm) at 2 minutes intervals until electrical signal to reached steady state (less than 5% deviati on). A Bioanalytical Systems, Inc, (West Lafayette, IN) electrochemical cell was used for all electro analytical experiments. A three electrode approach was used, consisting of a principle, reference, and auxiliary electrode. The cell stand used on board s tirring (magnetic stir bar) for all experiments. The cell stand was enclosed in a Faraday Cage to minimize external electrical interferences. Sensitivity, selectivity, response time and surface area were measured for all electrodes using the potentiostat. Calibrated microsensors were used in self referencing modality to directly monitor NO flux in developing ovules. In particular, selectivity is an important issue due to the lack of a NO specific sensing mechanism. In particular, exudates such as H 2 O 2 and n egatively charged organic acids will be tested for selectivity over NO. For these selectivity tests, each interferent will be added to the calibration solution at levels commonly found near the developing seed. Shifts in calibration slope and response time will
91 be calculated following the guidelines established by the International Union of Pure and Applied Chemistry (IUPAC). 5.2.4 Imaging of N anomaterials Scanning electron microscope (SEM) uses a focused beam of high energy electrons to generate a variety of signals at the surface of solid specimens. The signals that derive from electron sample interactions reveal information about the sample including external morphology (texture), chemical composition, and crystalline structure and orientation of materials making up the sample. Scanning image microscopy was performed at Major Analytical Instrumentation Center located at U niversity of Florida. A JEOL SEM 6400 with an acceleration voltage of 30 kV and magnification of 80,000X was used for all SEM imaging. Optical profiling was performed at the Department of Mechanical and Aerospace Engineering at the University of Florida (Courtesy: Dr H. Yamaguchi Greenslet and M.A. Tan). All the experiments were performed using a Zygo NewView 7200 Optical Profilometer. The profilometer measures surface heights at the nanometer scale and is used for characterizing and quantifying surface r oughness, step heights, critical dimensions, and other topographical features with precision and accuracy. 5.2.5 Statistics Characterization of all sensors was performed with at least three replicates (value of n reported for each Figure that was not a triplicate). Either test or analysis of variance (ANOVA) were used based on the open source programming language R. A confidence level of 95% was used for all
92 statistical analysis. Where applicable, error was calculated using th e standard error of the arithmetic mean (all error bars represent standard error). 5.3 Results and Discussion Fig ure 5 2 shows a representative SEM of as prepared nPt RGO nCe (Figure 5 2 A ) and nPt RGO nCe nPt ( Figure 5 2 B ) hybrid composites. In Figure 5 2 A agglomerates of nanoceria are clearly visible. These structures are similar to the morphology reported by Sun and Wang (2013) and Panky et al. (2013). When nCe aggregates were electroplated with nPt, the surface was more homogenous and contained nPt spheres approximately 20 nm in diameter. The size of these nanospheres is similar to nPt structures depo sited on CNTs (Claussen et al. 2011, McLamore et al. 2011). EDX analysis ( Figure 5 2 C ) indicated that the nPt structures (two different types of Pt) we re intercalated with nCe (four different types of Ce). In fact, the elemental composition of the upper layer was balanced with Pt and Ce, containing 53.13.8% platinum and 47.93.3% cerium. This indicates that molecules which contact the surface of the hyb rid nanocomposite have access to both nanoceria and nanoplatinum. 5.3.1 Electrochemical Performance of Hybrid N anocomposite toward H 2 O 2 NO and O 2 To conduct a baseline comparison for biosensors developed with oxidase enzymes, electroactive surface area ( in 4mM FeKCN) and sensitivity towards hydrogen peroxide were measured. Representative cyclic voltammograms and DCPA time series during peroxide injection are shown in Fig ure 5 3 The electroactive surface area of bare, unmodified Pt/Ir electrodes was 1.9 0.2 X 10 2 cm 2 (Fig ure 5 3 B ). Deposition of nPt onto bare Pt/Ir electrodes increased
93 surface area by 52 % (1.9 0.2 X 10 2 cm 2 ). When RGO was spin coated on nPt modified electrodes, electroactive surface area increased by approximately 168% (5.1 X 10 2 cm 2 ). The electrocatalytic behavior of the RGO hybrid nanocomposites is due to transformation of GO to reduced n type hybrids (Joung et al. 2011). Deposition of nPt onto RGO further increased electron transport due to formation of junctions between RGO an d the Pt/Ir electrode by 168% (5.1 0.2 X 10 2 cm 2 ). Similar effects have been observed in other metal graphene composites, where metal oxides uniformly dispersed on the plane of graphene facilitate d charge transfer (Jiang et al. 2012, Ji et al. 2013). Co nversely, deposition of nCe onto bare Pt/Ir reduced electroactive surface area by approximately 21% (1.5 2.0 X 10 2 cm 2 ). This result was surprising, as Ansari et al. (2008 ) and Solanki et al. (2009) showed that nanostuctured cerium oxide (35 nm) was an electrocatalysts. This discrepancy is likely due to the indium tin oxide (ITO) that was included in the design by Solanki et al. (2009), the manuscript did not investigate whether the nCe, ITO, or nCe ITO composite was the source of the catalysis. For our work, when nCe was spin coated on nPt RGO hybrid nanomaterial, the surface area increased by 63% (3.1 1.6 X 10 2 cm 2 ), but was lower than nPt RGO. The highest electroactive surface area (6.2 X 10 2 cm 2 ) was observed for hybrid nPt RGO nCe nanocomposites with a second deposition of nPt on top of the nCe (i.e., nPt RGO nCe nPt). This increase in electroactive surface area is due to the availability of Pt sites along the surface of nCe modified graphene (as shown by EDX in Figure 5 2 C ). A representative DCP A plot showing sensitivity of the nPt RGO nCe composite hybrid nanomaterial toward H 2 O 2 is shown in Fig ure 5 2 C When
94 comparing the various hybrid nanocomposites previously discussed, similar trends were noted regarding the inclusion of nPt, RGO, and nCe. Composites of nPt and RGO had a high sensitivity toward H 2 O 2 while electrodes with nCe as the upmost layer had a relatively low sensitivity toward H 2 O 2 to bare Pt/Ir). The highest sensitivity toward H 2 O 2 was for the nanocomposite which contained nPt RGO nCe nPt (11.1 3.8 A/mM), which is nearly a four fold increase in sensitivity compared to bare Pt/Ir and a two fold increase over nCe RGO. The data in Fig ure 5 3 support the conclusion by Huang et al. (2009) that graphene metal nanocomposites have ordered interplanar spacing that prevents aggregation into graphitic structures and maintains high electron transport efficiency. Our results indicate that nPt is more efficient than nCe for maintaining the catalytic activity of RGO. The lower limit of detection (LOD) for the nPt RGO nCe Response time (t 95 ) was calculated by averaging the 95% steady state response time of three successive step changes over the linear range tested. The steady state response was determined by performing non linear regression over single step changes in concentration (exponential rise to maximum/ single, 3 parameter in SigmaP lot 12.0) according to the following expression: Equation ( 5 2 ) The response time of a single step change can be computed as follows: E quation ( 5 3 )
95 Table 5 1 summarizes similar carbon/metal nanocomposite based devices recently reported in the literature in terms of hydrogen peroxide detection. The nPt RGO nCe nPt hybrid developed here exhibited an electrochemical performance comparable to most car bon/metal composites in the current literature with a few exceptions. The relatively high sensitivity towards hydrogen peroxide is due to the intrinsic oxidase activity of nanoceria together with the excellent catalytic be havior of nPt RGO (Asati et al. 20 09). Ensafi et al. (2013) synthesized a hybrid nanocomposite containing exfoliated graphene oxide (EGO) and a different rare earth metal oxide (Co 3 O 4 ) on a glassy carbon electrode. The electroactive surface area of the 2 ) and sensit ivity (74.88 A/mM) were higher than the hybrid nanocomposite presented here. From the data presented by Ensafi et al. (2013) it is unclear whether the Co 3 O 4 or the EGO was the source of the increased sensitivity toward H 2 O 2 and no standard deviation was reported; rendering a direct comparison impossible. Han et al. (2013) describe a hybrid nanomaterial composed of cobalt hexacyanoferrate nanoparticles (CoNP), carbon nanotubes (CNT), and nPt that showed the highest known sen sitivity toward H 2 O 2 (744 A/mM). The authors attributed the extremely high sensitivity of this composite to the presence of nPt, which triggered a synergistic electroctalytic effect when conjugated with CoNPs and CNT. However, CoNPs have demonstrated intr ins ic cytotoxicity (Alarifi et al. 2013, Jiang et al. 2012), which limits application in life sciences research and may even render biological elements (such as enzymes) inactive.
96 After developing the catalytic nanomaterial platform, Nafion and OPD were d eposited on the electrode. Figure 5 5 shows an electrodeposition plot of OPD immobilization at +900mV. Based on the work of Porterfield (2001), once a thin OPD film forms on the electrode surface (after approximately 45 min), the output current stabilized. Representative DCPA time series during peroxide, nitric oxide and oxygen anion injections are shown in Fig 5 6 a, 5 6 b and 5 6 c. Average sensitivity towards H 2 O 2 was 0.816+/ 0.3 Amp/M with a lower limit of detection (LOD) of 3.96 3.11 mM, and a resp onse time of 17.54 10.6 sec. Average sensitivity towards NO and O 2 was was 204.9241.19 A/mM and 27.6 4.1 nA/mM, respectively. Selectivity of the free radical sensor was measured against ascorbic acid (AA) (0.5 mM) and potassium ferro cynide (K 4 FeC N 6 ) (0.5 mM). In stirred solution of 20 ml PBS 1 L addition were made in following order, H 2 O 2 AA, H 2 O 2 K 4 FeCN 6 H 2 O 2 AA, H 2 O 2 Representative selec tivity plot is shown in Figure 5 7 5.4 Conclusion ROS and RON plays very important role in various metabolic pathways and stress signaling in plants. To understand about plant physiology in depth, we need to understand more about these molecules. Various techniques have been developed for measuring these important free radicals, but the short half life and rapid conversion in biological systems present a challenge for sensor development. The electrochemical sensors developed here using biocompatible hybrid nanocomposite demonstrate significant advantages o ver available techniques. The fast response time allows these sensors to be used for real time
97 monitoring, while the high sensit ivity, limit of detection, and selectivity ensure sensors can detect free radicals at biologically relevant concentations. This sensing platform provides a promising and high fidelity alternative for development of tools to measure free radicals in biological systems.
98 Figure 5 1 Conceptual schematic of nCe RGO nPt hybrid nanocomposites for measuring ROS and RNS. Nanoceria catalyzes detection of O2 by facilitating dismutase reactions. The graphene nanoplatinum layer catalyzes detection of H2O2 or NO.
99 Figure 5 2 Material characterization of graphene metal hybrid nanocomposites. A ) Representative SEM of nPt RG O nCe nanocomposite. Nanoceria formed 20 nm aggregates (inset shows expl oded view of image in panel a). B ) SEM image of nPt RGO nCe nPt nanocomposite. A homogenous nPt nCe layer on the surface enhanc es electroactive surface area. C ) EDX spectra confirm the presence of both cerium and platinum in the upper layers of the nanocomposite.
100 Figure 5 3 Electrochemical performance of metal g raphene hybrid nanocomposites. A ) Representative cyclic voltammograms in 4mM FeKCN for Pt/Ir electrode modified with nPt R GO nCe nPt hybrid nanocom posite at various scan rates. B ) Average electroactive surface area of hybrid nanocomposites formed with nanoceria, nanoplatin um and reduced graphene oxide. C ) Representative sensitivity of nPt RGO nCe nPt electrodes towards H2O2 D ) Average sensitivity toward H2O2 for hybrid nanocomposites formed with nanoceria, nanoplatinum and reduced graphene oxide
101 Figure 5 4 Non linear regression on the one step change to determine steady state response using exponential rise to maximum/ sin gle, 3 parameter in SigmaPlot 12.0 Figure 5 5 Current versus time plot for OPD electroplating on 2m diameter platinum microelectrode modified with platinum black and MWCNTs
102 Figure 5 6 Electrochemical performance of Free radical se nsor towards H2O2, NO and O2 A C ) Representative DCPA time series during stepwise addition of H2O2, NO, and O2 respectively. D F ) Average sensitivity toward H2O2, NO, and O2 respectively (n=4) Error bars represent standard error of the arithmetic mean Figure 5 7 Selectivity of hybrid nanocomposite sensor towards AA and K 4 Fe(CN) 6
103 Table 5 1 Summary of performance for recent H 2 O 2 sensors using similar nanocomposites ( NR = value not reported in manuscript) Nanocomposite Electroactive surface area [cm 2 X 10 2 ] Sensitivity [A/mM] LOD [M] Reference nPt/RGO/nCe/nPt 6.2 1.5 11.1 3.8 0.5 0.3 This work N Ce NR 0.4 NR Mehta et al. (2007) CNT/TMOS 6.2 0.2 12.8 1.9 3.7 NR Shi et al. (2011) GrOx/Pt black 0.6 0.05 4.8 2.2 1.0 NR Shi et al. (2012) CNT/GA 5.1 0.7 13.7 2.6 1.9 NR Shi et al. (2011) Graphene/ MWCNTs NR 0.431 NR 9.4 NR Woo et al. (2012)
104 CHAPTER 6 CONCLUSIONS AND FUTURE DIRECTIONS This work focuses on development of tools for monitoring oxygen and reactive oxygen species in biological systems. Sensors were used in proof of concept applications in plant systems to demonstrate how these tools can help understand plant physiology. In t he Chapter 2, various available techniques for measuring these molecules were discussed to allow comparison of the new sensors to other technologies in th e literature (Chaturvedi et al. 2013). The multiplexing system developed in Chapter 3 presents a new p latform for these sensing tools in field applications. The developed sensors were used to better understand the effect(s) of high temperature and oxygen partial pressure on soybean seed set (Chapter 4). By understanding the effect of these environmental co nditions on plants, we can better understand about the climate change and its effect on plants. The application of this system is not limited to plants, with slight modification; the system can be used in other applications for measuring other molecules as well (as demonsrated in C haturvedi et al. in review ). In the Chapter 5, electrochemical sensors were developed using biocompatible nanomaterial hybrid composites. This sensing tool was demonstrated for measuring reactive oxygen species and reactive nitrogen species, signaling molecules in plants. To further extend this work, the sensors and sensing platform developed can be applied in other applications for measuring various mol ecules with slight modification (Chaturvedi et al. 2013 ). Overall, this dissertation describes the develpment of sensor tools which can be helpful in agricultural, environmental and biomedical reserach.
105 APPENDIX A SUPPLEMENTARY FIGURES Figure A 1. Photograph of microoptrode inserted into a Brassica napus silique. Fi gure A 2. Experimental design for hyperoxia in Brassica napus A) Gas distribution manifold. B) C lose up of the syringe ba rrels over the siliques for in situ hyper oxygen treatment Micro optrode
106 Figure A 3. Conceptual schematic of nCe RGO nPt nanocomposites for biosensing applications. A) After deposition of amorphous nanoplatinum (nPt ). B) Reduced graphene oxide (RGO). C) CeO 2 nanoparticles (nCe) are spin coated on the surface. Finally, nPt is used to create electrical junctions between nCe, RGO and the electrode surface. Enzymes are immobilized on top of the catalyst layer by encapsulation in a hy drogel
107 Figure A 4. Effect of temperature (panels a b) and salinity (panels c d) on optrode performance. Panels a and c represent data collected with the MUX system. For comparison, panels b and d represent data collected using commercial optrodes and ha rdware from Wolrd Precision Instruments (WPI; MicroOxy ).
108 APPENDIX B ROS ACCUMULATION IN ARABIDOPSIS THALIANA OVULES AND SEED ABORTION Introduction This project is focused on improving our understanding of the underlying physiological mechanisms related to NO transport in developing A. thaliana ovules. Ultrasensitive nanomaterial mediated NO sensors will be developed which allow non invasive measurement of endogenous NO flux. Noninvasive measurement of NO flux in various genotypes of A. thaliana will improve our understanding of NO signaling in Arabidopsis thaliana. Methodology Plant growth and sample preparation Arabidopsis thaliana plants were received from Plant physiology Lab (Courtesy: Dr Bernie Hauser). A. thaliana were grown in the UF plant physiology lab located in the Department of Bio logy at the University of Florida. Two genotypes were selected for these experiments: Columbia 0 (wild type) and embryo lethal mutant (EMB). EMB is a mutant that segregates 3:1; 75% viable, 25% embryo lethal seeds). Soil (0.5 liters) (Scotts Miracle gro Co mpany) was used to grow the plants. Plants were watered every other day with 100 ml of tap water. Plants were grown in white light with a 14 hr photo period. Measurements of NO flux were taken 37 days after sowing. Four different plant treatments were use d for this study, including: i) wild type (Col) with no salt stress, ii) salt stressed Col, embryo lethal mutant with no salt stress, and iv) embryo lethal mutant with salt stress (7 5mM NaCl) (as shown in Figure B1 ).
109 Figure B 1 Results from four diffe rent plant types used for experiments. A ) W ild ty pe (Col) with no salt stress. B ) S alt stressed Col, embryo lethal mutant with no salt stress, and iv) embryo lethal mutant with salt stress. 37 days after sowing, Col siliques are approximately 8 10 mm in l ength, and 400 600 m in diameter (Fig ure B 2 ). The siliques wall is carefully opened using sharp edged dissecting blades under a dissecting microscope. After opening the silique wall, seeds are placed in a petridish containing 0.1M MS solution SR NO mic rosensors were used to non invasively measure flux at the surface of developing ovules. Measurements of flux were taken immediately after the dissection.
110 Figure B 2 Columbia background A. thaliana siliques 37 days after sowing. A ) A penny was used as a frame of referen ce for size. Scale bar = 5 mm. B ) dissecting microscope used to prepare silique for measurement of ovule NO flux Results Figure B 3 shows a representative real time plot of NO flux for an EMB ovule. The average NO eff lux for this ovule over the ten minute measurement was 0.660.02 pmol cm 2 sec 1 Figure B 3 Representative real time plot of NO efflux measurement from a n EMB ovule
11 1 Figure B 4 shows the average NO efflux for the four different types of plants measured (n=1 ovule). The NO efflux was higher in plants which were mutated or salt stressed. As evident from the Figure B 4 the average NO efflux in wild type unstressed ovules was significantly lower than stressed plants. Ongoing replications will provide furth er information as to whether this is statistically significant. Figure B 4 Average NO flux for wild type and EMB mutant A. thaliana ovules under salt stressed and unstressed condition (n=1 for each test)
112 APPENDIX C DEVELOPMENT OF MUX A photo multiplier tube (PMT) was used to convert photons in the emission signal to an electrical signal. The PMT used consisted of a photo cathode and a series of dynodes in an evacuated glass enclosure. Dynodes were maintained at succ essively less negative potentials, and a current is recorded by the MUX acquisition hardware using a National Instruments driver card. Figure C 1 Schematic of photomultiplier tube used to produce measured current There are 6 front panels in the Lab vie w corresponding to 6 main controls: .Data Acquisition (DAQ) 1 Motion (motion control system) 2 Monitor (real time input from LED and output from PMT) 3 Chart (long term monitoring of 10 channels) 4 Calibration (stored calibration values) 5 Diagnostics (troubleshooting and maintenance coding)
113 Figure C 2 Screenshot of DAQ in MUX software. output wave forms along with their properties are shown. Sine wave is used in bot h the cases. Maximum and minimum amplitudes for both the input and output waves are 10 to 10 V respectively. Desired frequency is kept at 5000 htz. Resulting frequency is also displayed. Current to voltage ratio for output wave is set at 10 mA/V.
114 Fi gure C 3 Screenshot of motion control system in MUX software. Motion panel describes the speed and positions related to stepper motor and fiber optic channels. Acceleration and deceleration rates for the stepper motor are 0.2 inch/sec 2 Objective lens is directly controlled by the stepper motor. Maximum velocity that can be achieved by the stepper motor is set at 0.25 inch/sec. Only 5% of the total current is provided to run the motor so as to avoid any kind of electrical damage to the motor. Range of the movement provided by the motor in horizontal direction is 0.05 to 4.5. Normal motion cycle is set up at 250 ms. Fiber location tabs provide the actual position on x axis. These locations are set in a way so as to maximize the intensity of light on the o bjective lens.
115 Figure C 4 Screenshot of sensor output in MUX software. Monitor panel displays the real time values during the experiments. Phase angle, amplitude and calibrated oxygen values are displayed in graphical form. These values are filtered values. Numbers of points used for filtering the value (moving average) can b e changed. Maximum noise filter without the loss of any information is achieved using the moving of 50 points. Input and output waves are also displayed. Phase angle between input and output waves and the amplitudes are displayed as well. Sensors calibrati
116 Figure C 5 Screenshot of multiplexing output in MUX software. Chart panel displays the averages of ph ase, amplitude and oxygen concentration of the data points collected in 10 sec. Different channels can be represented on the charts using the color coded on/off tab. Automated movement of the objective lens among the channels can be done from this panel. S can interval defines the time it takes to read all 10 channels.
117 Figure C 6 Screenshot of calibration screen in MUX software. Calibration panel provides option of 2 or 3 point calibration. It provided the option of selecting the concentrations and phase angles values are stored as well. Figure C 7 Screenshot of sensor diagnostics and troubleshooting in MUX software.
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138 BIOGRAPHICAL SKETCH Prachee Chaturvedi received her Ph.D. from University of Florida in s pring 2014. She gradua ted with Doctor of Philosophy in Ag ricultural and Bio logical Engineering from H.B.T.I. Kanpur, India in s pring 2007. After that she worked with Maruti Suzuki India Ltd for 2.6 yrs. During the summer 2012, she participated in internship with the NASA KSC. These experiences provided her with valuable experiences and insights. Prachee was a member of a number of professional and student organizations including the UF Chapter of Institute of Biological engi neers. She has presented her resea rch at international conference meetings and workshops including the IBE and SPIE. Additionally she has published her research in the international journals. In the short term, she looks forward to begin work as Biological engineer for a consulting or rese arch firm, where she can apply her knowledge and practical experience in the profession.
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