A summary of previously published electrochemical glutamate biosensors, their electrode material, surface modification configuration, analytical figures of merit, and authors.
\r\n\tHomeostasis is brought about by a natural resistance to change when already in the optimal conditions, and equilibrium is maintained by many regulatory mechanisms. All homeostatic control mechanisms have at least three interdependent components for the variable to be regulated: a receptor, a control center, and an effector. The receptor is the sensing component that monitors and responds to changes in the environment, either external or internal. Receptors include thermoreceptors and mechanoreceptors. Control centers include the respiratory center and the renin-angiotensin system. An effector is a target acted on to bring about the change back to the normal state. At the cellular level, receptors include nuclear receptors that bring about changes in gene expression through up-regulation or down-regulation and act in negative feedback mechanisms. An example of this is in the control of bile acids in the liver.
\r\n\tSome centers, such as the renin-angiotensin system, control more than one variable. When the receptor senses a stimulus, it reacts by sending action potentials to a control center. The control center sets the maintenance range—the acceptable upper and lower limits—for the particular variable, such as temperature. The control center responds to the signal by determining an appropriate response and sending signals to an effector, which can be one or more muscles, an organ, or a gland. When the signal is received and acted on, negative feedback is provided to the receptor that stops the need for further signaling.
\r\n\tThe cannabinoid receptor type 1 (CB1), located at the presynaptic neuron, is a receptor that can stop stressful neurotransmitter release to the postsynaptic neuron; it is activated by endocannabinoids (ECs) such as anandamide (N-arachidonoylethanolamide; AEA) and 2-arachidonoylglycerol (2-AG) via a retrograde signaling process in which these compounds are synthesized by and released from postsynaptic neurons, and travel back to the presynaptic terminal to bind to the CB1 receptor for modulation of neurotransmitter release to obtain homeostasis.
\r\n\tThe polyunsaturated fatty acids (PUFAs) are lipid derivatives of omega-3 (docosahexaenoic acid, DHA, and eicosapentaenoic acid, EPA) or of omega-6 (arachidonic acid, ARA) and are synthesized from membrane phospholipids and used as a precursor for endocannabinoids (ECs) mediate significant effects in the fine-tuning adjustment of body homeostasis.
\r\n\t
\r\n\tThe aim of this book is to discuss further various aspects of homeostasis, information that we hope to be useful to scientists, clinicians, and the wider public alike.
Glutamate is a nonessential amino acid, a precursor for gamma aminobutyric acid (GABA (the primary inhibitory neurotransmitter)), and an abundant excitatory neurotransmitter in the mammalian central nervous system that is produced by pyramidal cells located in the cerebral cortex and hippocampus. It has an important role in the formation and stabilization of synapses, long-term potentiation (i.e., the long-lasting enhancement in signal transmission between two neurons which results from synchronously stimulating them), neurodegenerative diseases, learning, and the formation of memories. In addition, maintaining a balance in glutamate levels in the central nervous system is important because increased levels lead to neurotoxicity and cell death, while decreased levels result in impaired long-term potentiation, impaired synaptic plasticity (i.e., the ability of synapses to change their structures in response to inputs and changes in their environment), and impaired cognitive performance [1]. Glutamate also plays a pivotal role in cellular metabolism as it is associated with transamination reaction, a key step in amino acid degradation, and is the product of deamination. Hence, quantifying glutamate levels in biological fluids and tissues reliably and reproducibly is of interest in many disciplines.
\nGlutamate dysregulation may induce excitotoxicity, which is closely associated with multiple psychiatric and cognitive disorders. Glutamate is hypothesized to have a key role in the pathophysiology of psychiatric disorders such as schizophrenia [2] and depression [3]. It is also currently a target of novel drugs for potential treatments of schizophrenia and depression. Briefly, the glutamate hypothesis of schizophrenia suggests that there is a hypofunction in
In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), schizophrenia is defined by a group of characteristic symptoms which consist of (1) psychotic or positive symptoms (i.e., symptoms characterized by the presence of something that should be absent, such as hallucinations or delusions), (2) disorganized symptoms (such as disorganized speech–e.g., incoherence or frequent derailment, disorganized or catatonic behavior, and inappropriate affect), (3) negative symptoms (i.e., symptoms characterized by the absence of something that should be present, such as avolition [lack of motivation], affective flattening [diminished emotional expression], or alogia [poverty of speech]), (4) deterioration in social, occupational, or interpersonal relationships, and (5) continuous signs of the disturbance for at least 6 months [1].
\nGlutamate-based theories of schizophrenia resulted from the observation that two related compounds—phencyclidine (PCP) and ketamine—produced symptoms and cognitive deficits in healthy subjects which resembled those found in schizophrenia. These symptoms, including negative as well as positive symptoms, resolved following elimination of the compounds. Years later, further research revealed that these compounds produced their effects by blocking neurotransmission at NMDA-type glutamate receptors which in turn gave rise to current glutamate-based models of schizophrenia. In addition, it has been found that many suspected genes for schizophrenia affect glutamate neurotransmission. Also, NMDA receptors are regulated by brain levels of glycine, D-serine, and glutathione such that disturbances in concentrations of these compounds and of the genes related to their synthesis may all contribute to schizophrenia. Since available data to date suggests that there is more than one single cause of NMDA receptor dysfunction among different individuals, perhaps threshold models are more useful in that for each individual, the sum of genetic and environmental factors determines whether NMDA receptor functions will fall below a critical level. In schizophrenia, it is postulated that psychosis may emerge once the level of NMDA receptor function decreases by approximately 20%, with worsening symptom severity thereafter. Furthermore, since glutamate and NMDA receptors are widely distributed throughout the brain, this has led to a concept of “whole brain” dysfunction, which involves the prefrontal, limbic, auditory, and visual cortices among others. Some specific examples of the consequences of glutamate deficits in certain brain regions include (1) working memory impairments and inability to unlearn dysfunctional behavior patterns due to glutamate deficits in the dorsolateral prefrontal cortex, (2) impaired response inhibition and hence an increased tendency for impulsivity and impulsive aggression due to glutamate deficits in the inferior prefrontal cortex, (3) impairments in learning and memory formation which contribute to psychosis and delusion formation due to glutamate deficits in the medial temporal cortex, (4) perceptual changes including impaired reading ability due to glutamate deficits in the visual cortex, and (5) impaired ability to detect vocal intonation which leads to impairments in emotional recognition and social cognition due to glutamate deficits in the primary auditory cortex. Thus, widespread cognitive dysfunction in these brain regions due to dysfunctional glutamate and NMDA-type glutamate receptors represents a formidable obstacle that prevents individuals with schizophrenia from returning to premorbid levels of functioning [5].
\nIn addition to its key roles in fundamental neurological processes and neurological disorders, glutamate is important in protein synthesis, protein degradation, and nitrogen metabolism. The concentrations of glutamate in intracellular environment vary from 2 to 20 mM [6]. Usual glutamate concentrations in plasma are approximately 150 and 10 μM in cerebrospinal fluid [7, 8]. Normal glutamate concentration in the extracellular space ranges between 1 and 80 μM [9]. The quantification of L-glutamate is also important in food analysis due to questions about its safety as a food additive. Monosodium glutamate (MSG, C5H8NO4Na), invented by Dr. Kikunae Ikeda in 1908, is a commonly used flavor-enhancing additive found in Chinese restaurant food [10, 11], canned soups, canned vegetables, and processed meats. Interestingly, there has been controversy surrounding the use and safety of MSG as a food additive. According to some studies, excessive intake of MSG may cause headaches and stomach pain in certain individuals as well as neuronal excitotoxicity. However, use of MSG as a food additive is generally regarded as harmless. Still, many food manufacturers choose to advertise their products as being MSG-free. Development of MSG biosensors for food applications is also an active area of research. For example, Monošík et al. recently prepared and characterized a bienzymatic nanocomposite electrode for quantification of MSG in food samples, utilizing L-glutamate dehydrogenase and diaphorase enzymes immobilized between chitosan layers on nanocomposite electrodes consisting of multiwalled carbon nanotubes (MWCNTs) [12]. The structure of monosodium glutamate is shown in Figure 1.
\nThe chemical structure of monosodium glutamate.
This chapter will primarily focus on providing an overview about various enzyme-based glutamate biosensors that utilize sensitive electrochemical detection methods. In addition, the characterization and optimization of newly developed enzyme-based electrochemical biosensors will be discussed briefly. A brief overview of the most common electrochemical detection methods utilized in biocatalytic glutamate sensor characterization, testing, and in quantitative analysis will also be provided. We will begin with a brief introduction to electrochemical biocatalytic sensors.
\nIn general terms, biosensors are devices that register a biochemical reaction which is then converted into a signal that can be detected and quantified [13]. A typical biosensor contains biological recognition molecules, such as enzymes, that are highly selective and specific for a given analyte. In electrochemical biocatalytic sensors, the biological recognition molecules bind reversibly to a particular analyte on or near an electrochemically active interface which may incorporate nanomaterials, giving rise to a measurable signal [13]. An electric transducer, usually a modified electrode, which is in contact with the electrochemically active interface, converts the biochemical reaction into an electrical signal that is further amplified by a signal processor into a useful form. The development of electrochemical biosensors for determination of glutamate is an active area of research as these sensors are typically easy to use, fast, reliable, convenient, portable, and affordable. Various instrumental analysis techniques such as spectrophotometry [14, 15], chemiluminescence [16], capillary electrophoresis [17–19], gas chromatography [20], and high-performance liquid chromatography [21–23] have also been utilized in detecting glutamate. However, these methods can be expensive, time-consuming, labor intensive, often require sample preparation, and utilize sophisticated instruments which require trained personnel to operate. Specifically, chromatography methods often require analyte derivatization, while spectrophotometry requires tedious sample pretreatment procedures.
\nGlutamate biosensors have been developed for both in vitro and in vivo applications. One of the challenges surrounding the utilization of sensitive and selective in vivo monitoring of dynamic levels of extracellular glutamate in living tissues using biosensors is that unlike an electroactive analyte such as dopamine, glutamate is nonelectroactive. Therefore, the direct measurement of glutamate using voltammetric electroanalytical techniques is not possible. However, if a biological recognition component such as an enzyme is added onto a physical transducer, in this case the electrode, the glutamate levels may be measured indirectly by quantifying one of the enzymatically generated products at the biocatalytic sensor [13]. A few commercial biosensors for routine glutamate measurements in the food industry are available from companies such as Yellow Springs Instruments (Yellow Springs, OH, USA).
\nMicrodialysis methods have been widely used in monitoring extracellular glutamate levels in the brains of conscious animals. Yao and Okano described an in vivo flow-injection biosensor system with online microdialysis samples attempting to simultaneously determine the concentrations of L-glutamate, dopamine, and acetylcholine [24]. The triple electrode measured an average of 6 μM of L-glutamate in rat brain, whereas dopamine and acetylcholine levels were below the detection limit of the biosensor.
\nFurthermore, studies have shown that the glutamate detected using microdialysis is not or is only partly derived from synaptic transmission. As a result, development of appropriate biosensors for the accurate detection and monitoring of glutamate in vivo remains an ongoing endeavor. The ideal in vivo biosensors would be enzyme-based electrochemical biosensors in which a highly specific enzyme is immobilized on the surface of a sensitive electrochemical transducer. Indeed, certain authors have reported the fabrication and utilization of glutamate biosensors capable of second-by-second in vivo monitoring of glutamate levels in freely moving rats and mice [25, 26].
\nNanotechnology and advances in microfabrication technology have played a crucial role in the fabrication of many biosensors. Nanoscale materials, with at least one dimension ranging in size from 10−7 to 10−9 m, have been incorporated into various enzymatic biosensors including many of the more recent L-glutamate sensors. The trend to manufacture smaller and more portable biosensor devices with improved performance has in part led to the incorporation of nanomaterials into biosensors. The unique chemical and physical properties of the nanomaterials also enhance the analytical performance of these biosensors. The nanomaterials utilized include carbon nanotubes (CNTs), graphene, nanowires, carbon fibers, and metal nanoparticles (NPs). Electrochemical biosensors that incorporate nanomaterials such as CNTs on the transducer surface generally have very good conductivities and much greater surface areas onto which enzymes and other molecules may be immobilized.
\nRecently, combining the high bioselectivity and specificity of oxidoreductase enzymes with the numerous and advantageous chemical and physical properties of organic and inorganic nanomaterials such as graphene, carbon nanotubes, carbon fibers, and gold has resulted in the development of highly sensitive and stable electrochemical biosensors with significantly improved performance for many analytes of medical interest, including glutamate.
\nMany glutamate biosensors are based on quantifying the oxidation of hydrogen peroxide (H2O2) liberated in a chemical reaction between glutamate oxidase (GmOx, an oxidoreductase enzyme, EC 1.4.3.11) and L-glutamate in the presence of oxygen (O2), water (H2O), and a flavin adenine dinucleotide (FAD) cofactor [27–45]. GmOx enzyme catalyzes the oxidative deamination of glutamate resulting in the formation of 2-oxoglutarate (i.e., α-ketoglutarate, α-KG), ammonia (NH3), and H2O2 [46].
\nThe oxidation of hydrogen peroxide occurs at the electrode surface or at very short distances away within the sample solution. The resulting current (i.e., detectable signal) which is proportional to the concentration of redox active species is then quantified by the transducer.
\nGmOx is a highly selective enzyme [47] and therefore unlike for many catalytic enzyme-based biosensors, interference from unwanted enzymatic reactions with similar molecular species is not a major concern for these glutamate biosensors. However, there is one study which mentions that a GmOx enzyme had a slight sensitivity (0.6 %) for L-aspartate, an amino acid with a similar side-chain group to L-glutamate [46].
\nIn addition, electrooxidation of the GmOx-generated hydrogen peroxide requires relatively high positive potential at which common electroactive interferents, such as ascorbic acid and dopamine, also undergo oxidation thereby adding to the current (i.e., the signal) [48]. Thus, the elimination of interference in glutamate biosensors that utilize GmOx as the biorecognition molecule is critical. Strategies for the elimination of this interference by other electroactive species (which are commonly found in the sample matrix) include coating the biosensor with permselective nonconductive or conductive polymers such as Nafion, cellulose acetate, o-polyphenylenediamine (PPD), polypolyaniline, polythiophene, or polypyrrole [9, 48, 49]. The idea behind the addition of a permselective membrane is that the small pores within the membrane or film will only allow certain small molecules to pass through therefore minimizing interference by other larger electroactive species (see Figure 2). Also, positively charged groups such as those found in sulfonated tetrafluoroethylene copolymer (Nafion) will prevent or minimize the diffusion of anionic sample components, such as ascorbic acid, across the membrane and onto the electrode surface where the redox reactions are detected. Furthermore, though membranes and composites incorporating permselective conducting polymers such as polypyrrole (PPY) have other desirable properties (such as high conductivity and being redox active), it should be noted that coating the biosensor transducer with these polymeric films can lead to longer response times and lower signals due to the added diffusion barrier for both the substrate for the enzyme and the redox active species produced in the enzyme-catalyzed reaction. Co-immobilization of peroxidase with a redox polymer [50], immobilization of ascorbate oxidase [9], and self-referencing [51] have also been utilized as strategies to minimize interference from other electroactive species. On the other hand, performing electrochemical peroxidation, a process that utilizes sacrificial electrodes and stoichiometrically balanced applications of hydrogen peroxide to efficiently destroy interfering species in the aqueous phase, poses a risk of also oxidizing the analyte of interest.
\nElimination of interference in glutamate biosensors based on GmOx enzyme.
Many glutamate biosensors based on the GmOx-catalyzed reaction also include redox mediators or a second enzyme that reacts with H2O2 and are often referred to as second-generation biosensors. By incorporating a redox mediator, it is possible to lower the potential required for the H2O2 oxidation, thereby further limiting interference in complex biological samples by other species present, such as ascorbic acid or uric acid, which may also be redox active at the higher detection potential. Commonly used redox mediators in enzymatic biosensors include ferrocyanide [12], ferrocene and ferrocene derivatives [52–54], osmium complexes [55], quinine derivatives, and hexacyanoferrates, such as Prussian blue [45, 56, 57] and Ruthenium purple [58].
\nPrussian blue is the oldest coordination compound and was serendipitously discovered by Diesbach, an artist in 1704. It possesses excellent electrochemical characteristics and exceptional catalytic properties, making it a popular redox mediator. It is often referred to as the “artificial peroxidase” and has been well characterized and incorporated into various high-performing enzyme-based biosensors due to its excellent electrocatalysis toward the reduction of enzyme-generated H2O2 [45, 57, 59, 60]. Prussian blue is electrochemically reduced to form Prussian white (PW) which can then catalyze the reduction of H2O2 at low potentials of 0 V versus Ag/AgCl reference electrode [59].
\nAnother commonly used enzyme utilized in glutamate biosensors is L-glutamate dehydrogenase (GLDH) [49, 61–67]. GLDH (EC 1.4.1.2) catalyzes the deamination of amino acids, specifically the oxidative deamination of L-glutamate to 2-oxoglutarate (i.e., α-ketoglutarate) in the presence of nicotinamide adenine dinucleotide (NAD+) which serves as an oxidized cofactor for the enzyme. In mammals, this reversible enzyme-catalyzed reaction strongly favors the formation of ammonium (NH4+) and 2-oxoglutarate.
\nGlutamate biosensors have also been fabricated wherein both GLDH and GmOx are co-immobilized on the transducer surface [68]. Basu et al. co-immobilized both enzymes on a polycarbonate membrane by cross-linking procedures, involving glutaraldehyde, in the presence of a bovine serum albumin (BSA) spacer molecule in order to develop a biosensor for quantification of MSG in food. The MSG biosensor utilized substrate recycling which resulted in the amplification of the transducer response, thereby increasing the sensitivity [68].
\nOn the other hand, rather than directly detecting the glutamate levels, Meng et al. quantified glutamate by the detection of the anodic current of enzymatically generated NADH [67]. The electron transfer kinetics of the oxidation of NADH is sluggish and the direct oxidation of NADH at bare electrodes requires a high overpotential (0.7–1.0 V), where many interferences can occur. Also, bare electrodes are more likely to be affected by fouling which is caused by the adsorption of oxidation products onto their surfaces. The authors overcame these challenges by preparing biocompatible biosensors utilizing thionine (Th) and single-walled carbon nanotubes (SWCNT) nanocomposite to catalyze the electrochemical oxidation of NADH at an anodic potential of less than 0.19 V versus a standard hydrogen electrode (SHE) [67].
\nAzmi et al. also developed a spectrophotometric biosensor based on GLDH that was immobilized in chitosan for the determination of ammonium in water samples [69]. Ammonium, which is known to be toxic even at low concentrations to various organisms, is widely used in the farming, chemical, and automotive industries. In addition, ammonium is used as a parameter in the assessment of drinking and industrial water quality. The authors immobilized GLDH in a chitosan film (a natural biopolymer which can found in the exoskeleton of crustaceans) and measured ammonium in water based on NADH oxidation in the presence of α-ketoglutaric acid. The biosensor had a detection limit of 0.005 mM and a linear range of 0.005–0.5 mM NH4+ [69].
\nChitosan GLDH film is a popular enzyme immobilization matrix for biosensors due to certain advantageous properties of chitosan such as being nontoxic, biocompatible, biodegradable, an effective antibacterial, having high mechanical strength, good adhesion, and containing numerous amino and hydroxyl groups. Other glutamate sensing materials include polymer/enzyme composites [70, 71], nanoparticle iridium/carbon film [72], DNA-Cu(II)/polyamine membrane [73], nanoneedles [74], ferrocene functionalized SWCNT interdigitated construction film [52], Prussian blue film [45], and others.
\nThe topography of the prepared electrodes is usually studied using scanning electron microscope (SEM). SEM imaging allows for evaluation of uniformity and dispersity of the materials within the hybrid films. For example, it is possible to see if the composite materials are distributed uniformly over the entire surface of the electrode transducer. Agglomeration (i.e., sticking of particles to one another forming large groups) is sometimes observed in SEM images when the composites do not cover the surface uniformly. Porosity of the composite materials may also be assessed based on SEM images. The three-dimensional nanostructure morphology gives an indication whether the enzymes in the hybrid film will be accessible to the substrates. Modified electrodes are also much less likely to suffer from surface fouling, which results from adsorption of oxidation products to electrode surfaces when compared to bare electrodes.
\nAs stated earlier, the incorporation of conductive nanomaterials, such as CNTs, into the hybrid film on a modified electrode surface is gaining popularity. CNTs often significantly improve electron transfer and kinetics [75]. This is due to the electrical properties of CNTs which vary significantly and depend on the structural differences between CNTs, resulting in some CNTs being highly conductive like metals, while others act more like semiconductors [76]. The current carrying capacity of certain CNTs can be up to 1000 times greater than that of a copper wire [77]. Furthermore, CNTs provide a tremendous increase in the surface area onto which enzymes or other biomolecules may be immobilized, which ultimately improves quantification of the chemical reaction that is being analyzed. CNTs are also ideal for use in biosensors as they are nontoxic, nanometer sized, strong, and chemically stable [76].
\nThe electrochemical performance of the modified electrodes is often evaluated using redox reactions of benchmark species such as Fe(CN)63−/Fe(CN)64− redox pair in cyclic voltammetry (CV) studies. CV of this redox pair can be obtained using –300 mV initial potential, +800 mV switching potential, and –300 mV final potential with a carbon-based electrode against an Ag/AgCl reference electrode. The effect of changing scan rates (e.g., ranging from 30 to 120 mV/s) on electrochemical behavior is often also studied. Moreover, other neurotransmitters such as dopamine, which can directly undergo oxidation to dopamine-o-quinone at the electrode surface, may be detected in the living brain using voltammetry.
\nCharacterization of modified electrode nanomaterials such as CNTs may be carried out by UV/Vis spectroscopy or Fourier transform infra-red (FTIR) spectroscopy. For example, Meng et al. utilized UV/Vis spectroscopy from 200 –800 nm [67] in their investigations. To confirm that thionine (Th) had adsorbed onto the SWCNT surface, the authors observed the occurrence of a notable absorption peak at about 600 nm as well as a small shoulder peak at circa 560 nm. To determine whether GLDH, the biocatalytic molecule, had been successfully immobilized on the Th/SWCNT nanocomposite, a peak at about 275 nm, which is an absorption peak characteristic for proteins, was monitored using UV spectroscopy.
\nWhen preparing a new biocatalytic sensor, parameters such as origin and availability of the enzyme, its operational and storage stability as well as immobilization procedure should also be carefully considered [78]. In addition, the influence of experiment conditions such as pH, temperature, ionic strength, and stirring on the enzyme-catalyzed reaction can be minimized by optimizing and keeping these conditions constant throughout the biosensor use. For example, GLDH-based biocatalytic sensors have utilized pHs ranging from 7.0 to 9.0 during the detection step [12, 67, 79]. Also, GmOx-based biosensors appear to utilize pHs from 7.0 to 7.4 [40, 68]. Of note, the optimal pH of the immobilized enzyme may be slightly different from the free enzyme in buffered aqueous solution. Furthermore, the detection temperatures which the enzymes are exposed to may vary depending on the specific application of the L-glutamate biosensor. The concentration of coenzyme NAD+ will also need to be optimized for GLDH-based biosensors. Furthermore, the activity of enzymes such as GLDH (a hexameric enzyme from bovine liver) is affected by various cations and anions. Specifically, lanthanide (La3+) and europium (Eu3+) ions can enhance the activity of bovine GLDH, at least in solution [80]. Also, Meng et al. observed a decrease in catalytic currents generated by GLDH when Zn2+ was added to the solution with the inhibitory effect increasing with increasing concentrations of the zinc ions [67].
\nAmperometry is perhaps the most common electrochemical detection method used in quantitative analysis of an analyte once the biosensor has been characterized and optimized. It is a very popular electroanalytical detection method for quantitative analysis due to its simplicity and the low detection limits that can be achieved. In amperometry, the analyte concentration is determined by measurement of the signal—the current produced in a redox reaction as a function of time when a constant potential is applied to the electrodes. Amperometry results in current versus time plots where the current increases stepwise with each successive addition or formation of the redox active species. Amperometric signal response consisting of back-to-back steps in a “staircase” makes it relatively easy to identify the starting and final current for each analyte addition or the formation of redox active species. The response in amperometry is usually rapid and reaches a dynamic equilibrium which results in a steady-state current signal within seconds. The electron transfer of reactants during electrochemical oxidation is mainly determined by the conductivity of the working electrode material and the active functional groups on its surface. Three electrode systems with working, reference, and auxiliary electrodes are typically used in amperometry. In amperometry, the oxidation or reduction potential used for the detection step is characteristic of the analyte species, thus adding to selectivity of the method by eliminating interferences from other redox active species that may also be present in the sample. Also, the detection potential is stepped directly to the desired, optimum value, and current resulting from the redox reaction is detected by the transducer (the working electrode in the biosensor). Current generated by the reaction (i.e., the current passing through the electrochemical cell over time) is proportional to the concentration of the electroactive species in the sample. Sometimes, the charging current or background current (i.e., the current needed to apply the potential to the system) present at the beginning of each measurement requires some time in order to stabilize before quantitative measurements can be made using amperometry.
\nIn order to be considered as an alternative to any existing and well-established instrumental analysis methods, such as high-performance liquid chromatography (HPLC) or spectrophotometry for quantification of L-glutamate in clinical or food applications, the performance of new biosensor devices has to be tested in serum or food samples, respectively. Ideally, no sample pretreatment other than dilution should be required. Also, the use of low-cost and disposable devices such as screen printed carbon electrodes (SPCE) is advantageous in the analysis of biological fluids where contamination may be a problem. Hughes et al. described the development and optimization of a disposable screen-printed amperometric biosensor for glutamate based on GLDH [79]. The authors also developed a stable, reagentless amperometric glutamate biosensor by incorporating the GLDH biorecognition components using a layer-by-layer deposition involving chitosan and MWCNTs on SPCE [81]. The new reagentless biosensor was applied to the measurement of glutamate in beef stock cubes and serum samples.
\nWhen new biosensors are being developed and characterized, it is common to report their detection limit, sensitivity, specificity, accuracy, reproducibility (i.e., precision), response times, reusability including recovery times, long-term stability (i.e., shelf-life), lifetime, and performance in real samples such as serum or soup [13, 82]. For electrochemical biosensors, reporting the electrode material and type as well as the transducer’s surface area and detection potential versus a reference electrode are also important experimental details. Table 1 summarizes many of these analytical figures of merit for selected glutamate biosensors allowing comparisons to be made between various enzyme-based biosensors.
\nElectrode | \nConfiguration | \nApplication | \nEnzyme | \nOptimal pH | \nApplied potential | \nResponse time | \nLOD | \nLinear range | \nRef. |
---|---|---|---|---|---|---|---|---|---|
Screen-printed graphite | \nCHIT/MB/SPCE | \nIn vitro | \nGLDH | \n7 | \n0.1 V vs. Ag/AgCl | \n2 s | \n1.5 μM | \n12.5–150 μM | \nHughes [79] |
Platinum microelectrode array | \nSilicon wafer/polypyrrole/Nafion | \nIn vivo | \nGmOx | \n7.4 | \n0.7 V vs. Ag/AgCl | \n<1 s | \n<1 μM | \n10–100 μM | \nWassum [40] |
Oxygen electrode | \nGlutaraldehyde/bovine serum albumin | \nIn vitro (food) | \nGmOx and GLDH | \n7 | \n\n | 120 d | \n0.02 mg/L | \n0.02–1.2 mg/L | \nBasu [68] |
Glassy carbon electrode | \nSWCNT/thionine | \nNADH | \nGLDH | \n8.3 | \n0.19 V vs. Ag/AgCl | \n5 s | \n0.1 μM | \n0.5–400 μM | \nMeng [67] |
Au planar nanocomposite | \nCHIT/MWCNT/ferricyanide | \nIn vitro (food) | \nGLDH and diaphorase | \n9 | \n\n | <60 s | \n5.4 μM | \n10–3495 μM | \nMonošík [12] |
SWCNT bundles | \nFerrocene/SWCNT | \nIn vitro | \n\n | \n | 0.2 V vs. Ag/AgCl | \n<300 s | \n1 μM | \n1–7 μM | \nHuang [52] |
Pt. microelectrode | \nCHIT/ceria & titania NPs | \nIn vitro (hypoxic brain tissue) | \nGmOx | \n\n | 0.6 V vs. Ag/AgCl | \n2–5 s | \n0.594/0.493 μM | \n\n | Özel [44] |
Graphite | \nPB/graphite | \nProof of concept | \nGmOx | \n6.5 | \n−0.05 V vs. Ag/AgCl | \n3 s | \n0.01 μM | \n0.01–0.1 mM | \nLiu [45] |
Au electrode | \ncMWCNT/AuNP/chitosan | \nIn vitro (sera) | \nGmOx | \n7.5 | \n0.135 V vs. Ag/AgCl | \n2 s | \n1.6 μM | \n5–500 μM | \nBatra [83] |
Vertically aligned CNT nanoelectrode array | \nVACNT-NEA | \nProof of concept | \nGLDH | \n\n | 0 V vs. Ag/AgCl | \n\n | 57 nM | \n0.1–300 μM | \nGholizadeh [75] |
Pt electrode | \nChitosan-glutaric dialdehyde gels | \nProof of concept | \nGmOx | \n\n | 0.6 V vs. Ag/AgCl | \n2 s | \n0.10 μM | \n0.10–500 μM | \nZhang [37] |
Pt electrode | \nChitosan | \nIn vitro (food) | \nGmOx | \n\n | 0.4 V vs. Ag/AgCl | \n2 s | \n0.10 μM | \n1–10 μM | \nZhang [38] |
Patterned Pt thin film electrodes | \nGlutaraldehyde/SiO2/PCB | \nCell culture fermentation | \nGmOx | \n\n | 0.6 V vs. Ag/AgCl | \n\n | 0.0002 μM | \n0.00022500 μM | \nBäcker [41] |
SAM on smart biodevice | \nECD/thioglycolic acid | \nProof of concept | \nGmOx | \n\n | NA | \n\n | 0.089 μM | \n0.1–10,000 μM | \nRahman [42] |
Pt electrode | \nGlutaraldehyde | \nIn vitro (brain tissue) uptake | \nGmOx | \n7.4 | \n0.6 V vs. Ag/AgCl | \n15–20 s | \n0.5 μM | \n2–800 μM | \nSoldatkin [84] |
Screen-printed graphite | \nMWCNT-CHIT-MB/CHIT-NAD+-MB/MWCNT-CHIT-MB/MB-SPCE | \nIn vitro proof of concept | \nGLDH | \n7 | \n0.1 V vs. Ag/AgCl | \n<60 s | \n3 μM | \n7.5–105 μM | \nHughes [81] |
A summary of previously published electrochemical glutamate biosensors, their electrode material, surface modification configuration, analytical figures of merit, and authors.
Enzyme-based electrochemical glutamate biosensors have tremendous potential for manufacturing of cost-efficient, easy-to-use, fast, and portable alternatives for a wide range of applications from medical/clinical testing or neurological studies for diagnostics involving this important neurotransmitter in vivo or in vitro to environmental monitoring, process monitoring, and food-sensing applications [13, 82]. Electrochemical detection schemes are also typically very simple, sensitive, independent of sample volume, and well suited for monitoring glutamate from nM to μM in real samples such as biological fluids or processed foods. The incorporation of the oxidase or dehydrogenase enzyme as the biorecognition component on the electrochemical transducer provides additional selectivity and in some detection schemes even significant signal enhancement. Many glutamate biosensors are label free while others incorporate various redox active mediator molecules into a composite material on the biosensor surface. An increasing number of enzyme-based glutamate biosensors also utilize the advantageous properties of nanomaterials such as biocompatible CNTs or metal nanoparticles. It is likely to take years or a decade before many of these biosensors described in this chapter go from proof-of-concept stage to mass production of inexpensive, small, and reliable devices capable of competing with existing instrument-intensive laboratory methods for glutamate quantification such as spectroscopy or chromatography. However, with continuous developments in molecular biology, nanofabrication methods, immobilization methods of biomolecules, and multiplexing capabilities, the production of sensitive, selective, fast, and easy-to-use biosensors for quantification of glutamate and other neurotransmitters will be feasible in the not too distant future.
\nThe phosphosilicate apatites containing a coupled substitution of the divalent cation by a trivalent lanthanide or a tetravalent actinide ion and the trivalent groupment PO4 by a tetravalent SiO4 groupment in the general formula Me(XO4)6Y2 (Me: divalent cation; XO4: anionic groupment and Y: monovalent anion) allow to obtain materials called britholite [1, 2, 3, 4]. Such materials were found in the natural nuclear reactors Alko of Gabon which demonstrated that they are storing some radionuclides such as uranium U, thorium Th, plutonium Pu and minor actinides like neptinium Np, americium Am and curium Cm [5, 6, 7, 8]. Moreover, silicate based apatite samples were found to contain up to 50 wt% of lanthanides (La, Ce, Nd) and actinides (U, Th) in Ouzzal site of Algeria [9]. Hence, britholites were considered as natural nuclear waste disposal and allowing the confinement of radionuclides and some fission byproducts produced by the nuclear industry [10, 11, 12]. In fact, many studies indicated that britholites are able to confine radionuclides with continuous irradiation for millions of years with conserved structure and thermal and chemical stability [13, 14]. Indeed, due to the stability and flexibility of their structure, apatites offer many possibilities for substitutions. Moreover, britholite materials favored many cationic and anionic substitutions in their crystallographic structure. These later might be in a total or limited range [15, 16, 17]. Therefore, these substitutions are governed by the ionic sizes, the valence, the electronegativity and the polarizability [18]. In this context, several processes have been developed for the preparation of these materials containing various elements such as actinides and lanthanides via solid state reaction or mechanical synthesis [19, 20, 21, 22, 23, 24, 25, 26, 27].
On the other hand, many investigations have revealed that britholites might be a good ionic conductor for their use in fuel cells. The conductivity was proved as a thermal process at intermediate temperature range 400–900°C [28, 29, 30, 31, 32]. Therefore, the electrical properties allow using the materials as a solid electrolyte in solid oxide fuel cells (SOFCs) [33, 34].
Like-apatite, phosphosilicate apatites have a hexagonal structure and a space group P63/m [15, 35]. Their framework is built on the sixth XO4 groups and the Me is divided between two crystallographic sites: four are located in the site Me(1), coordination 9, and six other are located in the site Me(2), coordination 7. Hence, in order to highlight the capacity of these materials to store non radioactive elements similar to radionuclides as well as their potentialities as ionic conductors, the sintered materials series Sr8La2−xNdx(PO4)4(SiO4)2F2 with 0 ≤ x ≤ 2 were investigated.
A solid state method was adopted to prepare strontium fluorobritholites compounds Sr8La2−xNdx(PO4)4(SiO4)2F2 with 0 ≤ x ≤ 2 [36]. The starting reagents: strontium fluoride SrF2 (99.99%. Merck)), strontium carbonate SrCO3 (≥99.00% Fluka), silica SiO2 (Prolabo), lanthanum and neodymium oxide (La2O3·Nd2O3) (99.99% Merck) and strontium diphosphate (Sr2P2O7) were used. The reaction equation (1) is the following:
Sr2P2O7 was synthesized by the following reaction at 900°C:
SrCO3 (>96% Riedel de Haen), Gd2O3 (>99.5% Prolabo), Nd2O3 (>99.5% Prolabo) SiO2 (>99.5% Alfa), SrF2 (>99.5% Prolabo) and (NH4)2HPO4 (>99% Acros Organics) were used as raw materials. For each composition the molar ratio (Sr + La + Nd)/(P + Si) and the obtained quantity of each composition should be respectively 1.67 and 1.5 × 10−3 moles. Before synthesis, each quantity of lanthanum and neodymium oxides given in Table 1 was furnaced at 1000°C for 12 h to avoid the formation of Ln-hydroxide. Then, the solid mixture was milled and homogenized in an agate mortar for about 30 min, and then cold pressed under 100 MPa into pellets (30 and 3 mm). During sintering, the pellets were sintered in the temperature range 1250–1450°C in a carbolyte type furnace with controlled argon atmosphere. The temperature varied with 50°C for each value of x. The sintering cycle is shown in Figure 1. The heating and cooling rate was of 10°C min−1. In the following sections, the samples will be named SrLa2−xNdxF where x is the substituted Nd rate.
Thermal cycle used for strontium fluorobritholite sintering.
Reactants | SrLa2F | SrLa1.5Nd0.5F | SrLa1Nd1F | SrLa0.5Nd1.5F | SrNd2F |
---|---|---|---|---|---|
La2O3 | 0.4887 | 0.3665 | 0.2443 | 0.1221 | — |
Nd2O3 | — | 0.1261 | 0.2523 | 0.3785 | 0.5047 |
Masses in grams of lanthanum and neodymium oxides used in the synthesis of
A PANalytical X’pert Pro diffractometer with a KαCu anode (λ = 1.54 Å) operating 40 kV and 40 mA was the apparatus used for the XRD patterns recording. The scans range was between 10 and 70° (2θ) with a step size of 0.02°. The crystallite size of the powder Dhkl was calculated using the (300) and (002) reflections following Debye Sheerer equation [37]:
Needs to remember that λ is the X-ray wavelength of the monochromatic X-ray beam. For the apatitic crystallites K is a constant equal to 0.9. β1/2 is the full width at half maximum of the selected reflection and θ is the Bragg’s diffraction angle.
The Fourier transformed infrared (FTIR)-attenuated total reflection (ATR) spectra were performed at room temperature on a Perkin Elmer spectrometer in the spectral range 4000–400 cm−1.
The chemical analysis of Sr., P, Si, La and Nd ions in the synthesized samples was determined via an inductively coupled plasma atomic emission spectroscopy (ICP-AES) (JY-Horiba Ultima-C spectrometer). The samples were thus previously mixed with 99.9% lithium metaborate, fused at 1000°C for 25 min and dissolved in HCl (0.6 M). The fluoride content in the synthesized samples was measured by a specific ion-selective electrode.
The complex impedance measurements were performed on pellets sintered at between 1250 and 1450°C for 24 h. Their densities varied from 72 to 83% of the theoretical density as a function the sintering temperature. The two faces of the pellets were coated with a silver paint and then two platinum wires electrodes linked them to a Hewlett-Packard 4192-A impedance analyzer. The measurements were recorded with the temperatures variation from 450 to 780°C and frequencies from 10 Hz to 13 MHz.
The samples’ quantitative chemical analyses are shown in Table 2. As observed there is a satisfactory agreement between the elements amount determined from the analyses and those introduced in the starting. As a consequence, the experimental formula was close to the theoretical ones. The
Samples | Sr | La | Nd | P | Si | F | Molar ratio |
---|---|---|---|---|---|---|---|
SrLa2F | 7.96 | 1.97 | — | 3.98 | 1.97 | 1.99 | 1.668 |
SrLa1.5Nd0.5F | 7.96 | 1.47 | 0.47 | 3.98 | 1.96 | 1.98 | 1.666 |
SrLa1Nd1F | 7.97 | 0.98 | 0.97 | 3.99 | 1.96 | 1.96 | 1.667 |
SrLa0.5Nd1.5F | 7.99 | 0.47 | 1.48 | 3.99 | 1.98 | 1.97 | 1.664 |
SrNd2F | 7.98 | — | 1.97 | 3.98 | 1.98 | 1.97 | 1.669 |
Number of atoms per unit cell of Sr8La2−xNdx(PO4)4(SiO4)2F2 (0 ≤ x ≤ 2).
Figure 2 showed the XRD patterns of all compositions. It is evident that the samples were single apatite phase. By comparaison to the JCPDS 17-0609 file data for the strontium fluorapatite, the samples are characteristic of the hexagonal symmetry and the P63/m space group. No additional diffraction lines relative to supplementary phases were detected in any of the patterns. However, the presence of very small quantities of impurities was not excluded. The XRD patterns of the Figure 3 indicated that when the substitution level increased, the peaks slightly shift towards the high 2θ angles indicating a contraction of the unit cell. This contraction, which agrees with the Nd3+ radius (VI
DRX spectra of strontium fluorbritholites Sr8La2Ndx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
Radiation (300) of fluorbritholites Sr8La2−xNdx(PO4)4(SiO4)2F2 (0 ≤ x ≤ 2).
As shown in Figure 4 and Table 3, the cristallographic parameters calculated using the Fullprof program without any structural refinement of the all compositions depended on the substitution level. In fact, if Nd content rose,
Lattice parameters as a function neodymium level in the Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
Sample | a (Å) | c (Å) | V (Å3) | D300 (Å) | D300 (Å) |
---|---|---|---|---|---|
SrLa2F | 9.735(2) | 7.281(2) | 597.55(2) | 304(3) | 387(4) |
SrLa1.5Nd0.5F | 9.730(3) | 7.278(3) | 596.69(2) | 278(2) | 362(3) |
SrLa1Nd1F | 9.725(2) | 7.271(2) | 595.43(2) | 254(3) | 347(2) |
SrLa0.5Nd1.5F | 9.720(3) | 7.267(2) | 594.57(3) | 237(3) | 326(3) |
SrNd2F | 9.717(3) | 7.263(2) | 593.87(3) | 223(4) | 307(4) |
Crystallographic parameters of strontium fluorbritholites Sr8La2−xNdx(PO4)4(SiO4)2F2 (0 ≤ x ≤ 2).
indicates the existence of a continuous solid solution in the explored substitution domain.
The rational parameters that govern the site occupation are the nature, the electronegativities, the valences and the polarizabilities of the ions. The bibliography studies’ results indicated that like those observed in natural phosphosilicate apatites, the substituted cations in the apatite structure had preferential occupation for Me(2) sites [42, 43, 44, 45]. Thus, it could be concluded that La3+ and Nd3+ ions substituting Sr2+ with 0 ≤ x ≤ 2 in our studied samples were subsequently preferentially localized in Me(2) sites.
The FTIR spectra of the samples were given in Figure 5. The identification of all the bands was done by comparison with un- and substituted strontium fluorapatite the previously reported in the literature [40, 41]. The characteristic absorption bands of SiO4 and PO4 were observed [41].
Infrared spectra of Sr8La2−xNdx(PO4)4(SiO4)2F2 (0 ≤ x ≤ 2).
The PO4characteristic bands observed at 1072–1024 cm −1 coincide with to the asymmetric stretching mode (
In the Figure 6 are represented the 31P NMR-MAS spectra. A single isotropic signal was observed for all the spectra. It indicated also that a unique crystallographic site for the PO4 tetrahedron in the apatite structure was present. However a slight chemical shift towards the lower values was observed as well as a broadening of the peaks was attributed to the Nd substitution. This fact was related to a disorder induced in the apatite network caused by the substitution of La by Nd. This was previously seen with doped with rare earth apatite’s [46, 47, 48].
31P NMR-MAS spectra of fluorbritholites Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
Materials densification optimization has been performed by sintering the synthesized samples in the temperatures range 1250–1500°C with a fixed holding time of 6 h. Relative density
where the theoretical density ρthe was calculated using the equation:
(Z: number of molecules/unit cell, M: molecular weight Na: Avogadro number and V: volume of the unit cell) and experimental density determined from the mass and the dimension of sintered pellets by means of the equation
Figure 7 shows that relative density of the sintered samples strictly depends on sintering temperatures as well as on Nd content. An irregular trend was noted and the highest relative density 89% was obtained with x = 2 Nd content when sintered only at 1250°C. The remaining samples presents lower than densifications ratios obtained at higher temperatures. From these data, it can be deduced that the grains morphology and size modification strongly depends on Nd content and sintering temperature. The Nd doping should improve the materials densification by reducing the porosity. This was confirmed by the percentage porosity of the higher densified samples calculated by the following equation:
Relative density versus sintering temperature of Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
As plotted on Figure 8, the porosity of the samples decreased as Nd content increased. This result muched the evolution of the relative density suggested to increase when crystallite size is reduced (i.e. grain size). This should promotes the materials densifications by eliminating the intergranular porosity.
Porosity versus Nd content of maximum densified samples.
The microstructure of the samples given on Figure 9 is closely coherent with the densification rates as well as porosity. Indeed, the micrographs show a progressive removal of the porosity when the Nd rate rises. Thus with x = 0 the microstructure is of intergranular aspect revealing the presence of abundant porosity. With x = 0.5, although some pores persist on the surface the porosity was reduced,. When x = 1 the open porosity has almost disappeared and only the closed porosity remains, reflecting the 89% densification.
Micrographs of sintered samples Sr8La2−xNdx(PO4)4(SiO4)2F2 (a) x = 0.0; (b) x = 0.5; (c) x = 1.0.
The ionic conductivity of the samples was determined between 400 and 800°C with a step of 20°C by complex impedance plots. Thus, for each sample, 20 complex impedance plots (plane, Z″ vs Z′) were plotted. The intercept of the semicircular arcs with the real axis allow obtaining the bulk resistance R. The ionic conductivity of the sintered samples was calculated from the equation:
The thickness and the area of the sample were e and S, respectively. Figure 10 reprinted the ionic conductivity σ versus the neodymium substitution. The first deduction is that σ depends on this substitution and particularly at higher temperatures. The curves obtained at 604 (877 K) and 482°C (755 K) indcated that the measured conductivity was about 4.4 × 10−7 S cm−1. By contrary with the increase of Nd content, σ rose up to 1.73 × 10−6 S cm−1 at 779°C (1052 K). Hence, the electric conductivity of the samples depend onthe Nd substituted level.
Ionic conductivity versus neodymium content.
The total activation energy of the samples was obtained from the Arrhenius equation:
The parameters to define are the pre-exponential factor A, activation energy Ea, Boltzmann constant k and absolute temperature T, respectively. Figure 11 shows an Arrhenius-type plot indicating that the electrical conduction of the materials is activated by heating. The σ values were slightly different from those found in the literature [49, 50]. The difference might have resulted from the preparation and sintering methods reflected by the difference in densification ratios (range 72–83%). The slope in the Arrhenius plots versus temperatures gives the activation energy. This later parameter increased when Nd level rose reaching a maximum of 1.1 eV when x = 1 then decreased to 0.91 eV (Table 4). Moreover a slight break in slope for x ≥ 1 was detected in the Arrhenius plots. This was related to the Sr/Nd▬F bond likely to the work of Njema and al [49]. In fact, in Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2) samples, the mobility of F− along the
Plots of LnσT versus 1000/T of fluorbritholites Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
x | 0 | 0.5 | 1 | 1.5 | 2 |
---|---|---|---|---|---|
Ea (eV) | 0.87 | 0.95 | 1.1 | 1.03 | 0.91 |
Activation energy of Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2).
Strontium fluorbritholites Sr8La2−xNdx(PO4)4(SiO4)2F2 with (0 ≤ x ≤ 2) were successfully prepared by reaction in the solid state. Characterization by several techniques revealed that all the powders were composed of a single apatite phase. The lattice parameters
The authors declare no conflict of interest.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). 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