Application of (mixed) metal oxides-based nanocomposites for biosensors

CHAPTER

Application of (mixed) metal oxides-based nanocomposites for biosensors

11

Ali Salehabadi1 and Morteza Enhessari2 1

Environmental Technology Division, School of Industrial Technology, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia 2Department of Chemistry, Naragh Branch, Islamic Azad University, Naragh, I. R. Iran

11.1 INTRODUCTION Smart materials are very difficult to define in explicitly. Inspiration for the search for new solutions of advanced material-based sensors is common throughout nature (Cichosz et al., 2018). Changing the daily optical-based glucose check for diabetes patients to electrochemical sensors, with millions of sensors sold annually, the method of production, cost, and easiness of use would have a huge impact on the market. Interdigitated transducers in biosensor technology are important to fulfill the requirements of the diverse criteria in the market, such as their combination with biomolecules to develop high-selectivity sensors. Their electrical excitation makes it easy to develop biosensors to perform automatically measurement and fluidics manipulation. Using advanced materials (plastic, nanomaterials, etc.) as a substrate in sensors technology helps to further reduce the costs of the sensors. Quality, cost, suitability, and automatization are important factors which make sensors excellent candidates as potential equipment for diverse applications. Biosensors were first fabricated in the 1960s. These were macroscopic membranes consisting of transducers and gluing wires. For smallscale production, as is required for clinical analyzers, conventional biosensors are not useful. In this chapter present-day applications of biosensors to clinical chemistry are reviewed, including basic and applied research, commercial applications, and fabrication techniques. Recognition elements include enzymes as biocatalytic recognition elements and immunoagents and DNA segments as affinity ligand recognition elements, coupled to electrochemical and optical modes of transduction. The future will include biosensors based on synthetic recognition elements

Materials for Biomedical Engineering: Inorganic Micro and Nanostructures. DOI: https://doi.org/10.1016/B978-0-08-102814-8.00013-5 © 2019 Elsevier Inc. All rights reserved.

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to allow broad applicability to different classes of analytes and modes of transduction extending the lower limits of sensitivity. Microfabrication will permit biosensors to be constructed as arrays and incorporated into lab-on-a-chip devices. The learning objectives of this chapter are: • • • • • •

To understand the various (bio)sensor devices and the principle of measurement; To be able to know the mechanism of detection of various analytes; To illustrate the criteria of the sensing mechanism; To find the materials (metal oxides, mixed metal oxides, nanocomposites, etc.) appropriate for sensing devices; To select the best sensing materials (composition) based on performance characteristics; To understand the range, linear range and detection limits, response times, recovery times, and lifetimes.

11.1.1 SEMICONDUCTING (NANO)MATERIALS Semiconducting materials are widely used in miscellaneous applications, such as energy-storing devices (Salehabadi et al., 2018a,b,c,d), sensing devices (Enhessari and Salehabadi, 2016; Hulanicki et al., 1991; Korotcenkov et al., 2009), etc. They consist mostly of metal oxides, some metals, or polymers. Titanium oxides, zirconia, silicon dioxide, etc. are semiconductors used as effective immobilization scaffolds in biosensor technology. These nanomaterials are able to promote direct electron transfer of the entrapped biomolecules and maintain the long-term bioactivity. Various synthesized nanostructural forms of semiconductors, including nanoparticles (NPs), nanotubes (NTs), nanowires, nanorods, nanobelts, nanosheets, nanotips, quantum dots (QDs), hollow spheres, etc. have been synthesized. Sol
gel synthesis, hydrothermal or solvothermal growth, physical or chemical vapor deposition, low-temperature aqueous growth, chemical bath deposition, or electrochemical depositions are methods used for nanoscale material formation. Electronic materials include insulators, semiconductors, conductors, and superconductors. A solid insulator is a substance with a very low electrical conductivity and there is a considerable energy gap before an empty orbital becomes available (Fig. 11.1A). A semiconductor is a substance which has electrical conductivity that increases with increasing temperature, and at room temperature, the conductivities are typically intermediate between conductors and insulators (Enhessari, 2013; Khanahmadzadeh et al., 2015; Zare et al., 2009; Enhessari et al., 2010, 2012a,b, 2013, 2016a,b; Nouri et al., 2016). Semiconductors are classified as intrinsic or extrinsic semiconductors; the former band gap is very small (Fig. 11.1B), therefore, the energy of thermal motion results in jumping of some electrons from the valence band into the empty upper

11.1 Introduction

band, and the former is a semiconductor in the presence of impurities; p-type doping atoms remove electrons from the valence band and n-type doped atoms supply electrons to the conduction band (Fig. 11.1C). A conductor is a substance with an electric conductivity that decreases with increasing temperature, and has zero band gaps (Fig. 11.1D). A superconductor (classified into three types) is a class of materials with zero electrical resistance. The origin of this class of materials is related to electron
phonon coupling and the resultant pairing of conduction electrons (Askeland and Phule, 2006). A metallic conductor, a semiconductor, and a superconductor can be distinguished based on the temperature dependence of the electrical conductivity. This variation can be observed in Fig. 11.2.

FIGURE 11.1 The structure of a typical (A) insulator, (B) intrinsic semiconductor, (C) extrinsic semiconductor, (D) conductor. Adapted from Atkins, P., Overton, T., Rourke, J., Weller, M., Armstrong, F., 2006. Inorganic Chemistry, fourth ed. Oxford, USA.

FIGURE 11.2 Variation of the electrical conductivity of a substance with temperature (Atkins et al., 2006).

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Semiconductors have many applications because their properties can be easily modified by the addition of impurities; these applications include computer chips, diodes, transistors, lasers, and LEDs. Furthermore, the electrical conductivities of semiconductors can be controlled by exposure to an electric field, light, pressure, and heat, hence, they can be used in many sensor devices (Atkins et al., 2006). An electron sea can be formed when elements lose electrons. This governs the chemical properties of the metallic elements and accounts for metallic bonding. The terms “metals” and “nonmetals” elements are directed toward the ionization energies. This definition can be observed in from 13 to 16 groups. The elements in these groups start with nonmetals and end with metals. There are allotropic variations in the sense that some elements exist as metals and nonmetals, like group 15; N and P are nonmetals, As is a nonmetal, a metalloid, and a metallic allotrope, and Sb and Bi are metals. Among all the elements, transition metalbased nanocomposites have been most widely investigated. Moreover, metalloids like Si are also used in biosensor technology. The most well known elements reported in biosensing technology are cobalt, copper, iron, manganese, nickel, osmium, titanium, zinc, and zirconium. Materials with high ionic conductivity have important applications in sensors and fuel cells of various kinds. Metal-oxide semiconductors can be used as sensors for monitoring environmental pollution, fire, and vehicle emissions. The fundamental sensing mechanism relies upon the change in electrical conductivity due to the interaction between the gases in the environment and oxygen in the grain boundaries (Hunter et al., 2006). Metal oxides nanostructures (MONs) have received enormous attention for their promising sensing applications. Unique features of MONs, such as controllable size, functional biocompatibility, biosafety, chemical stability, and catalytic properties make them suitable for fabrication of (bio)sensors. Moreover, the physicochemical properties of MONs, such as enhanced electron-transfer kinetics and strong adsorption capability, and the possibility of chemical modification of their surfaces, make them more advantageous than other materials in order to enhance chemical and biological sensor performances. MONs are good candidates for optical emitters, electronic conductors, catalysts, carriers for amplified detection signals, and biosensing interfaces. The term “nanocomposite” has been widely distributed in biosensor technology. Almost all elements can play a role in biosensor technology and its respective nanocomposites. The amalgamation of conducting and semiconducting NPs like gold, silver, platinum, carbon nanotubes, graphene, etc. has been reported. The optical, electrical, and magnetic properties of MONs can be enhanced as an amalgamate with NPs. As a result, the final materials, called “nanocomposites,” have improved selectivity, stability, and sensing performances. The selection, design, and application of MONs have an important role in the generation of new sensing devices with novel functionality, enhanced signal amplification, and coding strategies.

11.1 Introduction

The elemental distribution of transition metals is mostly focused on these elements. In transition metals, iron (Fe) have stimulated a great deal of interest. For example, facile electrochemical biosensors for hydrogen peroxide using efficient catalysis of hemoglobin on the porous Pd@Fe3O4-MWCNT nanocomposites are reported (Baghayeri and Veisi, 2015). A hydrogen peroxide biosensor based on electromagnetic poly(p-phenylenediamine) @ Fe3O4 nanocomposite was fabricated by Baghayeri and his coworkers (Baghayeri et al., 2010). They found that a pair of well-defined redox peaks of Hb at the Hb
PpPDA@Fe3O4 modified glassy carbon electrode with reproducibility and high sensitivity to H2O2. PFu@Fe3O4 conductive nanocomposites have been examined as a host of hemoglobin (Baghayeri et al., 2014). In this system, the Hb immobilizes on PFu@Fe3O4 nanocomposites. Bionanocomposite of antihuman IgG/COOH
multiwalled carbon nanotubes/Fe3O4 was made for electrochemical immunoassay of human tetanus IgG (hIgG) as a model antigen (Zarei et al., 2012). Fe3O4/polydopamine/Au nanocomposites were fabricated for detection of human leptin in serum (He et al., 2015). This immunosensor exhibited high sensitivity, good specificity, and a wide linear range for human leptin sensing from 1.0 to 8.0 3 102 pg/mL. Fig. 11.3 shows the fabrication process of this immunosensor. Xanthine molecules can serve as an indicator of meat spoilage, determined using a REGO/Fe3O4 bionanocomposite sensor (Dervisevic et al., 2015). Here, the current response of the linear range was in the range of 2
36 μM with a

FIGURE 11.3 Fabrication of a biosensor for human leptin (He et al., 2015).

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sensitivity of 0.17 μA/M, a response time of B3 seconds, and a detection limit of 0.17 μM. The basic principle of the electrochemical reaction with amperometric xanthine biosensors are as illustrated in Eq. (11.1): Xanthine+O 2 +H 2 O

Uric acid+ H 2 O 2 2H + +O2 + 2e–

(11.1)

Working electrode

Immobilization of a lipase (Candida rugosa) on functionalized Fe3O4@SiO2, which covalently bound to the nanoparticles and was entrapped in the membrane, was reported by Aghababaie et al. (2016). They reported that the relative activity and loading capacity of ENCM is higher than lipase immobilized on a UF membrane. Fe3O4/g-C3N4/HKUST-1 composites as a platform for ochratoxin A (OTA) are another class of biosensor recently fabricated (Hu et al., 2007). This is a fluorescence biosensor with strong adsorption capacity for dye-labeled aptamer and capable of completely quenching the fluorescence of the dye. The fluorescence intensity of the biosensor has a linear relationship with the OTA concentration. A stable colloid solution of core
shell Fe3O4/polyaniline nanoparticles in chitosan (CHT)/H2PtCl6 over the surface of a carbon paste electrode (CPE) was synthesized for determination of xanthine (Sadeghi et al., 2014). Copper is the second most applicable element in biosensor technology. Copper oxide nanowires/single-walled carbon nanotubes (Chen et al., 2016), CuO/grapheme (Hsu et al., 2012), Cu/Cu2O nanocrystals and reduced graphene oxide (Wang et al., 2015), copper oxide/polypyrrole/reduced graphene oxide (Moozarm et al., 2015), Cu2O/MWCNTs (Zhang et al., 2009a) nanocomposites are widely used in clinical and biological sensing applications. Zinc (Zn) and zirconium (Zr) are also used in the fabrication of biosensors. Graphene/zinc oxide nanocomposites were synthesized via liquid-phase exfoliation and solvothermal growth for DNA sensing (Shin et al., 2015). The results indicate that the graphene/zinc oxide nanocomposite can enhance the sensitivity and efficiency of electrochemical DNA biosensors. Fig. 11.4 shows a schematic fabrication of grapheme/ZnO biosensor-based nanocomposites. ZnO/CH3NH3PbI3/NCQD nanocomposites were used for detection of fibroblast-like synoviocyte (FLS) cells (Pang et al., 2015). A wide linear range from 1.0 3 104 to 10 cells/mL and a low detection limit of 2 cells/mL was obtained from current sensors. The authors proposed that this kind of bisosensor would provide an enhanced strategy for FLS cell detection. Indian researcher Mogha and his coworkers (Kumar et al., 2016) reported a biosensor made from acetylcholinestrase (AChE), and reduced graphene oxide (RGO)-supported zirconium pxide (ZrO2/RGO) nanoparticles for chlorpyrifos (pesticide) detection. They schematically illustrated the significance of this biosensor (Fig. 11.5).

11.1 Introduction

FIGURE 11.4 Schematic representation of graphene/ZnO biosensor-based nanocomposites (Shin et al., 2015).

FIGURE 11.5 Process of nanocomposite pesticide biosensing and detection from nature to the laboratory.

The concept of huge molecules is a globally accepted subject by many researchers. Natural, elastomer, and synthetic polymers are three main groups of polymers. Natural polymers have very complex structures as compared to synthetic polymers. Elastomers, include rubbery materials, have found to use widely

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in various bio-applications owing to their structural and mechanical properties. Elastomers can be prepared either synthetically or naturally. The term “conductivity” can be used for both, inorganic and organic materials (Fig. 11.6). The structural requirement for conducting polymer (CP) is a conjugated π-electron system. The CPs are also possible when the π-electron is a side chain. In this chapter, we attempt to answer two fundamental questions that may be encountered by researchers in developing biosensor performance, including: 1. The type of analytes that nanosized metal oxides can measure in an appropriate condition. Metals or metalloids

Analyte

Complement (CNT, graphene, clay, polymer)

Oxygen

(glucose, cholestrol,...)

Method (Electrochemical,...)

FIGURE 11.6 Conductivity of advanced materials.

Measurement

11.1 Introduction

2. The best criteria for selection of metal oxides capable for analyte measurement. Analyte Metal oxide

Method measurment

Analyte biosensor

11.1.2 POLYMERS Polymers are inherently low in electrical conductivity. However, there are several synthetic polymers with electrical conductivity, such as polypyrrole, polythiophene, etc. (Table 11.1). These polymers are not thermoplastics. Admixing conducting fillers into nonconducting polymers is an effective and economical way to produce electrically CP components with better processability, flexibility, and mechanical properties. Various elements can be used as a sensing element: DNA, antibodies, cells, molecularly imprinted polymers (MIPs), and conducting and semiconducting polymers (CPs and SCPs). The high application potential of CPs and SCPs in chemical and biological sensors is one of the main reasons for their intensive investigation and development. Conductive polymers or electroactive polymers are a famous subgroup of synthetic polymers, bearing at least one conjugated π-electron system. Polymers with conjugated π-electron systems display unusual electronic properties, including high electron affinities and low ionization potentials, hence, they are easily oxidized or reduced by charge transfer agents. The as-mentioned phenomena in poly (radicalcation) (p-type) or poly(radical-anion) (n-type)-doped conducting materials are close in relation to metallic conductivity. Fig. 11.6 illustrates the possible conductivity range of various materials.

11.1.3 NANOCOMPOSITES/PARTICLES Nano-ordered composite materials consisting of inorganic/inorganic or organic/ inorganic materials have been attracting attention for the purpose of creating high-performance functional materials (Salehabadi et al., 2014; Salehabadi, 2014; Tan et al., 2016). The advent of NPs in the 21st century with their many advantages, such as large surface-to-volume ratio, high surface reaction activity, and strong adsorption ability to immobilize desired biomolecules, especially enzymes, has changed the technology of making high-performance materials. NPs may not always be very useful in biomedical and technological applications. They have a large ratio of surface area to volume and strong dipole
dipole

365

Table 11.1 Conductive Polymers The Main Chain Contains

Heteroatoms Present No Heteroatom

Nitrogen-Containing

Sulfur-Containing

Aromatic cycles

Poly(fluorene)s Polyphenylenes Polypyrenes Polyazulenes Polynaphthalenes

The N is in the aromatic cycle: Poly(pyrrole)s (PPY) Polycarbazoles Polyindoles Polyazepines The N is outside the aromatic cycle: Polyanilines (PANI)

The S is in the aromatic cycle: Poly(thiophene)s (PT) Poly(3,4ethylenedioxythiophene) (PEDOT) The S is outside the aromatic cycle: Poly(p-phenylene sulfide) (PPS)

Double bonds Aromatic cycles and double bonds

Poly(acetylene)s (PAC) Poly(p-phenylene vinylene) (PPV)

11.2 Sensors and Biosensors

attractions; hence, they can easily aggregate. Moreover, the number of functional groups usually limits selective binding (Chen et al., 2011).

11.2 SENSORS AND BIOSENSORS Sensor technology is a basic enabling technology in many instances. In intelligent manufacturing processing, sensors have been considered in a range of applications, from assessing aircraft integrity to monitoring chemicals in the environment. The structural principle of sensor technology is instructed by ceramic materials. The oxygen-deficient crystal structure in semiconducting oxide materials can change significantly the resistance of an oxide sensor. A biosensor or a bioreceptor or transducer, is an integrated receptor-transducer device, which converts the biological-recognition events into a measurable physicochemical signal that is proportional to the target concentration (Lamabam and Thangjam, 2016). Electrochemical, optical, piezoelectric, and electromechanical sensors are four types of biosensors (Fig. 11.7). We will discuss the various types of biosensors in the coming sections.

11.2.1 SENSING MEASUREMENT The sensors, in general, are also classified by various criteria, including primary input quantity (measurand), transduction principles (using physical and chemical effects), material and technology, property and application. Certain features have to be considered in sensor technology. Accuracy, environmental condition (temperature/humidity), range measurement (limit of sensor),

FIGURE 11.7 Classification of biosensors.

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and calibration are essential criteria for selecting sensing devices. Moreover, resolution, linearity range, cost, and finally repeatability are the most important factors in sensor technology. In sensor measurement, in fact, the sensor performance measures from the Vout of RL that cascades RS (the resistance of sensor) using Eqs. (11.2) and (11.3). RS 5 RL 3 S5

ðVc 2 Vout Þ Vout Ra Rg

(11.2) (11.3)

where RS is the resistance of the sensor, S is the sensor response, Ra and Rg are the resistance in air and in the air mixed with detected gases, respectively (Enhessari and Salehabadi, 2016).

11.3 APPLICATION OF SENSORS Sensor applications can be expressed based on their classification into the following groups: •

•

•

•

Accelerometers: These are instruments for measuring the acceleration of a moving or vibrating body (pace makers and vehicle dynamic). They are based on microelectromechanical (MEM) sensors. Biosensors: As mentioned above, biosensors are based on electrochemical technology and are used for all bioactive sites like food testing, medical care device, water testing, and biological warfare agent detection. Image sensors: These are based on the complementary metal-oxide semiconductor technology and are used in consumer electronics, biometrics, traffic and security surveillance and PC imaging. Motion detectors: These are based on infrared, ultrasonic, and microwave/radar technology and are used in video games and simulations, light activation, and security detection.

In this section, the general observations of the sensors will be discussed in five categories: gas, chemical, environment, biology, and clinical.

11.3.1 GAS (BIO)SENSORS The advent of high-performance solid-state gas sensors has motivated several scientists in searching for new materials and investigating their gas-sensing properties. Spinels, illuminites, and perovskites are three famous groups in gas sensor

11.3 Application of Sensors

technology. The mixed metal oxides are oxidation catalysts or oxygen-activated catalysts. The stability of the mixed metal oxide structure allows synthesis of new compounds with a high extent of oxygen deficiency. In our previous chapter (Enhessari and Salehabadi, 2016), we summarized the as-reported perovskite nanomaterials in gas-sensing devices. We mentioned that the high catalytic activity of perovskite oxides depends on the high surface activity to oxygen reduction ratio or a large number of oxygen vacancies in the particular structure. Among the various reactions studied, automobile exhaust gas, various pollutant gases such as H2S (Shandiz et al., 2013) and NH3 (Song et al., 2011), NOx decomposition reaction gas (Zhou et al., 2014), hydrogen gas (Mukherjee and Majumder, 2014), methanol (Sen et al., 2015), and LP gas (Ranjith Kumar et al., 2014) have attracted particular attention. The perovskite materials can be used as a thin film (nanocomposites) or nanopowders. Table 11.2 represents the most important perovskite materials in gas-sensing devices. Lanthanum iron oxide nanocrystals (Bhargav et al., 2014, 2015), NiFe2O4 (spinel)-La0.8Pb0.2Fe0.8Co0.2O3 (perovskite) (Maity et al., 2015), SmFe0.7Co0.3O3 perovskite oxide (Zhang et al., 2010), lanthanum cuprate (La2CuO4) (Dharmadhikari et al., 2014), nanocrystalline La12xCaxFeO3 (Shi et al., 2014), barium stannate and aluminum oxide-based gas sensor (Kocemba et al., 2007), LaCoO3 (Velciu et al., 2015; Ding et al., 2016), NdCoO3 (Malavasi et al., 2005), Mg0.5Zn0.5Fe2O4 (Mukherjee and Majumder, 2013), NdCoO3 perovskite (Malavasi et al., 2005) are some examples of mixedmetal oxides used as promising materials for CO/CO2-sensing devices. The mechanism of CO sensors is as depicted in Eq. (11.4). 2 O2 ads 1 CO.CO2 1 e 1 COg .COaq: .COads 1 e2 2 COads 1 O2 2 .CO2 1 e COg 1 Oxo .CO2 1 e2 1 Vxo CO 1 1=2 O2 1 Vxo 1 e2 .CO2 1 Oxo

(11.4)

11.3.1.1 NOx Lanthanum-based mixed metal oxides are considered for NOx gases detection. (La0.8Sr0.2)2FeNiO62δ (Zhou et al., 2014), LaFeO3, and LaMnO31δ (Armstrong et al., 2011) are some examples of this sensing device. According to mixed potential theory, in a potentiometric sensor, the redox reactions always govern the potential difference between a sensing electrode and a reference electrode, according to Eq. (11.5), NO2 1 2e2 3NO 1 O22 2O22 3O2 1 4e2

(11.5)

Here, the reactions for NO2 exposure or reverse reactions for NO exposure occur at a sensing electrode where the reference electrode is exposed to air (Armstrong et al., 2011). NO2 acts as an oxidizing gas, hence, oxygen is produced at the electrode while NO is a reducing gas and consumes the oxygen. The former

369

Table 11.2 Gas-Sensing Materials Based on Mixed Metal Oxides (Enhessari and Salehabadi, 2016) Sensing Materials

O2

H 2O

CO/CO2

C2H5OH

Titanate

Sr(Ti0.65Fe0.35)O3 Pb(Zr0.2Ti0.8)O3

BaTiO3 CdTiO3 Na2Ti3O7

Li0.35La0.55TiO3

CoTiO3, Cr1.7Ti0.3O3, Zn2TiO4,

Ferrite

SrTi0.6Fe0.4O3aδ BaFeO3

La0.8Sr0.2Fe1axCuxO3 Cu0.5Zn0.5W0.3Fe1.7O4

Co1axNixFe2O4 Mg0.5Zn0.5Fe2O4

Co1axNixFe2O4 ZnFe2O4 NixZn1axFe2O4 GaFeO3 Ni1axCoxFe2O4

NOx

Ref.

Perovskite

LaFeO3, Co1axMnxFe2O4 CoFe2O4

[Argirusis et al., 2011; Belle et al., 2014; Du et al., 2010; Imran et al., 2013; Isarakorn et al., 2011; Santhaveesuk et al., 2015; Wang et al., 2011; Yoon et al., 2013; Zhang et al., 2008] [Armstrong et al., 2011; Bagade and Rajpure, 2016, 2015; Cavalieri et al., 2012; Chapelle et al., 2011; Iio et al., 2014; Joshi et al., 2016; Karthick Kannan and Saraswathi, 2014; Kazin et al., 2011; Meuffels, 2007; Mukherjee and Majumder, 2014, 2013; Sen et al., 2015; Sutka et al., 2013; Tudorache et al., 2013]

Cobaltite

Bi10Co16O38 Ln1axSrxCoO3

NdCoO3 Nd0.8Sr0.2CoO3 Bi12(Bi0.55Co0.45)O19.6 LnBaCo2O51δ Bi10Co16O38 Ba0.5Sr0.5Co0.8Fe0.2O32δ

Cobaltate

Mangenate

YCoO3 LaCoO3 La12xCexCoO3 NdCoO3

La0.8Sr0.2Al0.9Mn0.1O3 Ln1axCa(Sr)xMnO3

La0.6Ca0.4Mn1axNixO3

Cerate

BaCe0.90Gd0.1O3aδ

Niobate

BaNbO3

Nickelate Stanate

YCoO3

Ba12xNixSnO3 Ba12xLaxSnO3 ZnSnO3

AlNbO4 CrNbO4 InNbO4 LaNi03 BaSnO3

CaSnO3, Zn2SnO4,

LaNi03 Zn2SnO4

[Addabbo et al., 2015; CasasCabanas et al., 2011; Gómez et al., 2015; Malavasi et al., 2005; Michel et al., 2007; Shuk et al., 1993; Tealdi et al., 2007; X. Wei et al., 2010a] [Addabbo et al., 2015; Ding et al., 2015; Malavasi et al., 2005; Shi et al., 2014; Tealdi et al., 2007] [Armstrong et al., 2011; Franke et al., 2016; Mullen et al., 2014; Shuk et al., 1993] [Luyten, 1991; Schutter et al., 1992; Wei et al., 2011; Zhou et al., 2015] [Gnanasekar et al., 1999; Zhang et al., 2009b] [Xuchen, 2000] [Ganbavle et al., 2014; Huang et al., 2012; Jiang et al., 2011; Kocemba et al., 2007; Song et al., 2011; Tharsika et al., 2015; Upadhyay and Kavitha, 2007] (Continued)

Table 11.2 Gas-Sensing Materials Based on Mixed Metal Oxides (Enhessari and Salehabadi, 2016) Continued Sensing Materials

O2

H 2O

CO/CO2

C2H5OH

NOx

Ref.

Perovskite Zirconate

CaZrO3

Chromate

MgCr2O4

Molybdate

Tungstate

Bi3FeMo2O12

ZnMoO4 Bi3FeO4(MoO4)2 NiMoO4 CuMoO4 PbMoO4 ZnWO4 MnWO4

[Andre et al., 2014; Deng et al., 2001; Zhou and Ahmad, 2008] LaCr1axTixO3

Bi3FeMo2O12

CoWO4 SnxWO31x SnW04

[Pingale et al., 1996; Pokhrel et al., 2007; Saha et al., 2005] [Edwin Suresh Raj et al., 2002; Sears, 1989; Sundaram and Nagaraja, 2004]

Bi3FeMo2O12

CuWO4 SnW04 MgWO3 ZnWO4 BaWO4

[Gonzalez et al., 2012; Kärkkänen et al., 2010; Solis and Lantto, 1995; Solis et al., 2000; Suresh Raj et al., 2002; Tamaki et al., 1995; You et al., 2012]

11.3 Application of Sensors

Table 11.3 Examples of Alcohol Sensors and Their Respective Linearity Range Materials

Linearity

Ref.

Ni/nafion/graphene CNT/Ni Pd-Ni/SiNWs Ni
CF La0.8Pb0.2Fe12xMgxO3 LaCoxFe12xO3 In22xNixO3 Zn2TiO4

0.43
88.15 mM 500
600 μM 0
20.4 mM 0.25
87.5 mM 500 ppm 137
500 ppm 1
500 ppm 50
200 ppm

[Jia and Wang, 2013] [Chen and Huang, 2010] [Tao et al., 2009] [Zhang et al., 2015] [Azizi et al., 2015] [Feng et al., 2011] [Feng et al., 2012] [Santhaveesuk et al., 2015]

increases the potential at the sensing electrode (positive response) and the latter decreases this potential (negative response).

11.3.1.2 Ethanol Various compositions of materials, either mixed metal oxides or composites are used for alcohol sensing. The sensitivity, selectivity, and stability are three important factors in alcohol sensors. In Table 11.3, the famous materials and the respective properties are summarized. In the case of perovskite-based sensors, O2 adsorbs on the surface substrate, trap electrons and the negative-charged chemisorbed oxygen species in the form of O22 and O2. As sensing materials react with reducing gases, the gas molecules primarily react with oxygen, the carrier density is depressed (due to the electron-donating nature of gases), and finally increasing the resistance (Eq. 11.6). 2 C2 H5 OH 1 6On2 ðads:Þ -2CO2 1 3H2 O 1 6ne

(11.6)

For example, Ni/nafion/graphene nanocomposites are fabricated for alcohol sensing (Fig. 11.8). Here, nanoscale Ni is electrochemically deposited on the surface of nafion/graphene film to fabricate a nonenzymatic ethanol sensor (Jia and Wang, 2013). The high electrocatalytic activity of this sensor toward oxidation of ethanol in alkaline media is reported with linearity range of 0.43
88.15 mM.

11.3.1.3 Oxygen Monitoring oxygen as dissolved in medical, food processing, and waste management industries is important. The sensors for such detection including clark electrodes, aqueous electrochemical cells, paramagnetic gas sensors, and optical sensors (Ramamoorthy et al., 2003). In oxide semiconducting oxygen sensors the defects, that is, oxygen vacancies and free charge carriers, create oxygen adsorbates on the surface of the oxide semiconductor at elevated temperatures as illustrated in Eq. (11.7),

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FIGURE 11.8 Preparation of Ni/nafion/graphene nanocomposites (Jia and Wang, 2013).

O2ðgasÞ 2O2ðadsÞ O2ðadsÞ 1 e2 -O2 2ðadsÞ 2 2 O2 2ðadsÞ 1 e -2OðadsÞ

(11.7)

For example, the effect of Ce-doped BaFeO3 perovskite materials as oxygen sensors was fabricated by Penwell and Giorgi (2014). At 800 C, the conductivity of Ba0.95Ce0.05FeO32δ reaches 3.3 S/cm. Moreover, linear and reproducible responses of the sensor at 500 C and 700 C were observed.

11.3.1.4 Water (humidity) In many industries, monitoring of the water vapor concentration is very important. Industrial drying plant, ovens, and other processing activities, effluent gases from power plants, incinerators, metal refining furnaces, skin humidity, mineral processing kilns and chemical plants require measurement of humidity at elevated temperatures. Different ceramic sensors for humidity detection are investigated including ionic, electronic, solid-electrolyte, and rectifying-junction types. The polymeric resistive humidity sensors are also reported based on polyelectrolytes and conjugated polymers (Chen and Lu, 2005). Humidity sensors detect the relative humidity (%RH 5 (PV/PS) 3 100) of the moisture and temperature in the air. It is defined as a ratio of moisture to the maximum amount that can be held in the environment at the present temperature and pressure. Absolute humidity (AB 5 mw/v) is a ratio of the mass of water vapor in air to the volume of air. Fig. 11.9 shows the mechanism of humidity sensing.

11.3.2 CHEMICAL (BIO)SENSORS A chemical (bio)sensor is a device that transforms chemical information into an analytically useful signal. The chemical information may originate from a

FIGURE 11.9 Schematic representation of a typical humidity sensor.

Table 11.4 Classification of Chemical Sensors (Enhessari and Salehabadi, 2016) Type of Chemical Sensor

Source

Example

Optical (optodes)

Optical phenomena

Electrochemical

Electrochemical process

Electrical

Electrical properties

Mass sensitive

Mass change at a specially modified surface Change of paramagnetic properties Heat effects of a specific chemical reaction or adsorption

Absorbance, reflectance Luminescence Fluorescence Opto-thermal effect Light scattering Voltammetric sensors Potentiometric sensors Chemically sensitized field effect transistor potentiometric solid electrolyte gas sensors Metal oxide semiconductor Organic semiconductor Electrolytic conductivity Electric permittivity Piezoelectric devices Surface acoustic wave devices

Magnetic

Thermometric

Radiation

Change of physical properties

Oxygen monitors

Combustion reaction Enzymatic reaction Optothermal device X-, p- or r-radiation Chemical composition

chemical reaction of the analyte or from a physical property of the investigated system. The chemical biosensors can be used in diverse applications such as medicine, home safety, environmental pollution, and many others. Chemical sensors are classified according to the operating principle of the transducer (Table 11.4).

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11.3.2.1 Drugs As discussed in previous sections, a biosensor is a sensing device comprising a biological component which can be connected to a physical transducer. The interaction of a drug compound and immobilized biomatter allows quantitative investigation on the basis of this dual configuration (Yu et al., 2005). Enzyme-based biosensors can be applied in the pharmaceutical industry for monitoring chemical parameters in bioreactors (Wang et al., 2013; Sin et al., 2014; Olaru et al., 2015; Bachan Upadhyay and Verma, 2013; Ahmed et al., 2014). The determination of salbutamol by adsorptive stripping voltammetry on a CPE modified by iron titanate nanopowders has been reported by Attaran et al. (2012). They expressed that the resulting electrode-based nanocomposites have a linear response in the range of 0.2
25 nM with a detection limit of 90 pM. In an electrochemical study of nickel titanate nanoceramic modified electrode for salicylic acid (SA), Ghoreishi et al. (2015) observed that under optimized conditions, two linear calibration ranges of 3.0
40.0 μM and 40.0
1000.0 μM for SA were obtained with a detection limit of 68.0 nM (S/N 5 3). A study on captopril in the presence of para-aminobenzoic acid (Mehdi et al., 2015) showed that the MnTiO3/CPE nanocomposite is a promising metal oxide for catalytic oxidation of drug. The sensing device showed two linear dynamic concentration ranges of 1.0 3 1028 to 1.0 3 1027 and 1.0 3 1027 to 1.0 3 1026, with a detection limit of 1.6 nM. Co3O4/SnO2 nanocomposites are also used for diltiazem detection in tablets and biological fluids (Attaran et al., 2016). Anodic stripping voltammeter setup of chemically modified carbon paste electrode containing Co3O4/SnO2 nanopowders has been used with a linear response to a drug concentration range of 50
650 nM, with a lowest detection limit value of 15 nM.

11.3.3 ENVIRONMENT BIOSENSORS The constant increase in the number of harmful pollutants in the environment calls for fast and cost-effective detection analytical techniques. The requirement for disposable systems or tools for environmental applications in place of traditional analytical systems, in particular for environmental monitoring, is a driver for the development of new technologies for quantitative detection. The biosensor is an appropriate alternative or complementary tool, a subgroup of chemical sensors, in which a biological mechanism is used for analyte detection. Food, heavy metals, and pesticides, and dust are three important pollution areas that need to be detected/controlled. In this section the heavy metals and pesticides will discussed.

11.3.3.1 Heavy metals Heavy metals are essential nutrients (Fe, Co, Zn, etc), harmless (Au, Ag, In, etc), however, they can be toxic in larger amounts, and poisonous (Cd, Hg, Pb, etc). Cadmium, mercury, and lead are heavy metal environmental contaminants.

11.3 Application of Sensors

Heavy metal biosensors

Protein based

Non enzymes

Enzymes

Inhibition

Activation

Whole-cell based

Fusion

Antibodies

Regular

Natural

Genetically engineered

Monoclonal

FIGURE 11.10 Classification of heavy metal biosensors (Verma and Singh, 2005).

They are bioaccumulative and can impose serious organ damage. Efficient, economical, and deployable techniques are still required. Sensing devices based on CPs are reported while the metal-oxide-based nanocomposites are not widely reviewed. The enhancement of analytical performance for trace analysis using sensors is still in progress. Heavy metal ion detection using biosensors can be monitored using protein-based and whole-cell-based approaches (Fig. 11.10). An optical biosensor for the detection of trace heavy metals was fabricated by Shtenberg and his coworkers (2015). In this system, an unknown aqueous solution is incubated with enzyme functionalized P-SiO2, using a simple CCD spectrometer setup. The optical monitoring correlates to the trace elements in the sample. The results indicate the high specificity and sensitivity of the system towards three metal ions (Ag1 . Pb21 . Cu21), with a detection limit range of 60
120 ppb.

11.3.3.2 Pesticide and dust In the 21st century, new classes of pollutants like pesticides and dust directly affect human health. Agriculture is a main source of pesticide consumption. On the other hand, the dust in the atmosphere comes from soil, dust lifted by weather, volcanic eruptions, and pollution. Generally, for detection of these types of pollutions, chromatographic separation techniques such as gas and liquid chromatography are traditionally used. Nowadays, some sensors have been invented for quantitative sensing of pesticides and dust. Enzyme-based amperometric biosensors, such as acetylcholinesterase (AChE), are reported as important sensing devices for the detection of pesticides in healthcare measurement, the food industry, and environmental analysis. The use of (mixed) metal oxide nanoparticles and their respective nanocomposites were reported as promising materials with improved response time, linear range, detection limit, reproducibility, and long-term stability of biosensors

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(Kumar et al., 2016; Zheng et al., 2016; Li et al., 2014; Zhao et al., 2013; Won et al., 2010; Zhang et al., 2012; Gupta et al., 2010; Prabhakar et al., 2016). A biosensor based on graphene/Co3O4 nanocomposites was designed for organophosphate (OP) pesticide detection, electrochemically (Zheng et al., 2016). The synergic effect of nanocomposites, including high surface area, good biocompatibility, and excellent conductivity for this biosensor, were observed toward OP detection in the low detection limit, good repeatability, wide linearity, and short response inhibition time. Carboxylic silica nanosheet (SNS)/platinum nanoparticle modified glass carbon electrodes for pesticide detection were also fabricated by Li et al. (2014). They prepared a Pt NPs
CSNS
NF nanocomposite as shown in Fig. 11.11. SNSs were prepared from montmorillonite, modified using APTES, and functionalized by succinic anhydride in DMF to form CSNS followed by suspension of CSNS in H2PtCl6 and NaBH4 to get Pt NPs
CSNS. Finally, the as-prepared nanocomposites were mixed with NF to get Pt NPs
CSNS
NF. A GCE electrode were covered by Pt NPs
CSNS
NF and coated by AChE-CS. The above electrodes were immersed in PBS containing various concentrations of pesticide and transferred into an electrochemical cell (pH 7.4 PBS containing 0.5 mM ATCl) to study the amperometric response. The inhibition of pesticide can be measured using Eq. (11.8).  Inhibition % 5

 ip;control 2 ip;exp 3 100 ip;control

(11.8)

where ip,control and ip,exp are the peak currents of ATCl on the biosensor in the absence or presence of pesticide inhibition, respectively.

FIGURE 11.11 Preparation of pesticide biosensor-based Pt NP
CSNS
NF/GCE/AChE-CS nanocomposites (Li et al., 2014).

11.3 Application of Sensors

11.3.4 BIOLOGICAL SENSORS In bioscience, the sensors which can detect analytes are called biosensors. They are based on biological components, such as cells, protein, nucleic acid, or biomimetic polymers. On the other hand, nonbiological sensors are also used for biological sensing, called nanosensors. Biological agents like viruses, bacteria, pathogenic organisms, and the toxins they produce are difficult to detect. The sensing method in biological agents should be specific, that is, capable of discriminating between closely related pathogenic and nonpathogenic organisms or toxins, sensitive enough to detect trace targets, high affinity for maintaining binding even through repeated washing steps; and finally stable to long-term use (Sapsford et al., 2008). Biochemical compounds can be detected by sensors. Compared with the traditional analytical systems, sensors are relevant tools for bioanalyte detection, quantitatively. Sensors are composed of active sensing materials coupled with a signal transducer. These devices transmit the signal selectively and sensitivity electrically, thermally, or optically. Thus, the development of potential active materials plays a key role in designing efficient, reliable, and innovative sensing devices. Various metal oxide-based nanocomposites are shown in Table 11.5.

11.3.4.1 DNA Cancer is today’s most pressing health concern. Hence, very sensitive monitoring of cancer cells must be provided for an easy and effective way to monitor progression of the disease, DNA, and cells (Huang and Jie, 2013). Various sensors are biologically fabricated for DNA detection, such as MIPs, conducting and SCPs, and nanomaterials, either pristine or nanocomposites. In CPs, there is almost no conductivity in the neutral state. The charge carriers upon oxidizing (p-doping) or reducing (n-doping) their conjugated backbone are responsible for conductivity. Oxidation followed by relaxation processes causes the formation of localized electronic states (polaron) (Billmeyer, 2007). Complexity, timeconsuming procedures, and narrow targets are some of the drawbacks of traditional DNA detection. Recently, electrochemical DNA biosensors have been used for detection of bacteria, because of their fast response time, convenience, and high sensitivity (Li et al., 2013). A schematic diagram of the preparation, sensor fabrication, and detection process of GOx
Thi
Au@SiO2 (Fig. 11.12) was reported by Li et al. (2013). The nanocomposite-based (mixed) metal oxides for DNA detection are tabulated in Table 11.5.

11.3.4.2 Protein Rapid and low-cost determination of proteins is needed immediately. The most widely used tests are based on colorimetric procedures. In this technique, the proteins react to produce colored complexes. Various factors govern the detection, such as absolute protein quantity, amino acid composition, protein purity, and association state. Biosensors have achieved great interest due to their operational

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Table 11.5 Examples of Biosensors-Based Mixed Metal Oxides Nanocomposites Analyte

Composite

DNA

CuO/SWCNT [Chen et al., 2017, 2016, 2015; Deng et al., 2014; Gupta et al., 2010; Hushiarian et al., 2015; Li et al., 2013; Shukla et al., 2012; Tak et al., 2016; Wang et al., 2015; Yang et al., 2015]

TiO2/GO/ PPy Protein

Glucose

Cholesterol

Ru-silica/gold Chitosan/Fe2O3 CNT/MnO2 Co3O4/polyaniline Fe3O4/polydopamine/ Au Chitosan/Au ZnO/Au CNT-gold-titania ZnO-silicon ZnO-CuO CuO-graphene CuNiO-graphene CuO/polypyrrole/ graphene oxide RGO/ZnO Fe3O4/chitosan ZnO/chitosan-g-PVA Graphite/SrPdO3 Indium tin oxide (ITO)/ MMT Graphene/CNT/ZnO/ Au CeO2/chitosan/ITO CHIT/CeO2
GR CNT/Fe3O4 Au/TiO2 NiFe2O4/CuO/FeOchitosan

Linearity Fe3O4@3D-GO 3D NG/Fe3O4 Poly(3,4-ethylenedioxythiophene)/Au GOx-Thi/Au@SiO2 SiO2
CeO2 Zn/chitosan-PVA CuO/GO Sm/CeO2 ZnO/CNT 0.007
3.5 nM 0.2
200 pM
0.2
100 ng/mL 12
1280 μM 1.0
8.0 3 102 pg/mL 0.6
110 ng/mL 0.1
33.0 μM 0.1
8 mM

0.001
8 mM 0.05
16 mM 1
100 mM 5
400 μM 1
26 mM
0.1
6 mM 1
20 mM
10
400 mg/dL 0.02
1.3 mM
7
100 μM 50
5000 mg/L

Ref. 1.0 3 10214
1.0 3 1028 M 0.01
100 nM 1.0 3 10214
1.0 3 1026 M
0.02
50.0 nM

0.001
8 mM 1 3 10213
1 3 1026 M 5
180 ng/μL

[Afsharan et al., 2016; Cai et al., 2015; He et al., 2015; Masoomi et al., 2013; Prabhakar et al., 2016; Wang et al., 2014]

[Alizadeh and Mirzagholipur, 2015; Elads et al., 2015; Karuppiah et al., 2015; Kaushik et al., 2008; Miao et al., 2015; Moozarm et al., 2015; Shukla et al., 2012; Wei et al., 2010; Wu et al., 2013; Zhang et al., 2014; Zhang et al., 2015]

[Du et al., 2015; Joshi et al., 2015; Sandeep Kumar et al., 2015; Malhotra and Kaushik, 2009; Singh et al., 2012; Tang et al., 2016; Zarei et al., 2012]

(Continued)

11.3 Application of Sensors Table 11.5 Examples of Biosensors-Based Mixed Metal Oxides Nanocomposites Continued Analyte

Composite

Linearity

Ref.

Urea

ZrO2/PPI ZnO/CNT TiO2/ZrO2 CoFe2O4-HSA/CT Fe3O4/polydopamine/ Au ZnO/LiTaO3 GCE/chitosan/Au Fe3O4/Au/3-((2mercaptoethylimino) methyl) benzene-1,2diol CNT/Fe3O4 CNT
MnO2 Graphene oxide/ITO ZnO/LiTaO3 TiO2/Au ZrO2
RGO Polyaniline
TiO2

0.01
2.99 mM 10
100 mg/dL 5
100 mg/dL 1.99
19.23 μM 0.3
8.0 3 102 pg/mL

[Charan and Shahi, 2016; Shukla et al., 2014; Srivastava et al., 2013; Tak et al., 2013]

Immunology

100 μg/mL 0.6
110 ng/mL 0.6
110 ng/mL

[An, 2016; He et al., 2015; Jamil et al., 2015; Suveen Kumar et al., 2015; Masoomi et al., 2013; Tu et al., 2015; Zarei et al., 2012; Zhou et al., 2010; Zhu et al., 2015]

30
1000 ng/mL 0.2
100 ng/mL 10
450 ng/mL 100 μg/mL 2
22 ng/mL 10
2500 μM 0.01
2.5 mM

FIGURE 11.12 Schematic representation of typical biosensors for DNA detection (Li et al., 2013).

381

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CHAPTER 11 Application of (mixed) metal

FIGURE 11.13 Schematic representation of a protein biosensor. Adapted from Leca-Bouvier, B., Blum, L.J., 2005. Anal. Lett. 38, 1419.

advantages over standard photometric methods. These advantages are rapidity, ease-of-use, mass manufacture, cost, simplicity, and portability. By appropriate recognition element selection, it is possible to detect either a particular target protein or a broad range of proteins (Fig. 11.13) (Leca-Bouvier and Blum, 2005).

11.3.5 CLINICAL BIOSENSORS The applications of biosensors in clinical chemistry have been reviewed by many researchers, including for both commercial applications and fabrication techniques. Electrochemical, optical, and piezo-optical modes of transduction are used for recognition elements including enzymes (biocatalytic recognition elements) and immune agents and DNA-affinity ligand recognition elements. Microfabrication will allow biosensors to be constructed as arrays and incorporated into lab-on-a-chip devices (D’Orazio, 2003). In clinical chemistry laboratories, clinical analyses are no longer used for clinical measurements. Biological fluids generally need to measure in hospital point-of-care settings, by caregivers in nonhospital settings, and by patients at home. Biosensors are a good candidate for measurement of analytes in clinical chemistry and are also suitable for new biological applications. Of all the clinical biosensors fabricated since 1962, glucose, cholesterol, urea, and immune biosensors have been commercially fabricated and used in various clinical aspects.

11.3.5.1 Glucose The advent of glucose biosensors has been concentrated on recently by many researchers due to their extensive applications in clinical diagnosis. Two types of

11.3 Application of Sensors

FIGURE 11.14 Working reaction of an amperometric glucose biosensor.

glucose biosensor have been used, based on enzymatic catalysts and nonenzymatic catalysts. The sensing capabilities of biosensors like sensitivity, stability, biocompatibility, reproducibility, and selectivity are the most important objectives. Amperometric biosensors use enzymes as the recognition element used for the first time as a sensor for glucose in blood and involved immobilization of the enzyme glucose oxidase (D’Orazio, 2003). A solution of glucose oxidase was physically entrapped between two membranes; a gas-permeable membrane and a dialysis membrane. The reaction is illustrated in Fig. 11.14. Various metal oxide-based nanocomposites with different linearity ranges have been reported by many researchers for glucose detection (Table 11.5). The detection device is mostly based on amperometric transducers. For example, the amperometric responses of various modified GC electrodes to glucose based on CuO/graphene nanocomposites were reported by Hsu and his coworkers (2012). In this setup, an appropriate amount of glucose is mixed with NaOH solution in three modified electrodes system. After adding various concentrations of glucose into a potassium hydroxide solution, the oxidation currents of a working electrode are monitored at a fixed potential. From the amperometric curve, the linear relationship between the oxidation current and glucose concentration can be detected. Fig. 11.15 shows the amperometric responses of graphene, CuO nanoparticles, and CuO/grapheme-modified GC electrodes as expressed by Hsu et al. (2012).

11.3.5.2 Cholesterol Cholesterol is a crucial biobased material for human body as it is a structural component of biological membranes, nerve, and brain cells. Cholesterol is synthesized in the liver and supplied from food, and takes part in the production of hormones, bile acids, vitamin D, and other vital molecules (Saxena and Das, 2015). Above 200 mg/dL can damage the blood vessels, causing several diseases such as hypertension, arteriosclerosis, coronary heart disease, lipid metabolism dysfunction, brain thrombosis, etc. Typical (mixed) metal oxide-based nanocomposites for cholesterol detection are listed in Table 11.5. An interesting nanocomposite for cholesterol detection

383

CHAPTER 11 Application of (mixed) metal

600 500

Current (µA)

384

Graphene

400

CuO CuOG

300 200 100 0 0

300

600

900

1200

1500

1800

Time (s)

FIGURE 11.15 Typical representation of amperometric responses of graphene, CuO nanoparticles, and CuO/grapheme-modified GC electrodes (Hsu et al., 2012).

2FeOOH Ni(NO3)2.6H2O + Cu(NO3)2.3H2O + Fe(NO3)3.9H2O NH4OH

Ni(OH)2 + Cu(OH)2 + Fe(OH)2+ NH4NO3+H2O

NiFe2O4 + CuO+FeO + H2O Chitosan

FIGURE 11.16 Schematic representation of NiFe2O4/CuO/FeO/H2O/chitosan film nanocomposites.

has been reported by Singh et al. (2012). They fabricated an electrochemical bioelectrode of ChOx/NiFe2O4/CuO/FeO-chitosan/ITO nanocomposite. The bioelectrode detects as a function of cholesterol concentration. A multistep synthesis technique is used for preparation of magnetic parts of nanocomposites (Fig. 11.16)

11.3 Application of Sensors

11.3.5.3 Urea Urea is toxic above certain concentrations. It is produced during nitrogen metabolism, therefore, it has great significance in clinical chemistry (blood urea), food chemistry, and environmental monitoring. Hence, continuous real-time monitoring in clinical, environmental, and food-related environments is important. The conventional techniques are time-consuming and mostly laboratory-bound. Biosensors can play important roles due to their ease of use, portability, and the ability to furnish real-time signals. The biocomponent of a urea biosensor is urease. Urease can catalyze the hydrolysis of urea to ammonium and hydrogen carbonate ions (Eq. 11.9). O H +

O H2N

NH2

+3 H

Urease H

N 2H

H H

–

–

+ HO + HCO3

(11.9)

A setup of an amperometric urea biosensor is fabricated based on urease and glutamate dehydrogenase. The immobilization is done on the nylon nets coupled with a Pt electrode. In this system, glutamate dehydrogenase in the presence of sodium hydroxide enhanced the reproducibility and stability of the sensor (Singh et al., 2008). ZnO
MWCNT/ITO is one of the novel urea biosensor-based nanocomposites (Tak et al., 2013). The nanocomposite exhibits good linearity over a wide range of urea concentrations (10
100 mg/dL), high sensitivity, low value of Km (0.85 mM), and a low detection limit of about 0.23 mM. The process of electron transfer is shown in Fig. 11.17. In this process, the analyte (urea) is added into the PBS solution containing [Fe(CN)6]32/42 as the redox mediator. As oxidation occurs, chemical products such as NH3 and carbon dioxide (CO2) are formed and subsequently the urease is reduced. The Fe31 ions present in the PBS solution capture the released electrons and reduce to Fe21 ions. The reverse reaction releases electrons which are transferred to the ITO layer of the efficient matrix of bioelectrode.

11.3.5.4 Immunology Electrochemical immunosensors have recently received great clinical and academic attention. However, some deficiencies, like poor sensitivity and slow electron transfer kinetics, have enhanced the use of electrochemically active tags such as enzymes, noble metals, and transition metal oxides for signal amplification (Tu et al., 2015). The impedimetric immunosensor is a sort of sensing device based on singlefrequency impedance measurements, and is a robust and label-free device. The bioreagents develop as the immunoassay is performed on the electrode surface.

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CHAPTER 11 Application of (mixed) metal

Urea

NH3

+

CO2

Matrix (ZnO/ZnO-MWCNTs)

Ureaseoxi

Ureasered

ITO

Urease Glass Fe2+

Fe3+

e–

FIGURE 11.17 The process of electron transfer (Tak et al., 2013).

FIGURE 11.18 Immunosensor reaction.

As shown in Fig. 11.18, an immunochemical reaction between the pesticide residues and the immobilized antigen is generated and the limited amount of antibody (Ab) on the surface is determined (Rodrı´guez and Valera, 2013).

11.4 Fabrication

11.4 FABRICATION As mentioned above, biosensors are fascinating analytical tools appropriate for revolutionizing the market in the near future. However, due to technical and production problems, the biosensors are not distributed widely. The fabrication of a multibiosensor system is needed immediately for various bioapplications. Traditionally, biosensors were fabricated in the form of membranes and transducers. It must be noted that with the long historical background of biosensors, very few of them are able to perform measurements in undiluted biological fluids for long-term applications (Urban, 2000). A biosensor system consists of sample handling, biorecognition, transduction, and signal interpretation. Fig. 11.19 shows the relationship between different parts of a sensing system. For bioactive target monitoring, small, integrated and reliable sensing elements are necessary. In principle, the “(bio)sensors” in general are elements receiving information either physically or chemically or physicochemically and transforming it into an electric response (Fig. 11.20). Biosensor devices are generally classified into four groups: biosensors based on acoustic transducers, optical microtransducers, calorimetric transducers, and electrochemical. The classification of biosensors and their respective working devices is tabulated in Table 11.6. External

External Biorecognition

Transduction Data processing

Sample

Observer Wet

Dry

FIGURE 11.19 Junction between main compartments of a biosensing system.

FIGURE 11.20 A typical biosensor.

387

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CHAPTER 11 Application of (mixed) metal

Table 11.6 Classification of Biosensors Based on Transducers Acoustic transducers

[Urban, 2000]

Optical microtransducers

[Dey and Goswami, 2011]

Calorimetric transducers

[Urban, 2000]

Electrochemical

Conductometric biosensors

[Isildak et al., 2012]

Potentiometric transducers

[Wang et al., 2012]

Amperometric biosensors

[Shimomura et al., 2013]

11.5 Selectivity, Sensitivity, and Time Factors

11.5 SELECTIVITY, SENSITIVITY, AND TIME FACTORS Selectivity: This factor is the essence of sensors. It is rare to find a sensor which will respond to only one analyte. It is more usual to find a sensor that will respond mainly to one analyte, with a limited response to other similar analytes. The extent of interference based on Nicolskii
Eisenman’s equation (Eq. (11.10)) can be calculated in terms of the electrode potential and a selectivity coefficient, ki,j, as follows, n=z

E 5 K 1 Slogðai 1 ki;j aj Þ

(11.10)

where ai and aj are the activity of the primary analyte of charge n and the activity of the interfering analyte of charge z, respectively. Sensitivity: The concentration range covered by any analytical technique from the calibration point of view, and the linearity response over the section of this range, in general, is called the sensitivity. At the lower level is the “detection limit.” It is the concentration of analyte at which the extrapolated linear portion of the calibration graph intersects the baseline. The linear ranges are generally much larger for potentiometric sensors covering 12 powers of 10 of the hydrogen ion concentration. Amperometric sensors and biosensors generally do not have ranges of much more than two or three powers of 10 (Eggins, 2002). Response times: The time required for a system to reach equilibrium is called the “response time.” The response time for chemical or biochemical nature-based sensors is offset by the simplicity of the measurement and the minimal sample preparation time. The response times in biosensors can be from a few seconds to a few minutes. Recovery times: The time that elapses before a sensor is ready to be used for another sample measurement is called the “recovery time.” A sensing device should rest immediately or after one measurement to resume its base equilibrium. Lifetimes: The response during continuous use of the sensor when it is in contact. The time after which the response has declined by a given percentage is called the “lifetime.” On the other hand, it is the time over which the assembled sensor is stored. To compare all the above factors in experimental results, three glucose biosensors are compared in Table 11.7. From the table, it can be seen that CuO/ grapheme has an excellent response time and detection limit. Table 11.7 Some Examples of Important Factors in Sensing Measurements Sensing Materials

Analyte

Detection Limit (mM)

Sensitivity

Linear Range (mM)

Response Time (s)

ZnO/CHIT-g-PVAL Au
Fe3O4@SiO2 CuO/graphene

Glucose Glucose Glucose

0.2 0.01 0.001

40.04 V/mM
1065 μA/mmol/L cm2

2
1.2 0.05
8.0 0.001
8

3 5 1

389

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CHAPTER 11 Application of (mixed) metal

11.6 SUMMARY AND RECOMMENDATIONS FOR FUTURE WORK It is interesting to note that even though biosensors have a long history, very few are capable of performing commercially in undiluted biological fluids for longterm applications. Nanotechnology can facilitate the synthesis of novel materials with desired applications. The mechanism of analyte detection varies based on the nature of the measurand, biosensors, etc. It can be concluded that the most appropriate biosensors are applied based on analyte conditions and optimum linearity range, detection limits, response times, recovery times, and lifetimes. From the view point of sensing materials (MONs, NPs, polymers, and their respective nanocomposites) and the fabrication process, the selected biosensors have unique characteristics. Therefore, preparation of novel materials for fabrication of “easy to use biosensors” and “multidisciplinary biosensors” are still required for everyday life. The “global health communities” would have urgently reacted for development of clinical biosensors.

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