Summary of various methods used for the production of 1-D ZnO nanostructures, adopted from [28].
\r\n\tDNA is responsible for carrying all the information an organism needs to survive, grow and reproduce. However, during its lifetime an each organism experiences a wide range of cases with DNA damages; therefore the DNA repair ability of a cell is vital to the integrity of its genome and thus to the normal functionality of that organism. Mutagenesis is known as an important factor which may lead to different disorders, disabilities and diseases. Any defect in DNA repair system may lead to the death of the organism.
\r\n\r\n\t
\r\n\tRecognition of these items in different organisms drives us to know more about the characteristics of DNA repair systems in different types of organisms. Hopefully, this book will offer an interesting read by introducing, explaining and comparing these diversities.
Generally, compressible flow is frequently referred also to as gas dynamics which is the branch of fluid mechanics that deals with flows having significant changes in fluid density. That is because gases, mostly, display such behavior. In fact, all materials are compressible. Besides liquids, gases and plasmas, to some extent, the plastic solids under strong shock can also be modeled as compressible flows. Because, in the last case, that the strength of material is negligibly smaller than that of the shock. Flows with a Mach number less than 0.3 are usually treated as being incompressible for that the variation of density due to velocity is less than 5% in that case. The study on compressible flow is relevant to various fields, such as high-speed aircraft, rocket motors, jet engines, high-speed entry into a planetary atmosphere, gas pipelines, etc. where shock wave and/or detonation play a significant role. In this chapter, the following several kinds of flows including high Mach number flows with combustion, multiphase flows with phase separation and complex flows with hydrodynamic instability1 are taken as examples, and all flows are treated uniformly as being compressible. It is hopeful that the methods and ideas developed in this chapter may be adapted, more or less, to other kinds of complex flows.
\nSome common and typical features of these flows are as below: each of them possesses multi-scale structures and/or kinetic modes. There exist plenty of interfaces inside the system. The interfaces include material interfaces and mechanical interfaces. Each of them experiences complicated competitions between behaviors in various spatial-temporal scales. The forcing and responsive processes inside the system are very complicated. Such a system generally shows pronounced non-equilibrium behaviors.
\nThe traditional macroscopic models of compressible flows are generally based on Navier-Stokes (NS) equations, even Euler equations. The model of Euler equations assumes that the system is always at its local thermodynamic equilibrium (LTE). The NS model considers the thermodynamic non-equilibrium (TNE) via the viscous stress and heat flux. The viscous stress and heat flux compose a set of convenient and effective description of the TNE. But the description is also quite dense and coarse-grained. Many specific TNE behaviors are invisible under NS description, even though they are helpful for understanding the specific TNE status. Besides, since it includes only the first-order term of Knudsen number in the Chapman-Enskog expansion [1], it is reasonable only when the Knudsen number is very small. It cannot be used to access deeper TNE behaviors. To access the complicated non-equilibrium behaviors, one possible solution is to use the molecular dynamics (MD) [2] or direct simulation Monte Carlo (DSMC) [3]. The MD simulation can help to understand some fundamental mechanisms from the atomic level. But the spatial and temporal scales it can access are too small to be comparable with experiments. The DSMC simulation has a similar constraint of computational cost.
\nThe NS model is not enough to capture so complex non-equilibrium behaviors while the MD and DSMC cannot access spatial-temporal scales that are large enough. Under such conditions, a kinetic approach based on the non-equilibrium statistical mechanics (NESM) [1, 4, 5] is preferable.
\nStatistical mechanics is a branch of theoretical physics. It was developed to study the average behavior of a mechanical system with uncertain state by using probability theory. Microscopic mechanical laws do not contain concepts such as temperature, heat, or entropy; however, statistical mechanics shows how these concepts arise from the natural uncertainty about the state of a system when that system is prepared in practice. Statistical mechanics provides exact methods to connect thermodynamic quantities to microscopic behavior.
\nThe NESM is based on mechanics and some necessary assumptions. The concept of macroscopic observation and assumption of coarse-grained density are cornerstones. The Liouville equation [4, 5] is the most fundamental governing equation when without quantum fluctuations. It describes the N-particle system using a partial-differential equation for the probability density function, f = f(ξ1, ξ2, ⋯ , ξN, t), in a 6N-dimensional space,
\nwhere m is the particle mass, ζi = (qi, pi), qi and pi are the coordinate and momentum of the ith particle, ΦN(q1, … , qN) is the interaction potential of all the N particles. By integration over part of the variables, the Liouville equation can be transformed into a chain of equations, which is referred to as the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy [4], where the first equation connects the evolution of one-particle probability density function with the two-particle probability density function, the second equation connects the two-particle probability density function with the three-particle probability density function, and generally the Sth equation connects the S-particle probability density function, fs,with the (S + 1)-particle probability density function, fs + 1. Specifically,
\nwhere V is the volume of system, \n
Here
\nand
\nWhen S = N, the above equation recovers to the Liouville equation. For a macroscopic system, the particle number, N, is in the order of Avogadro constant, 1023. Generally, there is no way to solve Eq. (1) or Eq. (2).
\nWe need to simplify the model via considering some simpler cases. If considering only the case where correlations among three and more particles are negligible, the two-particle interaction is relevant to their distance, and the two-particle distribution function can be written as the product of two single particle distribution functions, specifically,
\nwe obtain the Boltzmann equation [1, 4],
\nwith
\nHere is the single particle distribution function. Compared with the MD and Liouville equation, the Boltzmann equation is a much coarse-grained model. The purposes of establishing Boltzmann equation are to define and calculate the entropy in non-equilibrium state, to derive, prove, even modify the fundamental hydrodynamic differential equation [5].
\nAll hydrodynamic quantities are some kinds of kinetic moments of the distribution function and can be expressed as:
\nwhere the particle mass has been assumed to be 1, W is the vector of macroscopic quantity composed of density, momentum and energy, W = [ρ, ρu, ρE]T. Besides, the total stress σ, viscous stress Π and heat flux q are related to f via
\nand
\nrespectively. Compared with Boltzmann equation, in Navier-Stokes equations,
\nwhere Π = 2/3μ(∇ ⋅ u)I − μ(∇u)T − μ∇u, q = − κ∇T, T is the temperature, μ and κ are the coefficients of viscosity and heat conductivity, respectively. More details of molecular motions are neglected and the distribution function f disappears as well. What remained are the conserved kinetic moments, density ρ, momentum (density) ρu and energy(density) ρE, and a few non-conserved quantities, momentum fluxes ρuu and Π, energy fluxes ρEu and q. The relation between internal energy ρT and pressure p is given by the equation of state. In Euler equations, the viscous stress Π and heat flux q are further neglected.
\nFrom molecular dynamics to Boltzmann equation, to Navier-Stokes, and further to Euler equations, in each step, the description becomes coarse-grained, and the contained physical information becomes less [6]. The switching of model in each step corresponds to the state of system under consideration gets closer to its thermodynamic equilibrium, the behavior is simpler, consequently the system can be described by fewer physical variables. For a given non-equilibrium system, the switching of model in each step corresponds to the spatial-temporal scale that we use to observe the system becomes larger, consequently more smaller structures and quicker kinetic modes are invisible. What obtained are the remaining larger structures and slower kinetic modes. Based on Boltzmann equation, the most relevant TNE effects accompanying the hydrodynamic behaviors can be studied, in addition to the general hydrodynamic behaviors described by the hydrodynamic model.
\nFrom Boltzmann equation to DBM, two steps of coarse-grained physical modelings are needed. The principle for coarse-grained modeling is that the physical quantities used to measure the system must keep the same values after simplification.
\nStep 1: Linearization of the collision term
\nEven though, compared with MD or Liouville equation, Boltzmann equation is a much coarse-grained model, its collision term is still too complicated to be solved in most practical cases. The simplest way to simplify is to introduce a local equilibrium velocity distribution function, feq, and write the collision term into the following linear form,
\nThe physical meaning of the linearized collision model is thus: collisions of molecules result in that f approaches to thermodynamic equilibrium feq and the relaxation time is controlled by the parameter τ. If we do not aim to measure the system using the specific values of f, but using only some of its kinetic moments, then only if these kinetic moments keep invariable after the simplification, that will be OK. The linearization of the collision term requires
\nwhere Ψ = [1, v, vv, vvv, ⋯]T contributes the kinetic moments used to measure the system. The specific form of feq depends on the terms that Ψ takes. According to the specific form of Ψ or feq, the linearized collision model may be referred to as the Bhatnagar-Gross-Krook (BGK) model [7, 8, 9, 10], ellipsoidal statistical BGK model [11], Shakhov model (for monatomic gas) [12], Rykov model (for diatomic gas) [13], Liu model [14], etc. When only the mass, momentum and energy conservation laws are kept, feq takes the simplest form, the Maxwellian. This is the so-called BGK model. Due to its simplicity, the BGK model is most extensively used.
\nIt should be note that the Single-Relaxation-Time model works when all the kinetic modes approaching to thermodynamic equilibrium share more or less the same relaxation time. For more complicated cases where the relaxation times of different kinetic modes approaching to thermodynamic equilibrium are significantly different, the multiple-relaxation-time (MRT) collision model is needed [15].
\nStep 2. Discretization of the particle velocity space
\nWe first consider the case without the force term. The discrete Boltzmann equation reads,
\nwhere i is the index of discrete velocity. Since the common schemes for discretizing the space and time do not work for discretizing the particle velocity space. To find an effective means to discretize the particle velocity space, we go back to consider what we really need. Here, we do not aim to describe the system using specific values of the discrete distribution function fi, but its kinetic moments. So, only if these kinetic moments, originally in integral form, can be equally calculated in summation form, that will be acceptable. Specifically,
\nwhere the left hand side gives the kinetic moments of f needed to describe the system and vi at the right hand side is the discrete particle velocity. According to the Chapman-Enskog analysis, a kinetic moment of f can be finally calculated via an appropriate kinetic moment of feq. Therefore the requirement of Eq. (18) can be further written as
\nwhere the left hand side gives the kinetic moments of feq being involved in the process of constructing DBM. The requirement of Eq. (20) can be rewritten as a matrix equation,
\nwhere \n
Via the same idea, it is straight forward to formulate MRT-DBM. To ensure the relaxation times have clear physical meanings, in the MRT-DBM, the collision term is first calculated in the kinetic moment space, and then transformed back to the discrete velocity space. To ensure DBM describe reasonable flow behaviors, a correction term is needed [15].
\nAccording to the Chapman-Enskog analysis, to access system which is deeper into thermodynamic non-equilibrium, higher order terms in Knudsen number should be considered. As a result, the requirement of f in Eq. (19) will lead to more kinetic moment relations of feq in Eq. (20). Consequently, more discrete velocities are needed. For the case with inter-particle force, one should generally first finish the derivative calculation of f with respect to v, and then perform the discretization of particle velocity space.
\nOnce the concept of equilibrium is defined, the concept of non-equilibrium is clear. In classical mechanics, a particle is in mechanical equilibrium if the net force on that particle is zero. By extension, a physical system made up of many parts is in mechanical equilibrium if the net force on each of its individual parts is zero. In addition to defining mechanical equilibrium in terms of force, there are many alternative definitions for mechanical equilibrium which are all mathematically equivalent. In terms of momentum, a system is in equilibrium if the momentum of its parts is all constant. In terms of velocity, the system is in equilibrium if velocity is constant. In a rotational mechanical equilibrium, the angular momentum of the object is conserved and the net torque is zero. More generally in conservative systems, equilibrium is established at a point in configuration space where the gradient of the potential energy with respect to the generalized coordinates is zero. Similarly, a fluid system is in fluid mechanical equilibrium or hydrodynamic equilibrium if the net force on each of its ‘fluid particles’ (small fluid elements) is zero and without temperature gradient around the ‘fluid particle’.
\nFor ideal gas system, in thermodynamic equilibrium there are no net macroscopic flows of matter or energy either within a system or between systems. In non-equilibrium systems, by contrast, there are net flows of matter or energy. Global thermodynamic equilibrium means that the relevant intensive parameters are homogeneous throughout the whole system, while local thermodynamic equilibrium means that those intensive parameters are varying in space and time, but are varying so slowly that, for any point, one can assume thermodynamic equilibrium in some neighborhood about that point. Rarefied gases at ordinary temperatures behave very nearly like ideal gas and the Maxwell speed distribution is an excellent approximation for such gases. Thus, it forms the basis of the kinetic theory of gases.
\nIt is evident that Euler equations are used to investigate fluid flows which are at local thermodynamic equilibrium but mechanical non-equilibrium, while NS, Burnett and Super-Burnett equations are used to investigate fluid flows which are at mechanical and thermodynamic non-equilibrium. Only when all kinds of kinetic moments of (f − feq) are zero, the system is at thermodynamic equilibrium. hydrodynamic non-equilibrium (HNE) drives the evolution of f and results in viscous stress and heat flux.
\nIn many practical cases, it is neither necessary to know all the details of f nor necessary to know all the kinetic moments of (f − feq).Therefore, one can (i) care only the main feature of f, specifically, neglect the high order terms of Knudsen number, (ii) care only the kinetic moments of (f − feq) which are most relevant to the macroscopic behaviors under consideration, specifically, those involved in constructing the discrete Boltzmann model.
\nA centrally important motivation of DBM is to check, measure and analyze the non-equilibrium state and effects [6, 16, 17, 18]. The DBM presents two sets of measures for the TNE. One set is dynamically from the difference of the kinetic moments of f and feq,
\nwhere Mm(f) is the mth rank moment of velocity tensor of f, \n
Entropy production is a highly concerned quantity in both physics and engineering studies. From the physics side, it is helpful for understanding the complex non-equilibrium behaviors. From the engineering side, a process with lower entropy production may have higher energy transformation efficiency. Following the way of defining entropy equilibrium equation in the non-equilibrium thermodynamics, a new entropy equilibrium equation can be obtained as follows [19],
\nwhere s is the entropy density,
\nand
\nare the entropy flux and entropy production rate, respectively. \n
The TNE behaviors are very complex and difficult to quantitatively investigate. Finding a convenient and efficient method to characterize the TNE status and effects is the corner stone, DBM presents such a potential approach [16, 17, 18, 19, 20, 21, 22, 23, 24, 25].
\nThe traditional CFD needs to first know the exact form of the hydrodynamic equations, then design numerical algorithm according to the properties of those equations. The DBM is a coarse-grained model derived from the Boltzmann equation. In principle, it can be formulated and applied to simulate flows without knowing the exact form of the hydrodynamic equations, only if necessary kinetic moments of feq are followed. From the perspective of physical application, a DBM is roughly equivalent to a hydrodynamic model supplemented by a coarse-grained model of TNE. Via the DBM, it is easy to perform multi-scale simulations in a wide range of Knudsen number.
\nCombustion has long been playing a dominant role in the transportation and power generation. To improve combustion efficiency and decrease pollution, in recent years, some new combustion concepts have been proposed. For example, pulsed detonation engine, spinning detonation engine, microscale combustion, nanopropellent, partially premixed and stratified combustion, plasma-assistant combustion, cool flames, etc. All these new combustion concepts involve complicated non-equilibrium chemical and transport processes [26].
\nThe chemical reaction process is very complex and may include varieties of reaction mechanism. So far, most of the chemical reaction kinetic models are phenomenological. As the first step, we consider only the simplified form of Lee-Tarver chemical reaction rate law [21]. Considering the thermal initiation, the reaction kinetic is described by
\nwhere a and b are constants, λ is the concentration of the product and works as the reaction process parameter, Tth is the temperature threshold for chemical reaction. Consider the case where the chemical reaction is slow enough compared with the kinetic process of approaching thermodynamic equilibrium, so we can treat f as f∗eq during the reaction process. The evolution equation of single-relaxation-time DBM for combustion reads
\nwhere \n
Non-equilibrium quantities defined in Eq. (22) are used to study a simple case of detonation [21]. For the case of CJ detonation shown in Figure 1(a), the corresponding non-equilibrium quantities, \n
Profiles of physical quantities for CJ detonation. (a) The density ρ, pressure P, temperature T, x-component of velocity u, and the reaction process λ. (b) and (c) The non-equilibrium quantities \n\n\nΔ\n2\n∗\n\n\n and \n\n\nΔ\n3\n∗\n\n\n, respectively (see Ref. [21] for more details).
Figure 2 gives the pressure P and corresponding non-equilibrium quantity Δ2 , xx at a certain time [22]. From Figure 2(a)–(c), the relaxation time, 1/Ri, decreases by 10 times in each case so that the detonation wave changes from unsteady to steady. In Figure 2(d)–(f), the shaded area, enclosed by the curve Δ2 , xx and the x-axis, presents a rough description on the global TNE effect around the detonation wave. It is clear that the viscosity (and/or heat conductivity) decreases the local TNE while increases the global TNE around the detonation wave.
\nProfiles of physical quantities for different relaxation times. (a)–(c) The pressure P. (d)–(f) The corresponding non-equilibrium quantities Δ2 , xx (from Ref. [22] with permission).
Figure 3 shows some numerical results aiming to investigate the main mechanisms resulting in entropy increase and their relative importance in the combustion system [19]. It is clear that, in the checked cases, the most pronounced contribution to entropy increase is from the chemical reaction, ΔsCHEM, the lest contribution is from the non-organized energy flux, ΔsNOEF, the contribution of non-organized momentum flux, ΔsNOMF, is in between. With the increasing of Mach number, the entropy production caused by non-organized momentum flux becomes more remarkable.
\nMechanisms for global entropy production in four cases (from Ref. [19] with permission).
Phase separation is an important branch in the field of multiphase flows. It is also a kind of non-equilibrium phase transition. The key step for modeling phase separation is to incorporate the non-ideal gas effects into the discrete Boltzmann equation. Enskog equation can be regarded as an extension of Boltzmann equation under the hard-ball molecule model [1]. Although the specific treatments may be different, the aims are the same. Those are all to replace the equation of state of ideal gas with a more practical one.
\nIn 2007 Gonnella, Lamura, and Sofonea (GLS) [27] introduced an appropriate inter-particle interaction to the Watari’s model [28] to describe van der Waals fluids. The evolution equation of GLS model reads:
\nwhere the external force term Iki takes the following form:
\nIn a recent work [20], the GLS model was further developed to be a kinetic model which can be used to access both the hydrodynamic non-equilibrium and the thermodynamic non-equilibrium. To roughly and averagely estimate the derivation amplitude from the thermodynamic equilibrium, a TNE strength can be defined as
\nFigure 4 shows that the maximum value point can work as a physical criterion to discriminate the two stages, spinodal decomposition and domain growth, of phase separation. The TNE strength increases with time in the first stage while decreases with time in the second stage. More details are referred to the original publication [20].
\nEvolutions of the boundary length L and the TNE strength D for the phase separation process (see Ref. [20] for more details).
Rayleigh-Taylor instability (RTI) occurs at the interface between two fluids with different densities. The compressible RTI system can be described by [23, 24]
\nwhere aα is the acceleration due to external force.
\nGenerally, the depth of the mixing layer is an important parameter to measure the evolution of RTI. For incompressible RTI, the measurement is readily performed by tracing the constant density. However, for the compressible case, how to measure the mixing layer remains a thorny problem. Here we present two independent interface-tracking methods. One is by tracking the mean temperature of the upper and bottom fluids while the other is by tracking the maximum values of TNE characteristic quantities, such as \n
The perturbation amplitudes developing with time obtained by two different tracking approaches (see Ref. [23] for more details).
Figure 6 shows the profiles of physical quantities in the process where the shock wave passes outwards from the heavy medium to the light one. Figure 6(a) and (b) are for the case without and with initial perturbations at the interface, respectively. From left to right, one can find three kind of interfaces, the rarefaction wave, material interface and shock wave, which are indicated by dashed lines [25].
\nProfiles of physical quantities in the process of shock wave passing outwards from the heavy medium to the light one. (a) Without initial perturbations at the interface. (b) With initial sinusoidal perturbation at the interface (from Ref. [25] with permission).
According to the TNE information, the main feature of actual particle velocity distribution function can be qualitatively recovered. Figure 7 shows an example, where the interface is not perturbed initially. The details are referred to Ref. [25]. DBM simulations [25] show that the shear stress exists only for the oblique shocking. As a consistent correspondence, MD results [29] show that fluctuating shear stresses exist if observed in a scale with a few angstroms, while their mean value becomes negligibly small when being averaged over a scale with several hundred angstroms.
\nSketches of the actual distribution functions in velocity space (vr, vθ). Panels (a)–(c) show the recovered distribution functions at the rarefaction wave, the material interface, and the shock wave, respectively (from Ref. [25] with permission).
A comparison of the DBM results and those of the MD is shown in Figure 8. Figure 8(a) and (b) shows the DBM results for the cases where the interface is not and is perturbed initially, respectively. Figure 8(c) shows the shear stresses from MD simulation. Since the MD uses particle description, the existence of locally fluctuating shear stress corresponds to the observation in the case with perturbed interface; the observation that mean value of shear stresses becomes negligibly small in a much larger scale roughly correspond to the case with non-perturbed interface for the DBM simulations.
\nShear stress within shock wave. (a) DBM results for the case without initial interface perturbation. (b) DBM results for the case with initial interface perturbation. (c) MD results (figures (a) and (b) are from Ref. [25] with permission, figure (c) is from Ref. [29] with permission).
Figure 9 shows an example for that the TNE effects can be used to physically discriminate shock wave in plasma from those in common fluid. From the first two rows, the two TNE quantities, \n
TNE characteristics of three types of shock waves. The three rows, from top to bottom, correspond to pure shock wave (see Ref. [25] for more details), shock wave with chemical reaction (detonation) (see Ref. [21] for more details), and shock wave in plasma (see Ref. [30] for more details). The profiles on the left column show the values of \n\n\nΔ\n2\n∗\n\n\n around the wave fronts while the profiles on the right column show the values of \n\n\nΔ\n\n4\n,\n2\n\n∗\n\n\n.
Understanding compressible flows need more time. DBM presents a convenient way to model and simulate systems with trans-scale Knudsen number. Mathematically, the only difference of discrete Boltzmann from the traditional hydrodynamic modeling is that the NS equations are replaced by a discrete Boltzmann equation. But physically, this replacement has a significant gain: a DBM is roughly equivalent to a hydrodynamic model supplemented by a coarse-grained model of the TNE, where the hydrodynamic model can be and can also beyond the NS. The TNE provided by DBM has been used to investigate non-equilibrium effect during detonation process, to discriminate different stages of phase separation, to recover actual particle velocity distribution function qualitatively, to track the interfaces of different fluid, and to discriminate shock wave in plasma from those in common fluid. More use of those TNE quantities are further being discovered with the deeper investigation of the compressible and complex flows.
\nWe warmly thank Profs. Wei Kang, Zhihua Chen, Yanbiao Gan, Feng Chen, Chuandong Lin, Huilin Lai, Zhipeng Liu, Bo Yan, et al. for helpful discussions. We acknowledge support of the National Natural Science Foundation of China (under Grant Nos. 11475028 and 11772064), Science Challenge Project (under Grant No. JCKY2016212A501 and TZ2016002), and Science Foundation of Laboratory of Computational Physics.
\nZinc oxide (ZnO) material has been known as a semiconductor for over 70 years, with some of the first literature being reported as early as in 1944 [1]. It was never put to use like other semiconductors (GaN, Si) because it is difficult to dope. The past 19 years have seen a revival on the research and use of material because of new and emerging ways of doping it. The material is naturally n-type [1, 2, 3, 4], and by controlling the conditions of growth, the donor concentration can be controlled. The growth conditions include temperature, diethyl zinc (DEZ) reactant, O2 or H2O reactant, and pressure. P-type material [1, 2, 3, 4] is difficult to grow and tends to slowly revert back to n-type. Researchers [5, 6, 7, 8, 9, 10, 11, 12, 13, 14] who managed to deposit the p-type material have shown that it converts back to n-type within a few days. Maximum time period shown on p-type ZnO was a few months [5, 6, 7, 8, 9, 10, 11, 12, 13, 14].
\nZnO is a wide bandgap semiconductor [e.g., (0 K) = (3.441 ± 0.003) eV; (300 K) = (3.365 ± 0.005) eV]. It belongs to the group of IIb-VI compound semiconductors which crystalize exclusively in the hexagonal wurtzite-type structure. The lattice parameters of the wurtzite crystal structure are: a = 3.24 Å and c = 5.21 Å. Related to similar IIb-VI (e.g., CbS, CbSe, ZnSe, and ZnS) or III-V (e.g., AlSb, Bas, GaN, and InSb) semiconductors, it has comparatively strong polar binding and large exciton binding energy of (59.5 ± 0.5) meV. Its density is 5.6 g cm−3, a value which corresponds to 4.2 × 1022 ZnO molecules per cm−3 [1, 2].
\nZnO has practical advantages that make it an attractive semiconductor from an industrial point of view. It has low cost; is abundant, nontoxic, and transparent; has large excitonic binding energy of 60 meV; is soluble, compatible with intercellular material; and has wide and direct bandgap of 3.37 eV, making it highly sensitive. It is well known that semiconductors have a small bandgap which allows switching between conduction and off-states. The larger the bandgap, the better the semiconductor is able to switch states and insulate leakage currents. Bandgap affects sensitivity because a device that possesses a wider bandgap allows for higher currents to travel but also prevents leakage currents, which results in more sensitive and accurate readings. With low-temperature fabrication processes, high-quality devices can be fabricated using the conventional processing technology, thereby making it suitable for low-cost mass-production. It has potential applications in optoelectronics, transparent electronics, and spintronics. ZnO and its alloys have versatile electrical and optical properties for applications in thin film or nanowire transistors, light emitters, biosensors, and solar cells. The nanowire biosensor has a high surface-to-volume ratio, enabling real-time and label-free detection [1, 2, 3, 4, 15, 16, 17].
\nCurrently, the main commercial application for ZnO (and/or IGZO) material is in displays, with companies like Sharp and Samsung putting IGZO into mobile phone displays [18, 19, 20]. IGZO displays outperform other semiconductor displays such as amorphous silicon and organic semiconductors by providing improved resolution and reduced power consumption. This is possible because IGZO has a 20× to 50× times higher mobility than amorphous silicon and polymers, which allows for device scaling without affecting performance [18, 19, 20]. Higher mobility values can also be achieved with amorphous silicon technology, but it needs to be laser annealed which is expensive.
\nZnO films can be grown using three methods: gas transport (vapor phase deposition), hydrothermal synthesis, and/or melt process. Melt growth techniques are a problem due to high vapor pressure of ZnO. Growth using gas transport is difficult to control for large film layers and is normally used for bottom-up ZnO nanostructures. Hydrothermal synthesis is therefore preferred as a method of growth. Thin films can be produced through chemical vapor deposition, metalorganic vapor phase epitaxy, electrodeposition, pulsed laser deposition, sputtering, sol–gel synthesis, atomic layer deposition, spray pyrolysis, etc. All the mentioned techniques fall under hydrothermal synthesis, and one of the preferred methods is atomic layer deposition (ALD). The ALD process is capable of producing highly conformal and quality films [21]. The process is cyclic and is based on the number of reactants. Figure 1 shows that the ALD process for ZnO films is cyclic and depends on two reactants: metallization and oxidation.
\nSchematic diagram illustrating a single cycle of ZnO deposition using the ALD tool (A) metallisation and oxidation step, (B) Purge and pump step (C) Cleaning with O2 plasma step, (D) Removing non-used products with Ar step [22].
Metallization uses diethyl zinc (DEZ) as the zinc (Zn) metal precursor. Purge and pump steps are used to separate the execution of the reactants and to remove by-products. Before deposition, the wafer (substrate) is preheated at a temperature that will be used for deposition and it is also cleaned with O2 plasma so as to remove any polymer layer. During the metallization step, the DEZ (Zn (C2H5)2) is absorbed onto the surface of the wafer and the residual Zn (C2H5)2 is removed from chamber. “R” in Figure 1 represents C2H5.Then on another step, water or O2 is delivered to react with the absorbed DEZ [23, 24, 25]. These steps are executed separately, and to ensure this, purge steps are introduced in between the steps.
\nWhen water is used instead of O2 for oxidation, the process is called thermal ALD. This process tends to produce films similar to chemical vapor deposition (CVD) techniques [25, 26, 27]. When O2 is used instead of water, then the process needs plasma energy. Remote plasma atomic layer deposition (RPALD) is a fairly new process which is why it is still not in used. It is better than the other deposition techniques as it tends to produce films close to epitaxial layers. The layers are crystalline but tend to be nonuniform to the underlining layer which is why they are not called epitaxial layers. It is a process with great potential for depositing highly conformal and quality films. The process is better than thermal ALD in terms of conformity and quality, but both processes do not generally produce epitaxial layers due to nonuniformity to the underlining substrate. The plasma-assisted ALD method has the following advantages: reduction of OH impurity, allows more freedom in processing conditions, and provides wider range of material properties. The OH impurity is not desired as it affects the conductivity of the semiconductor and induces defects in the dielectrics.
\n\nTable 1 compares various growth techniques and how they affect NWFET output characteristics. Chemical vapor deposition (CVD) is the most popular technique for bottom-up nanowire processes. There are two growth techniques classified under CVD which are vapor–liquid–solid (VLS) and vapor–solid (VS) deposition techniques. CVD normally give the highest mobility as they produce crystalline wires with the only flaw being from the catalysts that guide the growth. VS produces better quality nanowires than VLS as it uses no catalysts but instead uses very high temperatures (>900°C). The problem with VS is that it is usually harder to control the size and morphology of the nanowires.
\nNo | \nProcessing route | \nSynthesis method | \nStarting materials | \nSynthesis temp. (°C) | \nMorphology | \nDiameter of ZnO nanostructure (mm) | \nLength of ZnO nanostructure | \nRef. | \n
---|---|---|---|---|---|---|---|---|
1 | \nVapor phase processing | \nThermal evaporation | \nZn metal, O2, and Ar | \n650–670 | \nNanowire | \n100 | \nSeveral microns | \n[29] | \n
2 | \nRoute | \n\n | Zn metal pellets, O2, Ar | \n900 | \nNanowire | \n20 | \n— | \n[30] | \n
3 | \n\n | \n | Zn powder, O2, Ar | \n600 | \nNanowire | \n80 | \n1 μm | \n[31] | \n
4 | \n\n | Vapor phase transport | \nZnO powder, graphite, Cu catalyst | \n930 | \nHierarchical dendrite | \n60–800 | \n— | \n[32] | \n
5 | \n\n | Aerosol | \nZn powder, N2 gas | \n500–750 | \nFiber-mat | \n100–300 | \n— | \n[33] | \n
6 | \n\n | \n | \n | \n | Cauliflower | \n20–30 | \n— | \n\n |
7 | \n\n | RF sputtering | \nZnO deposited over Pt sputtered interdigitated alumina substrate | \n— | \nNanobelt | \n— | \nFew micrometer | \n[34] | \n
8 | \n\n | Molecular beam epitaxy | \nZn metal, O3/O2 plasma discharge, Au coated substrate | \n600 | \nNanorod | \n50–150 | \n2–10 μm | \n[35] | \n
9 | \nSolid-state processing | \nCarbothermal reduction | \nZnO powder, graphite powder, Ar gas flow, Au coated silicon substrate | \n900–925 | \nNanowire | \n80–120 | \n10–20 μm | \n[36, 37] | \n
10 | \nRoute | \nSolid-state Chemical | \nZnCl2, NaOH, polyethylene Glycol, Na2WO4.2H2O | \nRT | \nNanorod | \n40–60 | \n200 nm | \n[38] | \n
11 | \n\n | Reaction | \n\n | \n | \n | 20–40 | \n100 nm | \n\n |
12 | \nWet processing | \nHydrothermal | \nZnAc2, NaOH, absolute ethanol, distilled water | \n180 | \nNanorod | \n— | \n— | \n[39] | \n
13 | \nRoute | \n\n | Zn(CH3COO)2·2H2O, C6H8O7·H2O, absolute ethanol, distilled water | \n400 | \nNanorod (vertically aligned) | \n50 | \n500 nm | \n[40] | \n
14 | \n\n | \n | Zn(NO3)2·6H2O, NaOH, cetyltrimethyl ammonium bromide, ethanol | \n120 | \nNanorod | \n— | \n— | \n[41] | \n
15 | \n\n | \n | Zn(NO3)2·6H2O, NaOH, cyclohexylamine, ethanol, water | \n200 | \nNanorod | \n150–200 | \n2 μm | \n[42] | \n
16 | \n\n | \n | Zn(SO4)·7H2O, NH4OH, deionized water | \n75–95 | \nNanorod | \n— | \n— | \n[43] | \n
17 | \n\n | ALD | \nDEZ (Zn (C2H5)2), H2O | \n— | \nNanowire | \n70–100 | \n5 μm | \n[44] | \n
18 | \n\n | Plasma ALD | \nDEZ (Zn (C2H5)2), O2\n | \n150–190 | \nNanowire | \n36–100 | \n2–20 μm | \n[22] | \n
Summary of various methods used for the production of 1-D ZnO nanostructures, adopted from [28].
\nTable 1 also shows that atomic layer deposition (ALD) is an attractive technique because it deposits high quality films at low temperatures between 120 and 210°C [22, 45]. The problem with ALD is that it has only this window for good quality conducting films. At temperatures below 120°C, the deposition can be incomplete or experience condensation depending on growth rate. At temperatures above 210°C, the deposition tends to experience desorption or it decomposes toward CVD deposition. Nonetheless, it is one of the best techniques toward growing films close to epitaxial growth (crystallinity is achievable whereas uniformity is still difficult to achieve) [22, 45]. The tool has shown potential by achieving high values of field effect mobility >30 cm2/Vs with excellent crystallinity.
\nThere are three types of defects in a crystal lattice: point defects, area defects, and volume defects. Point defects which are caused by native elements and impurities are the major problem for ZnO semiconductor. Native point defects for ZnO include the following: zinc interstitial (Zni), zinc antisite (Zno), zinc vacancy (VZn), oxygen interstitial (Oi), oxygen antisite (OZn), and oxygen vacancy (Vo). Over the years, a lot of research advocated them as the major cause for the n-type behavior. Oxygen defects are seen as the main contributors toward the n-type behavior [3, 15]. There are some researchers [1, 2, 3, 4] who hypothesize that impurities (not the native point defects) are the main cause of the n-type behavior because they tend to be shallow donors whereas Zn and O2 defects tend to be deep donors [1, 2, 3, 4]. The two theories have not been proven so currently the main cause of the natural n-type behavior of ZnO [1, 2, 3, 4] is not certain.
\nZnO impurities (foreign atoms) are normally incorporated in the crystal structure of the semiconductor. There are two reasons of impurity incorporation: they can either be unintentionally introduced due to lack of control during growth processes or they are intentionally added to increase the number of free carriers in the semiconductor. Impurities in the ZnO should have the ability to be ionized; which is desirable as it increases conductivity. This means that the impurity atoms should be able to give off electrons to the conduction band. If the impurities were acceptors—they should be able to give off holes to the valence band [3, 16].
\nDonor Impurities for the n-type ZnO can either be shallow or deep. Figure 2 shows shallow donors compared to deep donors. Shallow impurities require little energy to ionize (this is energy typically around the thermal energy or less). These donor impurities possess energy close to the band edge—the extra valence electron of these impurities are loosely bound and occupy effective-mass states near the conduction band maximum- CBM- at low temperatures. Deep impurities on-the-other-hand require energy greater that the thermal energy to ionize. These donor impurities possess energy far from the band edge (CBM) making them very hard to ionize. Their presence within the semiconductor tends to contribute only a small fraction of free carriers. Deep donors are also called traps because they act as effective recombination centers in which electrons and holes fall and annihilate each other. Grain boundaries (GB) are main source of deep state impurities and they adversely affect transistor performance. ZnO is a wide bandgap material and research suggests [3, 4, 16] that there exist possible deep-level traps in GBs. The examples of deep donors are Zn and O ions. Zn acts as a deep donor when there is a vacancy and O acts as a deep donor in any defect state. An example of a shallow donor is the H ion.
\nShallow versus deep donors [1, 2, 3, 4].
There are four main methods capable of producing nanometer features using top-down approaches: UV stepper lithography, e-beam lithography [46], focused ion-beam lithography [47], and spacer method [45, 48]. UV lithography is the standard industrial method for fabricating nanodevices. E-beam and focused ion-beam lithography are often used and can pattern devices down to 5 nm, but the equipment is very expensive and the pattern writing is very slow. These two instruments resemble scanning electron microscope (SEM) in terms of operation. Whereas SEM is used to focus a beam of electrons to image samples within a chamber, these instruments are used to create patterns on the samples. The difference between e-beam and focused ion-beam is that the latter uses an ion beam to pattern wafers and hence does not require photoresist. Their advantage over optical UV lithography is the small features they reach. For low-cost applications such as biosensors, the problem with these two methods is that they are expensive.
\nThe spacer technique is a low-cost fabrication method for fabricating nanowires. It was first reported in 2005 by Ge et al. [49], and other researchers [44, 50, 51] have since carried it forward. The technique has great potential in shaping nanometer features using conventional, low-cost photolithography. Figure 3 shows the concept of the spacer technique. It uses first anisotropic etch to create a vertical pillar on an insulating layer (SiO2), then after deposition of a semiconductor layer (ZnO) and a second anisotropic etch, to create nanowires made up of the semiconductor layer. This method allows nanowire features with controllable dimensions to be developed. The ICP tool is usually used for anisotropic etching and produces surface roughness <1.5 nm. Other tools such as RIE and ion beam etch produce roughness >5 nm. The fabrication process for the complete ZnO NWFET structure is as outlined in [52].
\nNovel spacer technique used to pattern nanowire features. Cross-sectional schematic of nanowire formation (a) before dry etch and (b) after dry etch [22].
The ZnO field-effect transistor (FET) has been around for decades. The success of the device in meeting the technological demands has largely been dominated by the shrinking size of its physical geometry. It has an advantage as a junctionless (no p-n junctions) FET compared to conventional FETs [17, 21, 23, 24, 25, 26, 27, 53, 54]. There has been an introduction of new materials and heterojunction structures developed so as to move away from conventional silicon devices. High-K dielectrics have been introduced to replace the conventional SiO2 which should help maintain acceptable dielectric thicknesses while keeping gate leakage currents low [17, 21, 23, 24, 25, 26, 27, 53, 54].
\nEven with so many improvements being made to the device, the limits of FET scaling are approaching. The thickness of the oxide (tox) cannot be less than 1 nm due to high tunneling current and significant operational variation. The substrate doping is also very high which creates leakage and tunneling currents that are unacceptable to device operation.
\nTFTs have also been fabricated using ZnO, mainly as thin film transistors for application in displays. Figure 4 compares 20 ZnO TFTs fabricated by different authors [27, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71] using a variety of fabrication methods over the last 5 years. The graph is a plot of field effect mobility versus subthreshold slope which are two of the main parameters that describe the performance and efficiency of a device. The best device was fabricated by Bayraktaroglu et al. [70] with a SiO2 insulator and pulsed laser-deposited ZnO active channel layer. The device had a field effect mobility 110 cm2/Vs and an excellent subthreshold gate voltage swing of 109 mV/decade. This value of mobility is much higher than the value of around 1 cm2/Vs that is typically achieved with amorphous silicon TFTs in production displays. It is clear therefore that ZnO TFTs have considerable potential for application in high performance displays.
\nGeneral literature review on TFTs looking at field effect mobility versus subthreshold slope of as-deposited and doped ZnO films.
Emerging nonplanar devices [17, 21] are being researched to prolong the future progress for FETs. Devices based on quasi-one-dimensional (1-D) nanostructures are still at an embryonic stage from an industrial point of view. These nanostructures include the following: nanowires, nanobelts, nanoribbons, and nanoneedles [72, 73]. This review is interested in nanowire FETs which are also being researched for application in biosensors because the high surface-to-volume ratio provides high sensitivity.
\n\nFigure 5 compares 15 different ZnO NWFETs fabricated by different authors using a variety of methods [22, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]. The graph is plotted with field effect mobility against the subthreshold slope, which are two important device parameters that determine ZnO NWFET performance. The nanowires were fabricated using top-down and bottom-up (self-assembled) processes. Self-assembled processes tend to display very high field effect mobility which is normally above 200 cm2/Vs; whereas the top-down have lower mobility values. Most of the top-down fabricated devices have mobility <1.0 cm2/Vs with around three papers giving a mobility >10.0 cm2/Vs. The difference in the mobility may be due to the fact that self-assembled nanowires are single-crystal, whereas top-down nanowires are polycrystalline. Nonetheless, top-down techniques are desirable as they currently pave way for mass production and will be pursued in this research investigation.
\nLiterature review on nanowire FETs looking at field effect mobility versus subthreshold slope of as-deposited and doped ZnO nanowires.
A biosensor is defined by the International Union of Pure and Applied Chemistry (IUPAC) as “a self-contained integrated device that is capable of providing specific quantitative or semiquantitative analytical information using a biological recognition element (biochemical receptor), which is retained in contact direct with a transduction element” [87]. A biosensor is a “more-than-Moore device” because it incorporates functionalities that do not necessarily scale according to Moore’s law. Under the roadmap, the device falls under the category of sensors and actuators. Other categories include analogue/RF, passives, HV power, and biochips [88, 89].
\n\nFigure 6 shows a typical structure of a biosensor [90, 91, 92]. The biomolecules are contained within an analytic solution and attach themselves to immobilized enzymes or immune-agents on the linkers. Linkers in turn are attached to the transducer. The transducer then converts the charge on the analyte into an electrical signal which is then transmitted for data processing. Biosensors can be considered as part of the research field known as “chemical sensors” in that a biological mechanism is used for analyte detection within an analyte solution [93, 94, 95]. Quasi-one-dimensional nanostructures have a greater surface-to-volume ratio compared to planar structures and are therefore expected to be more sensitive than planar sensors [93, 94, 95].
\nTypical structure of a biosensor. The biomolecules are contained within an analytic solution and attach themselves to immobilized enzymes or immune-agents on the receptors. The transducer then converts the energy signal produced into an electrical signal which is then transmitted for data processing. [22].
Nanowires are the same as nanorods. The words can be used interchangeably [80]. These have received enormous attention due to their suitable properties for designing novel nanoscale biosensors. For example, the dimensions of ∼1–100 nm are similar to those of many biological entities, such as nucleic acids, proteins, viruses, and cells [79]. In addition, the high surface-to-volume ratios for nanomaterials allow a large proportion of atoms in the bio-analyte to be located at or close to the surface. Moreover, some nanowire materials have surfaces that can easily be chemically modified which makes them significant candidates for biosensors [79, 80]. There are a number of nanostructure-based electrical biosensors which include single-wall carbon nanotubes (SWCNT), nanowires, nanogaps, nanochannels, and nano-electromechanical (NEM) devices. The project will focus on nanowire-based devices as they have considerable potential for electrical biosensing that offer the possibility of portable assays in a variety of point-of-care environments [48, 90, 96].
\nOver the past decade, silicon nanowires have been the most researched for application as biochemical sensors [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108]. Silicon nanowires are of interest for a number of reasons, for example, the material is well known and is compatible with CMOS integrated circuits for the development of sensor systems [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108]. The nanowire is expected to have high surface-to-volume ratios which give high sensitivity and the electrical sensing will give real-time label-free detection without the use of expensive optical components. Mass manufacturing is also a main advantage for silicon and is critically important for nanowire biosensor applications because of the widespread uptake of biosensors in “point-of-care” settings, the biosensor needs to be disposable [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108].
\nA number of fabrication methods are well established for silicon nanowires which utilize both bottom-up and top-down methods (these methods are called hybrids). It still remains that bottom-up techniques have the advantage of simplicity [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108]. Bottom-up methods are still limited due to the alignment problem. The hybrid methods require further nanowire technologies to achieve alignment, such as electric field or fluid-flow-assisted nanowire positioning to locate the nanowires between lithographically defined source and drain electrodes. The technique is interpreted as a hybrid between bottom-up and top-down. Top-down methods overcome these problems, and several researchers have used advanced lithography techniques to fabricate single-crystal silicon nanowires on silicon-on-insulator (SOI) substrates. SOI wafers are expensive and to overcome the problem some researchers [109] have devised alternatives to SOI. The electrical output characteristics of silicon nanowires are good and they are well suited for biosensing applications. The sensitivity range for most silicon-nanowire based biosensors is between 50 and 400 mV [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134].
\nZnO is investigated as it is expected to be more sensitive than Si due to its wider bandgap [109]. This is observed by comparing Table 2 with Table 3. ZnO devices show results comparable to silicon devices; especially looking at response time and limit of detection. It is required that biosensors should have the liquid reference electrode. There are many different types of ZnO nanostructures being used for sensing application and Table 2 compares the ZnO nanostructures such as nanotetrapods, nanocombs, and nanorods used for biosensing [110, 121]. Nanotetrapods [123] are like nanorods but with four single crystalline legs. Most of the ZnO devices were synthesized by vapor phase method and then transferred on Au electrode to form a multiterminal network for the sensor receptors. Like all other bottom-up ZnO nanostructures discussed here, they are transferred to a surface of a working electrode to form a thin layer to modify the transducer. The devices have low sensitivity but the nanotetrapods exhibit good detection limit down to ~1.0 nM. The researchers [123] did not explain why the nanostructures possess low sensitivity but its three-dimensional features have the potential for multiterminal communication applications [123].
\nNo. | \nReference electrode | \nType of sensor | \nChannel material | \nZnO fabrication process | \nLOD (μM) | \nResponse time (s) | \nRef. | \n
---|---|---|---|---|---|---|---|
1 | \nAu | \nBiosensor | \nZnO nanorod array | \nHydrothermal | \n10 | \n<5 | \n[111] | \n
2 | \nITO | \nBiosensor | \nZnO nanotube array | \nHydrothermal/chemical | \n10 | \n<6 | \n[112] | \n
3 | \nAu | \nBiosensor | \nTetrapod-like ZnO | \nCVD | \n4 | \n6 | \n[113] | \n
4 | \nGlass capillary | \nBiosensor | \nZnO nanoflakes | \nHydrothermal | \n0.5 | \n<4 | \n[114] | \n
5 | \nGCE | \nBiosensor | \nFork-like ZnO | \nAnnealing | \n0.3 | \n3 | \n[115] | \n
6 | \nAu | \nBiosensor | \nComb-like ZnO | \nCVD | \n20 | \n<10 | \n[116] | \n
7 | \nTi | \nBiosensor | \nZnO/C nanorod array | \nHydrothermal | \n1 | \n4 | \n[117] | \n
8 | \nITO | \nBiosensor | \nZnO/Cu array matrix | \nHydrothermal | \n40 | \n<6 | \n[118] | \n
9 | \nGCE | \nBiosensor | \nZnO/Au nanorods | \nHydrothermal | \n0.01 | \n<5 | \n[119] | \n
10 | \nPt | \nBiosensor | \nZnO/NiO nanorods | \nHydrothermal | \n2.5 | \n<5 | \n[120] | \n
Summary of characteristics for various 1-D ZnO biosensors, adopted from [110].
No. | \nReference electrode | \nType of sensor | \nChannel material | \nZnO fabrication process | \nLOD (pM) | \nResponse time (s) | \nRef. | \n
---|---|---|---|---|---|---|---|
1 | \nNo reference electrode | \nBiosensor | \nSi NW | \nnanocluster-mediated vapor–liquid–solid growth method | \n10 | \n<10 | \n[97] | \n
2 | \nAu | \nBiosensor | \nSi NW | \nChemical vapor deposition | \n0.002 | \n<10 | \n[98] | \n
3 | \nPlatinum wire | \nBiosensor | \nSi NW | \nSNAP technique | \n10 | \n<10 | \n[101] | \n
4 | \nNone | \nBiosensor | \nSi NW | \nReactive-ion etching (RIE) | \n0.01 | \n<10 | \n[106] | \n
5 | \nNone | \nBiosensor | \nSi NW | \nSynthesized by chemical vapor deposition | \n100 | \n<10 | \n[122] | \n
Summary of characteristics for various 1-D Si biosensors, adopted from [121].
In nanocombs [116] design, each comb has between 3 and 10 rods connected to one another by a single rod. ZnO nanocombs were used as the channel for sensing glucose [116] and as label-free uric acid biosensor based on uricase [124]. The functionalized ZnO nanorods showed thermal stability, anti-interference capability, and direct electron transfer (DET) between enzyme electroactive sites and external electrodes. The activity of the enzyme and the sensitivity can be increased by introducing a lipid film between the channel and the enzyme. Another uric acid biosensor [125] example is based on uricase-functionalized ZnO nanoflakes, which was hydrothermally prepared at low temperatures on Au-coated glass. The sensor produced a sensitivity based on subthreshold slope of ~66 mV/decade. Bottom-up ZnO nanorods [126] were also used as lactate oxidase (LOD) biosensor using glutaraldehyde cross-linkers. The device had a subthreshold sensitivity of ~41 mV/decade, with maximum detection of 0.1 μM. To test for cholesterol, porous ZnO mirco-tubes [127] were constructed using 3-D assembled porous flakes. ZnO nanorods [128] were grown on Ag electrode to make a cholesterol sensor.
\nMost researchers use bottom-up approaches to fabricate the ZnO biosensors because of the straightforward synthesis process. However, these bottom-up devices have variable electrical performance due to the lack of geometrical dimension control and addressing the nanostructures for sensing application. So far, there is limited research reported on top-down ZnO biosensors, and previous work demonstrated the viability of top-down ZnO NWFET for biosensor applications. In the work, however, there was no passivation layer on the ZnO nanowires, which led to the dissolution of the material. This made the device unstable and the sensing results were not reproducible. There exists a need to develop a passivating layer technology and optimize the fabrication process for biosensor applications. That way, a reliable measurement of sensitivity for the nonspecific and specific sensing of lysozyme and bovine serum albumin (BSA) can be achieved.
\nN.M.J. Ditshego would like to acknowledge the Botswana International University of Science and Technology (BIUST) for supporting his doctoral studies and the Southampton Nanofabrication Centre for the experimental work. The author would like to acknowledge the EPSRC EP/K502327/1 grant support.
\n"I work with IntechOpen for a number of reasons: their professionalism, their mission in support of Open Access publishing, and the quality of their peer-reviewed publications, but also because they believe in equality. Throughout the world, we are seeing progress in attracting, retaining, and promoting women in STEMM. IntechOpen are certainly supporting this work globally by empowering all scientists and ensuring that women are encouraged and enabled to publish and take leading roles within the scientific community." Dr. Catrin Rutland, University of Nottingham, UK
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\\n\\nInterested? Contact Ana Pantar (book.idea@intechopen.com) for more information.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'We have more than a decade of experience in Open Access publishing. The advantages of publishing with IntechOpen include:
\n\nOur platform – IntechOpen is the world’s leading publisher of OA books, built by scientists, for scientists.
\n\nOur reputation – Everything we publish goes through a two-stage peer review process. We’re proud to count Nobel laureates among our esteemed authors. We meet European Commission standards for funding, and the research we’ve published has been funded by the Bill and Melinda Gates Foundation and the Wellcome Trust, among others. IntechOpen is a member of all relevant trade associations (including the STM Association and the Association of Learned and Professional Society Publishers) and has a selection of books indexed in Web of Science's Book Citation Index.
\n\nOur expertise – We’ve published more than 4,500 books by more than 118,000 authors and editors.
\n\nOur reach – Our books have more than 130 million downloads and more than 146,150 Web of Science citations. We increase citations via indexing in all the major databases, including the Book Citation Index at Web of Science and Google Scholar.
\n\nOur services – The support we offer our authors and editors is second to none. Each book in our program receives the following:
\n\nOur end-to-end publishing service frees our authors and editors to focus on what matters: research. We empower them to shape their fields and connect with the global scientific community.
\n\n"In developing countries until now, advancement in science has been very limited, because insufficient economic resources are dedicated to science and education. These limitations are more marked when the scientists are women. In order to develop science in the poorest countries and decrease the gender gap that exists in scientific fields, Open Access networks like IntechOpen are essential. Free access to scientific research could contribute to ameliorating difficult life conditions and breaking down barriers." Marquidia Pacheco, National Institute for Nuclear Research (ININ), Mexico
\n\nInterested? Contact Ana Pantar (book.idea@intechopen.com) for more information.
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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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