Various inorganic and organic species detected at carbon nanotubes and nanofibers and silver/copper nanoparticles-modified carbon nanotubes electrode materials.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tSkeletal muscle is the most abundant tissue in the body comprising 30 to 40% of your total mass. Their main function is to enable the human body to produce the force needed to move and function. A large series of pathological conditions can affect skeletal muscle, from mild injuries to serious or even life-threatening myopathies. These latter include skeletal muscle dystrophies which encompass a heterogeneous group of more than 30 distinct pathologies causing progressive and irreversible degeneration of skeletal muscle fibers. The causes are genetic; hence, a family with a history of muscular dystrophy increases the chance of having a child affected by the disorder. Diagnosis of inherited myopathies is frequently challenging due to broad genetic and phenotypic heterogeneity. As if that weren't enough, the prognosis is often, if not always, poor for the majority of these conditions since there are no treatments available. Currently available treatments can only help manage and reduce the severity of symptoms. However, the recent technologies led to the identification of responsible genes causing these conditions thus leading to an improvement in the diagnosis and the clinical course of patients. Therefore, a better understanding of the pathogenesis of the disease is pivotal for the development of individual therapies. In this book, thanks to the contribution of all the authors, we aim at gathering information about the latest discoveries and novel research trends concerning technologies and methods underpinning diagnostic and therapeutic advancements in genetic rare orphan diseases.
\r\n\t
Nowadays, the introduction of nanomaterials within sensors composition has been shown a growing interest because they offer exciting new opportunities in the field of sensing based on their fascinating properties.
Nanostructured carbon in forms of carbon nanotubes and carbon nanofibers represents the most commonly studied nanostructured materials in sensor obtaining. Single-wall carbon nanotubes and multi-wall carbon nanotubes forms of carbon nanotubes have been studied more extensively in comparison with carbon nanofibers for the sensing applications based on their more sensitive properties. However, both forms of nanostructured carbon exhibit great potential for use as unmodified or modified electrode for the detection applications [1,2].
It is well-known that the carbon based sensors/electrodes are widely used in electroanalytical applications because of the properties of the carbon materials,
Several detection applications using carbon nanotubes and nanofibers and silver/copper nanoparticles-modified carbon nanotubes electrodes are summarized in Table 1.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Carbon nanotubes | \n\t\t\tH2O2\n\t\t\t\t Bi (III) As(III), Pb (II) Valocyclovir Carbohydrates Phenol derivates Butylparaben | \n\t\t\t[6] [7] [43] [10] [12], [13] [14], [15], [16] [18] | \n\t\t
Carbon nanofibers | \n\t\t\tH2O2\n\t\t\t\t Phenol derivates | \n\t\t\t[2] [17] | \n\t\t
Silver-modiefied carbon nanotubes | \n\t\t\tIbuprofen Dopamine, ascorbic acid | \n\t\t\t[23] [26] | \n\t\t
Copper-modified carbon nanotubes | \n\t\t\tGlucose Ammonia | \n\t\t\t[22], [24] [25] | \n\t\t
Various inorganic and organic species detected at carbon nanotubes and nanofibers and silver/copper nanoparticles-modified carbon nanotubes electrode materials.
This chapter is focused to present the unmodified/modified nanostructured carbon composite electrode correlated with different types of electrochemical techniques, i.e., cyclic voltammetry (CV), differential-pulsed voltammetry (DPV), square-wave voltammetry (SWV), chronoamperometry (CA) and pulsed amperometry (PA), applied for sensing application as voltammetric/amperometric sensors. The electrochemical detection performance is directly linked to the electrochemical properties of electrode surface in conjunction with the specific electrochemical techniques. The electroanalytical performance for voltammetric/amperometric detection applications of the nanostructured carbon composite (NCC) and metallic nanoparticle-modified nanostructured carbon composite (M-NCC) electrodes,
Taking into consideration the main advantage of the composite electrode due to the specific behavior of ensemble/array of microelectrodes, which are characterized by significant higher useful currents due to the large surface area [27], the nanostructured carbon-based composite (NCC) electrodes represent the new generation with the enhanced properties. The most representative and often used of the nanostructured carbon composite materials are nanofibers and nanotubes [21, 28]. Also, metallic nanoparticles decorated on the composite electrode have been studied for various applications [22, 23].
Various detection and sensing applications of NCC electrodes have been extensively studied due to their interesting mechanical, electrical and electrochemical properties [4, 5, 9, 15, 29].
A major importance is given by the composite electrode obtaining, which is responsible for the NCC electrode properties and further detection/sensing applications.
The development of composites electrodes consisting of conductive nanostructured carbon phases dispersed within polymeric matrices has led to important advances in analytical electrochemistry and sensor devices. These new electrode materials combine the electrical properties of carbon with the ease of processing of resins and show attractive electrochemical, physical, mechanical, and economical features [19, 20, 30, 31]. The application of metallic nanoparticles has been shown a raised interest in the field of electrochemistry, in particular in electroanalysis due to their enhanced optical, electronic, magnetic and catalytic properties. Also, in terms of electroanalysis the metallic nanoparticles materials are appropriate to modify electrodes due to several advantages,
Based on the composite classification, the composite electrodes discussed in this chapter belong to randomized ensembles, in which nanostructured carbon is mixed with epoxy resin, after its dispersion in a suitable solvent (Figure 1). Tetrahydrofuran (THF) and N, N-dimethylformamide (DMF) are efficient solvents for a good dispersion of carbon nanofibers (CNFs) and carbon nanotubes (CNTs) to avoid their agglomeration [15, 22, 23]. Various obtaining methods have been developed to deposit metal nanoparticles on various substrates to obtain so-called chemically modified electrodes (CMEs) characterized by enhanced properties for detection/sensing applications. The most common metal nanoparticles used are copper, silver, gold, and platinum, and as well as the most frequently methods encountered in literature are in respect to the pathway of metallic nanoparticles deposition. Thus, the chemical synthesis is referred at the reduction with different reagents, UV light or electron-beam irradiation [35-37]. Also, the deposition of metallic nanoparticles can be performed by electrochemical techniques [38]. The electrochemical deposition method provides an easy and rapid alternative for the preparation of metallic nanoparticles based electrodes within a short period of time. In addition, this technique present some advances over chemical method such as high purity of the particles, higher control over the dimension, lower particle size distribution, more control over the density [39-41].
Schematic diagram of M-NCC-electrode preparation; metallic nanoparticles obtaining by electrodeposition.
Another main aspect is to distribute nanostructured carbon within epoxy matrix to reach the composite electrodes. Scanning electron miscroscopy (SEM) technique is very useful to evaluate the dispersion of the carbon filler within epoxy, which is responsible by the surface morphology and the structure and as consequence, the electrical and electrochemical properties of the NCC electrode (Figure 2). Also, the metallic nanoparticles can be observed and measured by this technique. Using two-roll mill procedure to obtain NCC electrode is very simple and assures a good dispersion and homogeneous distribution of nanostructured carbon filler within epoxy matrix (Figure 2 a, b). The electrochemical techniques used for the electrodeposition influenced also, the nanoparticles size and distribution on the conductive carbon filler. Thus, by maintaining the electrode potential at a certain value, e.g.,-0.4 V/SCE for Ag in AgNO3 supporting electrolyte, which is assured by chronoamperometry (CA) technique (described in subchapter 3.1), a spontaneous formation of silver nanoparticles on NCC surface occurred, which are deposited in aggregated form. Ag particles are distributed randomly on the electrode surface and characterized by various sizes (see Figure 2c-e). Better distribution and homogeneous sizes of Ag nanoparticles are achieved if cyclic voltammetry (CV) (described in subchapter 3.4) is applied by succesive running within a certain potential range (Figure 2 f).
The electrode surface morphology, structure and nanoparticles sizes influence the electrical and electrochemical properties of the NCC electrode envisaging their detection applications.
SEM image of NCC and M-NCC electrode surfaces: a)-CNT; b)-CNF; c)-AgCNT; d)-AgCNF; e)-CuCNT; f)-CuCNF.
The electrical behavior of the NCC composite is an important property for the electrochemical detection application. For the composite electrode, the electrical property is given by the conductive filler loading within the insulating matrix and there is a percolation threshold loading beyond there is electrical conductivity. At low filler loading, the conductivity is very close to the pure insulating matrix conductivity because the fillers are dispersed individually or as small clusters in the matrix. Above the percolation threshold loading, independent conductive fillers tend to link together to form conductive networks, which leads to a significant increase of the electrical conductivity of the composite. Various percolation thresholds for carbon nanotubes-based composites have been reported by various research groups, from 0.0025 %, wt. for carbon nanotubes-epoxy composites up to 5%, wt. [42]. Above this loading, the electrical conductivity increased very much until the carbon nanotubes reached 20%, wt. and beyond this loading no significant increasing of the electrical conductivity occurred.
In general, the electrical conductivity is measured by four-point probe resistance measurements (FPP), and some values for various NCC electrode surface are given in Table 2.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
CNT | \n\t\t\t0.596 | \n\t\t
CNF | \n\t\t\t0.247 | \n\t\t
AgCNT | \n\t\t\t0.602 | \n\t\t
AgCNF | \n\t\t\t0.320 | \n\t\t
CuCNT | \n\t\t\t0.605 | \n\t\t
CuCNF | \n\t\t\t0.319 | \n\t\t
The electrical conductivity of several NCC and M-NCC electrode materials.
The presence of metallic nanoparticles enhanced slightly the electrical conductivity for the electrode materials. All electrode materials exhibit the electrical conductivity suitable for the electrochemical applications.
The electrochemical properties related to the electroactive surface area are studied by cyclic voltammetry (CV). CV is a very versatile electrochemical technique that allows to identify the aspects regarding the mechanism of redox system and transport properties of an electroactive species in solution. This technique is the first electrochemical experiment performed in order to characterize an electrode material for every type of application. This technique provides rapid information on the thermodynamic redox processes, on the kinetics of heterogeneous electron-transfer reactions, and on coupled chemical reactions or adsorption processes. This is accomplished with a three-electrode arrangement whereby the potential relative to some reference electrode is scanned at a working electrode, while the resulting current flowing through a counter (or auxiliary) electrode is monitored in a supporting electrolyte. The technique is ideally suited for a quick search of redox couples present in a system, and once located a couple may be characterized by more careful analysis of the cyclic voltammogram. Usually, the potential is scanned back and forward linearly with time between two extreme values using a triangular potential waveform.
The most common method to determine the electroactive surface area is the ferro/ferricyanide redox couple method [15]. Ferro/ferricyanide redox system gives rise to a reversible redox system that involves one electron per molecule. Given the one electron involving and chemical reversibility of the redox system, the cyclic voltammetric analysis of the ferro/ferricyanide process envisaged the determination of the electroactive area of the NCC and M-NCC electrodes through the apparent diffusion coefficient of this redox system on these electrodes, calculated based on the Randles–Sevcik equation:
Where n is the number of electrons involved in the redox process, A is the active area of the working electrode (cm2), D is the diffusion coefficient (cm2 s–1), C* is the bulk concentration of the electroactive species (mol cm–3), and v is the potential scan rate (V s–1).
Thus, using cyclic voltammetry recorded at different scan rate in the presence of 4 mM K3Fe(CN)6, the electrochemical behavior of ferrocyanide system is studied after CV recording (Figure 3), which offers the opportunity to determine the characteristics of a cyclic voltammetric response originating from a reversible process. The reversibility of the system was estimated by the peak-to-peak separation (the separation between the anodic peak potential and the catholic peak potential, ΔEp=Epa – Epc). For a reversible couple the ΔEp is egual to 0.059/n V (n, the number of electron exchanged in the reaction, and in this case is equal with 1), and it is independent of the scan rate. For quasi-and irreversible conditions, the ΔEp depends on the voltage scan rate. Another important parameter that is relates to the electrochemical reversibility of an electrode reaction is the peak current, and more specific, the ratio between the anodic peak current and the cathodic peak one (ipa/ipc), whose value is unity for a simple reversible couple.
Cyclic voltammograms recorded at: a) CNT and b) Ag-CNT electrodes in the presence of 4 mM K3Fe(CN)6 and 1M KNO3 supporting electrolyte; potential scan rate: 1-0.025, 2-0.05, 3-0.1, 4-0.2, 5-0.3 Vs-1; potential range:-1 ÷+1.5V/SCE.
By calculating diffusion coefficient based on the recorded voltammograms and comparing with theoretical diffusion coefficient (6.7x10-6 cm2s-1), the values of the electroactive surface area can be determined. In general for all NCC and M-NCC the electroactive surface area are higher in comparison with the geometrical one. In addition, based on the reversibility parameters values, ΔEp and ipa/ipc, some aspects regarding diffusion and adsorption process on the electrode surface can be elucidated, which must be correlated with the morphological, structural and electrical properties.
Also, other important aspects related to the electrochemical behaviour very important for electroanalysis are the potential window and the background currents. A large potential window and low background current are desired for the electrochemical detection applications. The background current is given by the capacitive component of the electrical double layer and it is desired that this component to be minimized. However, it is well-known that the electrode characterized by the electrocatalytic activity is characterized by high background current and low potential value for the oxygen evolution, which denotes a narrow potential window. Though, the electrodes characterized by the electrocatalytic activity are very useful for the detection application especial for the hard oxidizable or reduction species. In general, nanostructured carbon-based composite electrodes are characterized by low background current and large potential window (Figure 4a). However, it can be noticed that CNT exhibits higher background current and lower potential value for oxygen evolution, which can be attributed to a possible electrocatalytic effect towards this process. Metallic modified-carbon composite electrode exhibits the electrocatalytic activity towards oxygen evolution process, being characterized by higher background current and lower potential window (Figure 4b).
This technique is most useful technique to start the study of the detection applications for each type of the electrode material due to it provides information about the oxidation or reduction potential value, very important to optimize and simplify the detection procedure envisaging the batch injection analysis using chronoamperometry technique (BIA-CA), the easiest detection procedure. The information related to the electrochemical behaviours of NCC and M-NCC electrodes in the presence of various target analytes can be achieved by this technique, several examples will be given bellow. Also, some aspects about the mechanism elucidation related to the process control and adsorption aspects that are very useful for the detection application are provided by this technique operated at different scan rates.
The oxidation or reduction processes of analytes on NCC and M-NCC electrodes are often very complex processes, involving both the adsorption of the reactant/intermediate or oxidation/reduction products and the formation of passive, nonconductive layer on their surface by electropolymerization or adsorption. The mechanistic aspects of the overall oxidation or reduction processes of target analyte on the electrode surface are elucidated using the cyclic voltammograms recorded at various scan rates (0.01-0.2 Vs-1) in the presence of a certain concentration of the target analyte. As example, Figure 5a shows CVs recorded at CNF electrode in 0.1 M Na2SO4 supporting electrolyte and the presence of 4 mM sulfide. The anodic current recorded at about+0.7 V vs. SCE increased linearly with the square root of the scan rate (Figure 5b) suggesting that the reaction is mass-transfer controlled. No zero intercept inform about the adsorption process that can not be neglected. Moreover, the starting potential of the oxidation peak shifted towards positive potential when increasing v indicating that the electrooxidation process of sulfide is irreversible (Figure 5c) that is proved also, by the lack of the cathodic corresponding peak. In addition, CVs shapes informed about two steps of the sulfide oxidation process to elemental sulfur, both steps being difussion-controlled.
(a) CVs recorded at CNF electrode in 0.1 M Na2SO4 supporting electrolyte and 0.5 mM sulfide with different scan rates: 0.01, 0.02, 0.03, 0.04, 0.05, 0.07, 0.09, 0.1, 0.2 Vs-1 (curves 1-9); potential range: – 0.5 and+1.2 V/ SCE. (b) The anodic peak current recorded at+0.7 V/SCE vs. square root of scan rate; (c) The peak potential Ep vs. log (v).
Another example is given for the electrochemical behaviour of arsenic on AgCNF electrode (Figure 6) envisaging its detection. Arsenic (III) detection procedure involves anodic stripping voltammetry (ASV), which supposes two stages: first corresponding to reduction of arsenic (III) at the electrode surface for a certain time followed by the second stage of electrochemically stripping from the electrode surface resulting a Faradaic response that is direct proportionally with arsenic concentration. The processes corresponding to these stages can be schematically represented by the following reactions [43]:
(a) CVs recorded at AgCNF electrode in 0.1 M Na2SO4 supporting electrolyte 0.09 M Na2SO4+0.01 M H2SO4 supporting electrolyte and in the presence of 3 mM As with different scan rates: 0.01, 0.02, 0.03, 0.04, 0.05, 0.07, 0.09, 0.1, 0.2 Vs-1 (curves 1-9); potential range: – 0.5 and+1.2 V/ SCE. (b) The anodic peak current recorded at+0.7 V/SCE vs. square root of scan rate; (c) The peak potential Ep vs. log (v).
The deposition stage for this procedure is achieved by maintaining the electrode potential at-0.4 V/SCE for 120 seconds. Based on the results regarding the CVs shapes, the liniar dependence of anodic peak height versus the square root of the scan rate, the oxidation potential shifting to positive direction with increasing scan rate and the presence of the cathodic peak corresponding to the anodic one, arsenic stripping voltammetric process is quasi-reversible and diffusion-controlled. These results are promising for the application of these oxidation processes for the detection purposes.
CV technique is very useful for the detection applications. Figure 7 presents the results of the application of CNT electrode using CV for sulphide detection in various supporting electrolytes envisaging different practical applications. 0.1 M Na2SO4 supporting electrolyte has been chosen due to it is very common for the detection application in environmental application. 0.1 M Na2SO4+1 M NaCl supporting electrolyte presents the composition very close to seawater and the question that raised was to check the possible interference of chloride anions. Real surface water was tested envisaging in-field detection application, for which no supporting electrolyte is added.
CVs recorded at CNF electrode in the presence of various sulfide concentrations 0.1; 0.3; 0.5; 0.7; 0.9; 1 mM in supporting electrolyte: 0.1 M Na2SO4 (a); 0.1 M Na2SO4+1M NaCl (c); real surface water without adding supporting electrolyte. Calibration plots of the anodic current recorded at+0.7 V/SCE vs. sulfide concentrations for each supporting electrolytes (b, d, f).
CV results presented in Figure 7b informed that high concentration of chloride does not affect anodic current response characteristics to the sulfide detection, because the chlorine evolution occurred at high potential value. This result is very promising for sulfide detection in the seawater.
The literature data has been reported about the electrochemical behavior of carbon-based composite electrodes characteristics to ordered (array) or randomized micro/nanoelectrode ensembles characterized by the heterogeneous higher electroactivity [5, 44-49]. Sometimes, the voltammetric responses of carbon-based composite electrodes are sometimes similarly to that found for „edge effect”, which contribute significantly to the Faradaic current [5]. However, carbon-based composites obtained without a specific template are not rigorously arrays but are truly randomized ensembles, with the size, shape and inter-nano/micro carbon islands separation distributed over a wide range of insulating matrix. The random ensembles of microelectrodes can exhibit array behaviour in certain situation. Taking into consideration the distribution of the conductive filler islands within the insulator matrix, the main requirement is that the distance between individual conductive carbon microzones that should be much larger than its radius and mass transport is controlled by the spherical diffusion. The closely spaced carbon nano/microzones array will behave similar to a macroelectrode (linear diffusion-controlled mass transport) because of the diffusion layer overlap. Another important requirement is subjected to the insulating matrix to prevent current leakage, which resulted in the distortion of the cyclic voltammetry. Microelectrode arrays exhibit for the detection applications the advantages of single microeletrodes, e.g., reduced ohmic drop and charging current, the lower detection limit and better sensitivity. In the same time, their use overcomes the disadvantages of single microelectrode subjected to low current output and the high susceptibility to the electrochemical noise [47-49].
The above-presented CV results for the NCC and M-NCC obtained by two-roll mill procedure showed that these composite electrodes are not ordered (array) nano/microelectrodes, because it noticed the linear diffusion-controlled mass transport with a substantial hysteresis, explored by the scan rate influence of the CV shapes, which is characteristics to the macroelectrode behavior.
However, another practical micro/nanoelectrode array peculiarity is given by the ability to deliver the current responses in the absence of any supporting electrolyte, which is appropriate for in-situ or in-field detection applications. Even if the CV shapes recorded at NCC and M-NCC informed that these composites do not exhibit the array electrochemical behavior, however the similar anodic peak current for sulfide detection was noticed direct in surface water without supporting electrolyte at CNF composite electrode (Figure 7c). It can be seen that a slight lower sensitivity was found, and also, non considerable ohmic drop increasing was noticed in surface, which encourage the possible application for in-field sulfide detection.
Differential-pulsed voltammetry (DPV) is a very sensitive detection technique appropriate for trace levels of the analytes. The height of differential pulsed voltammogram is direct proportional to the concentration of analyte [27]:
where σ=exp[(nF/RT)(ΔE/2)], ΔE is the pulse amplitude.
The peak potential can identify the species especial for the simultaneous detection and the background current is much smaller in comparison with CV due to the charging current contribution is negligible. The optimization of the modulation amplitude (MA) and step potential (SP) operational parameters of DPV technique must be considered for each application in direct relation with the the sensitivity and the lowest limit of detection. Example of DPVs recorded on CNT in the presence of various pentachlorophenol (PCP) concentrations are shown comparatively with CV in Figure 8, and the electroanalytical parameters are gathered in Table 3. It can be noticed an improved detectability of PCP on CNT using DPV. Also, as we mentioned and expected, the detection peak potential is shifted to lower value, which is also very desired in electroanalysis.
Differential-pulsed voltammograms recorded at CNT electrode (modulation amplitude 0.2 V, step potential 0.01 V) in 0.1 M Na2SO4 supporting electrolyte and in the presence of different pentachlorofenol concentration (20-120 µM) potential scan rate 0.05 Vs-1 and the potential range between 0 V and 1.25V vs. SCE
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t||
+ 0.97 V | \n\t\t\tCV | \n\t\t\t20-100 | \n\t\t\t5.3 | \n\t\t\t0.990 | \n\t\t\t1.633 | \n\t\t
+ 0.80 V | \n\t\t\tDPV | \n\t\t\t20-120 | \n\t\t\t11 | \n\t\t\t0.984 | \n\t\t\t0.801 | \n\t\t
Electroanalytical performance of the CNT composite electrode for the detection of pentachlorophenol in 0.1M Na2SO4 supporting electrolyte.
The resolution for the simulatenous detection is improved by DPV measurements, sometimes being possible the detection of the analytes characterized by similar detection potential. An example of simultaneous detection of 4-chlorophenol (4-CP) and oxalic acid (OA) as its oxidation intermediar is given in Figure 9. The peak-shaped response coupled with the very low background current and the low detection potential values makes this technique involving NCC electrodes to be very useful for the simultaneous detection of the analytes.
Simultaneous detection of 4-chlorophenol (4-CP) (E=+0.8V/SCE) and oxalic acid (OA)(E=+1 V/SCE) using CNF electrode: (1)-0.5 mM OA; (2)-0.5 mM OA+0.5 mM 4-CP; (3)-0.5 mM OA+0.6 mM 4-CP; (4)-0.6 mM OA+0.6 mM 4-CP.
Square-wave voltammetry (SWV) is a large amplitude-differential technique characterized by a higher sensitivity than DPV, especial for the reversible system due to the reverse pulse cause the reverse reaction and the difference between the forward and reverse components are measured [27]. A very important advantage of this technique is the speed. The effective scan rate is given by fΔE, were f is frequency and ΔE is the step potential (SP). In comparison with DPV, for the detection of pentachlorophenol (PCP) the higher sensitivity was reached for 0.1 V MA, 0.01 V SP and 10 Hz frequency (see Table 4).
Square-wave voltammograms recorded at CNT electrode
The voltammetric techniques can be applied in a preconcentration/voltammetric detection scheme, which is useful for the detection of the trace levels of the analytes in direct relation with the electrode material. The adsorption property of the nanostructured carbon-based electrode towards the target analyte is not generally desired, because of electrode fouling and the loss of the electrode activity. However, this aspect could be exploited however in a positive way to detect organics at the trace level by applying the preconcentration/voltammetric detection scheme. The extent of preconcentration is a function of accumulation time, which determines the degree of adsorption on the electrode surface. The accumulation time represents the time of maintaining the potential value at an open-circuit potential (OCP) value [50]. As example, the effect of accumulation time on the currents of the square-wave anodic peaks recorded at+0.90 V/SCE corresponding to PCP oxidation was investigated. The enhancement factor of 10 determined as ratio of the peak current recorded after 30 minutes accumulation times to that recorded without a preconcentration scheme was found for 8 µM PCP detection using CNT electrode. In Figure 11 is shown the SWVs recorded through the preconcentration-square-wave voltammetric technique. It can be noticed that preconcentration applying improved significantly the sensitivity (Table 4).
Square-wave voltammograms recorded at CNT electrode (modulation amplitude 0.1 V, step potential 0.01 V and frequency 10 Hz) in 0.1 M Na2SO4 supporting electrolyte and in the presence of different pentachlorophenol concentration (2-8 µM) potential scan rate 0.05 Vs-1 and the potential range between 0 V and 1.25V vs. SCE.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t||
+ 0.80 V | \n\t\t\tDPV | \n\t\t\t20-120 | \n\t\t\t11 | \n\t\t\t0.984 | \n\t\t\t0.801 | \n\t\t
+ 0.90 V | \n\t\t\tSWV | \n\t\t\t20-120 | \n\t\t\t16 | \n\t\t\t0.998 | \n\t\t\t0.991 | \n\t\t
+ 0.95 V | \n\t\t\tPrec./SWV | \n\t\t\t2-8 | \n\t\t\t138 | \n\t\t\t0.952 | \n\t\t\t0.033 | \n\t\t
Electroanalytical performance of the CNT composite electrode using pulsed techniques for the detection of pentachlorophenol in 0.1M Na2SO4 supporting electrolyte.
Chronoamperometry is the easiest electrochemical detection technique and it is appropriate for the practical detection applications. This technique is operated at the constant potential value, which is selected based on the existing well-established essential point of reference provided by the cyclic voltammograms, and the current-time dependence is monitorized. As mass transport is controlled by difussion, the current-time dependence reflects the change in the concentration gradient of the electrode surface vicinity. For macroelectrode characterized by linear difussion, this technique should be used for difussion coeficient determination and as consequence, for the specific electroactive surface area determination using Cotrell equation [27]:
For the detection application, the most common method is batch injection analysis (BIA), where the amperometric response recorded by chronoamperometry is obtained for successive and continuous addition of a certain concentration of the analyte. Figure 12 shows the amperometric response (BIA) recorded at AgCNF electrode in 0.1 M Na2SO4 supporting electrolyte by adding 2.4 µM ibuprofen (IBP) at an applied potential of 1.1 V vs. SCE. The response of the electrode is linear in the concentration range of 2.4 µM to 21.6 µM IBP (Inset of Fig. 12).
Amperometric response-batch system analysis (BIA) of the Ag-CNF electrode for the successive and continuous addition of 2.4 µM IBP. Applied potential: 1.1 V vs. SCE. Inset: calibration plots of useful signal vs. IBP.
Pulsed-amperometry is an alternative for chronoamperometry to overcome the disadvantage of the electrode fouling during the detection application. In different working programs applied in practice of the pulsed amperometric detection (PAD) the measurement potential is kept for a short time (measurement pulse) after the electrode cleaning pulse and conditioning potential pulse [51]. This technique has proved to be extremely sensitive for the detection of organic molecules because of the advantage of
PA technique is very useful for the practical detection application especial for the detection of the analytes that can be very easily adsorbed on the electrode surface causing electrode fouling. Application of this technique for the detection of pentachlorophenol at CNT electrode as alternative for CA allowed achieving one hundred times better sensitivity under the operating conditions of three potential pulses, whose values were established on CV behaviour. The pulses were applied continuously using the following scheme [24]:
Pulsed amperograms (PAs) recorded at CNT electrode in 0.1 M Na2SO4 supporting electrolyte for the successive and continuous addition of 2 µM PCP recorded at E=+1.25 V;+0.97 and-0.1 V vs. SCE.
Electrochemical techniques should be regarded as suitable tools for the electrochemical characterization and the detection applications of the nanostructured carbon composite (NCC) and metallic nanoparticles-modified nanostructured carbon composite (M-NCC) electrodes. Cyclic voltammetry (CV) is very useful for the electrochemical characterization that provided mechanistic information based on which the operating conditions are optimized. In general, CV results are considered as reference for the all electrochemical technique applied in the detection application, especially for chronoamperometry (CA) and pulsed amperometry (PA). NCC and M-NCC electrodes obtained by two-roll mill procedure are very promising materials for the detection applications, due to fascinating properties of the nanostructured carbon and metallic nanoparticles. The electrode morphology affected the electrical and electrochemical behaviour of the electrode materials. Their electrochemical detection performances are linked to the electrochemical technique specificity, the best results being achieved using pulsed voltammetric techniques (DPV and SWV). These techniques are very effective also, for the simultaneous detection of multi-components. However, for the practical detection application, CA is most useful due to its simplicity but sometimes, for the specific process that occurred on the electrode surface during the detection, this technique favours the electrode fouling and as consequence, the loss of the electrode activity. PA overcomes the electrode fouling disadvantage since in-situ electrochemical cleaning can be assured by applying simultaneously the potential at which desired advanced oxidation/reduction as cleaning step and mild oxidation/reduction for electrode stabilizing besides the detection potential.
This work was supported by grants of the Romanian National Authority for Scientific
Research, CNCS – UEFISCDI-partially by the project: PN-II-ID-PCE 165/2011 and partially by the project: PNII-60/2012 (WATUSER).
As the world population is projected to grow close to 10 billion by 2050, we need to produce about 50% more food compared to 2013 production to meet the global demand [1]. This goal needs to be met while facing the challenges of climate change, the limited scope of arable land expansion, and dwindling water resources. In addition, anticipated food production also needs to incorporate practices for sustainable management of croplands to preserve soil health, conserve water resources, and encompass biodiversity [2]. Considering these challenges and constraints in achieving our food production targets there is an unprecedented need for monitoring of crop growth and health and timely interventions to maintain or improve crop productivity while reducing wastage of inputs and resources. Advances in sensors, communication technologies, computational systems, and powerful data analytics are enabling us to accomplish these tasks. Technologies that can enable efficient use of agricultural inputs and reduce environmental losses while contributing to increased and sustainable production are of great value for achieving food security. Several existing and emerging tools and technologies such as geographic information system (GIS), remote sensing (RS), Global Positioning System (GPS), Artificial Intelligence (AI), Big Data Analytics, and Internet of Things (IoT) are instrumental in achieving this goal through efficient monitoring of crops and soils, and, combined with other pieces of information, are providing data-driven insights for targeted or site-specific management of crops ensuring increased productivity [3]. Geographic Information System (GIS), a key foundational technology, is defined as a powerful system comprising tools for the collection, storage, and retrieval of data at will, as well as analyzing, transforming, and displaying the spatial data for a specific purpose [4, 5, 6, 7]. It plays a critical role as it provides the spatial context and information on several features each of which is available as a data layer. In addition, it provides the tools to manipulate spatial and non-spatial data and presents them through intuitive and illustrative map formats [8]. GIS has been making an impact in diverse domains that include geography, environmental sciences, natural resources, forestry, agriculture, food, manufacturing, banking, and health services [8]. Recent decades have seen a significant increase in the application of GIS tools for diverse applications in agriculture at local, regional, national, or global scales. These applications most often involve the use of GIS along with partner technologies such as remote sensing, GPS, and data analytics towards an in-depth understanding of a given farm or a region and facilitating intervention or corrective measures for the crops and/or the soils. Since the GIS data are linked to a common referencing system, another advantage GIS offers is that the same data can be used for different applications or goals and we can also bring in other data and, combining that with existing data, we can perform a joint analysis for deriving novel insights. Many studies have reported the use of GIS for diverse applications in different crops [9, 10, 11]. To further enable the readers to develop a strong appreciation for the role of this powerful technology in agriculture, here we have reviewed the most widely used and emerging applications of GIS, either by itself or in combination with other partner technologies, and how it has been making major impacts on agricultural productivity and supply chains.
\nThe power and impactful contributions of GIS in diverse domains can be attributed to the combined use of GIS and two other key geospatial technologies: GPS and Remote Sensing. Each of these three partner technologies plays a crucial role in realizing the goals of applications (Section 3), and, therefore, are briefly described below.
\nBased on its role in supporting the collection, storage, retrieval, and analysis of data on features and location, and its utility for data-driven solutions, especially in site-specific management, GIS is considered the brain of Precision Agriculture [12]. Digital GIS maps differ from conventional maps in that they harbor several layers of information each layer providing information or a map about a given attribute such as soil survey, precipitation, nutrient status, pest infestation, yield, etc. In addition, GIS provides the analytical capability by using statistical tools and geospatial analytics enabling extraction of inter-relationships between attributes, and the insights, thus derived, are valuable for decision making with respect to management practices.
\nThis positioning/navigation system based on a satellite network enables the determination of positional information by providing the latitude, longitude, and elevation of a location. The location information collected by GPS receivers enables farmers and researchers to the reliable identification of fields, mapping of field boundaries, water bodies, infested or problematic areas in the field, and for understanding the relation to several other attributes within and outside the boundaries of a given field. Such a high-fidelity field mapping permits site-specific application of nutrients, pesticides, herbicides, and water, thereby improving productivity and reducing input costs—the essence of precision agriculture.
\nRemote sensing, with its diverse methods and applications in agriculture, has revolutionized crop monitoring and interventions for improving farm productivity [13, 14]. RS, in combination with GPS, GIS, and other tools is critical for implementing the goals of precision agriculture. This combination is crucial for enabling several applications that provide the basis for site-specific management of fields and include soil mapping, crop growth monitoring, estimation of soil moisture and fertility, detection of biotic (pests and diseases) and abiotic (drought and flood) stresses, and yield estimation.
\nThe onset of digital agriculture, considered the fourth revolution in agriculture, has totally transformed the way farming is done, thanks to advances in geospatial technologies, sensors, artificial intelligence, robotics, and other tools and technologies. The ability to precisely identify the problem areas in cropland and monitoring and management of all steps in the entire agriculture value chain requires image and non-image data along with spatial context. GIS, with its component tools and analytic modules, and the data gathered by its partner technologies like remote sensing and GPS provides intuitive and lucid visualization of information for data-driven decision making for improving crop productivity. While GIS has been used for agricultural applications for quite some time, the number of applications has been growing rapidly in recent years due to technological advances. Several most common and emerging applications are presented and discussed below.
\nWe are in an era where we are facing the challenge of feeding billions of people while the fertile land is shrinking, therefore, we need to optimize the use of natural resources to maximize the benefits. GIS provides an excellent platform for assessing the quality of land for suitable applications. Multi-criteria decision-making (MCDM) approach based on GIS is the most popular choice among researchers for land use planning. Researchers use different features offered by GIS such as soil type distribution, soil texture map, buried deep underground water level distribution, soil fertility distribution, soil pollution distribution, hydraulic conductivity of soil (Ks), slope (S), soil texture (ST), depth to water-table (DTW), and electrical conductivity of groundwater (ECw), climate conditions, topography, and satellite data, and identify the variety of interactions, dependencies, and the impact of these interacting factors on sustainable land use.
\nChen et al. [15] evaluated weight sensitivity of MCDM model for land suitability assessment for irrigated agriculture. They aimed to examine the sensitivity of changing weights of the input features on the model output. The results suggested a strong influence of sensitivity and, therefore, they have recommended giving special emphasis on this criterion. Zolekar and Bhagat [16] have used GIS-based MCDM model with IRS P6 LISS-IV images as input for the evaluation of agricultural practices in hilly regions. The rank of influential criteria was determined by correlation analysis and recommendations from scientific literature. The combined use of remote sensing and GIS turned out to be beneficial for land suitability evaluation. Pan and Pan [17] applied three scales, two-step analytic hierarchy processes (AHP) for GIS-based crop suitability assessment. They have emphasized the importance of selecting appropriate evaluation factors and suggested the consideration of features with a significant difference and controlling the land use and avoiding causality. Following this approach of feature selection, the AHP output was spatially distinct. The authors have recommended appropriate land use based on land suitability maps. In another study, [18] selected the features based on growth requirements for examining the land suitability for the wheat crop. Analytic Network Process (ANP) model was deployed for assessing the interdependence of strategic input features for site suitability evaluation of citrus crops [19]. The ANP coupled with GIS–MCDM identified critical factors for maximizing yield and minimizing production loss. AHP integrated with geo-statistics had proven its merit for maize cultivation land suitability mapping in calcareous and saline-sodic soils [20]. These powerful GIS tools enable land reclamation planning with suitable conservation practices.
\nIntegrated fuzzy membership and GIS model were used to analyze arable land suitable for farming. Topography and eight soil parameters were utilized for fuzzy membership classification and the important crop productivity-related soil features were accommodated accordingly. Fuzzy membership allowed the consideration of partial memberships which is unlikely in classical approaches for classification. This self-adaptive approach revealed that the land was better suitable for groundnut cultivation contrary to the current practice of Finger millet cultivation. Results of this experiment proved that the GIS-based decision system can surpass the traditional knowledge and, if deployed accurately, can improve the productivity of land [21]. This is the need of the hour technology as land and natural resources are declining, and the demand for food production is increasing rapidly. The fuzzy set model, AHP, and GIS were combined to generate a land suitability map for tobacco production [22]. This study has once again demonstrated the advantage of using Fuzzy membership functions for land suitability analysis. AHP has the power of accurately assigning weights to the input factors in a logical way. The maps were generated by ArcMap. The integrated application of fuzzy, AHP, and GIS helped to circumvent the problems resulting from the uncertainties, subjectivities, and hierarchy characteristics of the traditional land suitability assessment process. GIS is a powerful tool to delineate the study area, manipulate geographic data, process maps, and present results in land suitability assessment. Integration of Fuzzy set and AHP methods with GIS provides a precise and powerful combination in applying for land suitability analysis. Researchers advocate that Fuzzy logic coupled with other decision-making methods is one of the best approaches for land suitability analysis [21, 22, 23]. Scientists are also exploring artificial intelligence along with GIS for efficient land use planning [23].
\nAbundance of water supply is a primary requirement for meeting the demand for food production by the ever-increasing global population. As indicated earlier, farmers have the responsibility of feeding about 10 billion people in 2050 which demands a 50% increment in food production compared to 2013 level [24, 25]. The availability of clean water is decreasing and dependence solely on rainfall is not a viable option for the farmers [26, 27]. In this challenging scenario, water resource management is the key to success. Irrigation is the best solution for meeting the water requirement in agriculture. GIS technology backed by remote sensing has already proved its merit for the management of water resources [28, 29, 30, 31]. Researchers strongly suggested that remote sensing can supplement the traditional geophysical models for groundwater potential assessment and recharge experiments [32, 33]. Many researchers supported the potential of GIS for groundwater management [34, 35]. Tripathi et al. [36] integrated the MODFLOW groundwater model with the GIS for watershed prioritization. Singh et al. [37, 38, 39] combined GIS and remote sensing for delineating groundwater potential zones. Lineament and hydro- geomorphological maps were prepared from remote sensing images. The delineated groundwater potential zones have been found to show synergy with the well-yield data. When sub-watershed level runoff and sediment yield were assessed using the combination of GIS and remote sensing data it reduced the time of the input data process and produced good results compared to actual runoff and sediment yield [40]. Determining the suitability of irrigation for a given geography is one of the most popular applications of GIS. A study conducted in UAE accounted for non-renewable sources like desalination and treated sewage effluent (TSE) to assess irrigation suitability [41]. This type of water-scarce region needs optimization of water resources management. Land management, topography, climate conditions, soil capabilities, and water potential were used in the analytical hierarchical process (AHP) GIS model to assess crop suitability. The results showed that the land was unsuitable for cereals and vegetables but the cultivation of sorghum, jojoba, fruits, date palm, and forage was recommended. This study unleashed the power of GIS technology for using every acre of fertile land in a geography with a high level of water scarcity. Reduction in clean water resources is motivating researchers and policymakers to identify suitable alternatives for irrigation water. [42] evaluated the scope of using urban treated wastewater as an alternate source of irrigation. They have utilized the MCDM method which was executed in the GIS software environment and the Analytic hierarchy process (AHP) was used. This analysis revealed that the suitability of treated wastewater is subject to suitability for crop cultivation, nitrate contamination burden, and aquifer vulnerability. ISAREG irrigation scheduling model was integrated with GIS with the aim of generating efficient irrigation scheduling advice and identification of practices to account for water savings and salinity control [43]. These results advocated the successful outcome of the model for irrigation scheduling and choosing water saving measures during both wet and dry years. Though the intensification of irrigation is beneficial for food production, it was pointed out that soil salinization and waterlogging are the major drawbacks of irrigated agriculture intensification, and that strong emphasis should be put on leveraging GIS and remote sensing technology for monitoring the problem areas followed by planning conservation and preventive measures [44].
\nSoil fertility is directly proportional to productivity. It controls the availability of nutrients and water to the crop. The soil fertility has been degrading due to various factors like pollution, sealing, overgrazing, waterlogging, excessive use of agricultural chemicals, and erosion. It is crucial to determine soil health and fertility status for planning effective practices for site-specific management or precision farming [45, 46, 47]. Soil macronutrients (N, P, and K), micronutrients (Zn, Mn, and Fe), pH, soil organic carbon (SOC), water holding capacity, erosion status, and moisture content are extensively used features for soil fertility status assessment [48, 49, 50, 51]. Spatial interpolation, Multi-Criteria Decision Analysis (MCDA) [52, 53, 54, 55], and Ordered Weighted Averaging (OWA) [56, 57, 58, 59] are the most popular geospatial analysis techniques which provide spatiotemporal variability of soil health and fertility status to the decision-makers.
\nSoil erosion status is an essential parameter for soil quality assessment and spatial variation in erosion gives a clear picture for agricultural planning [60]. It was demonstrated that geospatial maps of soil erodibility generated by Inverse Distance Weighted (IDW) method is a great tool for assisting in sub-watershed level land use planning. [61] combined the remote sensing and GIS technology to assess the soil fertility status. They have used the LISS III and IV images for land use classification and the RUSLE method for soil erosion estimation, collected the soil nutrient field data, and applied a geostatistical model to identify the spatial variation of soil erosion and nutrient availability. In another study, [62] used the IDW model for derived soil nutrient maps and applied the OWA method to make the maps homogenized and used those as the input for the fuzzy inference system for soil fertility mapping. Fuzzy mathematics developed with soil organic matter (SOC), total N, total P, total K, available N, available P, available K, pH value, and cation exchange capacity as indicators in ArcGIS showed that the soil fertility of mid- and low- yielding fields were low and are directly correlated with soil profile configuration [63]. The association of crop productivity with the soil fertility is evident and GIS-based soil maps and fertility status give prior information about the field-specific crop suitability.
\nLeena et al. [64] proposed GIS-enabled cloud technology for soil fertility management decision support system. This system has the capability to make fertilizer recommendation based on soil test and crop response. This recommendation system helps farmers optimize their fertilizer usage and maximize yield. This system generated spatial nutrient variation works as fantastic e-governance system for the government agencies. GPS- and GIS-based soil fertility maps are great tools for thorough monitoring of the soil health and, based on such maps, [65] recommended application of paper mill sludge to reduce acidity in the soil and cultivate pulses and groundnut to make the best use of the acidic soil. These geospatial soil maps have proven to be an effective decision support system in the context of food production challenges due to soil degradation. [66] applied soil fertility index (SFI) based on the variables of sand, silt, clay, pH, EC, OM, CaCO3, Ntotal, Pavb, Kexc, Caexc, Naexc, Mgexc, and available micronutrients (Feavb, Cuavb, Znavb, Mnavb) and proved the strength of SFI. This study demonstrated the potential of combining Sentinel 2 image-derived crop yield for validation of soil fertility model. Advances in the observatory systems such as remotely sensed data of fine-to-coarse spatiotemporal resolutions, and in the process-based and data-driven modeling techniques have facilitated the collection, storage, analysis, visualization, and interpretation of non-spatial data for soil fertility index (SFI) [67, 68, 69, 70, 71, 72].
\nLi et al. [72] applied weighted space fuzzy clustering coupled with the soil nutrient space mutation distribution for soil fertility characterization. This information aids in optimizing the fertilizer recommendation system. Agricultural practices such as crop residue management, nutrient management, soil tillage, and pest management affect ecosystem goods and services and soil quality and fertility [73, 74, 75]. The best management practices, compatible land use/cover changes, and land suitability analysis are required to prevent the degradation and loss of prime farmlands [73, 76, 77]. Soil erosion management, soil biodiversity improvement, and rehabilitative farming systems are some of the best management practices used to improve soil quality and crop yields [78, 79, 80].
\nA study that leveraged GIS and fuzzy evaluation method to evaluate the soil fertility status used total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus, available potassium, soil organic matter, cation exchange capacity, and pH as indicators for the generation of fertility indices [63]. This fertility index revealed that total nitrogen and soil organic matter are higher for paddy fields. These fertility maps also give an insight into the suitable soil qualities under different types of land use and climatic conditions. Sub-watershed level nutrient mapping revealed that available N, P, S, Zn, and Fe are controlling agents of soil fertility [81]. Thus, fertility maps and their relationship with soil properties and crop yields serve as an information system for precision agriculture.
\nStudies have reported that biotic crop damage, caused by insects, fungi, and other pests, can cause 15–70% yield loss [82, 83, 84]. This scenario impacts the demand and supply chain and also affects the economy of farmers. The changing pattern of weather makes the crops susceptible to pests and diseases. The availability of crop protection methods is quite beneficial for tackling crop health, but the lack of timely information about the pests and diseases makes the damage irrepressible. GIS technology holds immense potential for site-specific pest and disease management. Remote sensing and GIS-based forewarning systems are boon to farmers to arrest the yield and economic loss. Ranjan and Vinayak [85] advocated that pest and disease forecasting systems allow farmers to apply the control measures in time to reduce the cost of production. Apart from the forewarning system, the pest population density map also plays a crucial role in identifying the hotspots and extending advisory to farmers. According to [86], information about the geospatial density of oriental fruit moth,
Natural calamities cause irreversible damage to agriculture. Rapid mapping and quantification of damage aid in economic loss recovery and act as a decision support system. A geo-spatial model is used in a case study to assess the impacts of extreme flood events on agricultural production in the Quang Nam province of Vietnam. [97] generated the water surface by interpolating flood depth marks by the inverse distance weighting (IDW) and employed a digital elevation model (DEM) to generate the flood inundation map. This map overlaid with the land use map gave an effective estimate of the damaged agricultural area [98, 99, 100]. Drought is another constraint to agricultural productivity and understanding the hotspot and climatology is crucial to strategically minimize the impact. MODIS satellite Normalized Difference Vegetation Index (NDVI) derived drought risk classes were prepared to access the spatial pattern [101]. GIS-based characterization of climate variability and drought zones provides scope for strategic measures adoption to maximize productivity [102, 103].
\nMonitoring of crop growth, health, and accurate or near accurate prediction of yield is crucial not only for estimating economic return but also for assessing the food production thereby helping in the management of food security. Many studies showed that traditional methods of crop yield estimation could lead to poor assessment and inaccurate crop area appraisal [104, 105]. Moreover, these methods require time-consuming, labor-intensive, and expensive crop and yield data collection. This is where technologies like remote sensing (RS), GPS, and GIS provide a huge advantage as they can be used to assess temporal and spatial variability of crop dynamics and yield output [106]. The use of two key partner technologies, RS and GIS, with required input from others can provide an efficient solution for monitoring crop health and developing models for predicting crop yields across diverse spatial scales. While remotely sensed images and associated analytics permit the tracking of crop health and predicting the yield, GIS technology enables the collection, storage, retrieval, and visualization of data that were linked geographically. Remotely sensed geospatial data acquired by satellites, aircrafts, or unmanned aerial vehicles (UAVs) can be used to gather information on several features of the crops and the characteristics of the soils supporting their growth thereby enabling the assessment of crop health. The images gathered can be used for assessing general vigor, disease or pest infestations, or deviations from expected growth due to drought or other abiotic stresses. Geospatial data collected in a spatiotemporal manner and the associated analysis techniques help in assessing the changes in the health of crops thereby permitting management interventions while providing predictions on anticipated yields based on the growth and health of the crops. A commonly used method for assessing crop health is based on the determination of vegetation indices that are calculated based on surface reflectance from crop canopies at two or more wavelengths. Many vegetation indices are available for evaluating the extent and vigor of vegetation, crop growth dynamics, stress due to biotic or abiotic factors, and other useful assessments [107]. Adhav et al. [108] used multiple vegetation indices that included Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Vegetation Index (CVI), and Difference Vegetation Index (DVI) to determine crop health as well as variations in health conditions. To further improve the efficiency of health assessment, they have combined all vegetation indices using ArcMap 10.5 software and reclassified the merged indices which were then used for categorical representation of health scenarios. Such a representation helps farmers to identify areas that need immediate management intervention [108]. Determination of crop health is particularly critical in smallholder farms as the subsistence and livelihood of these farmers depend on the productivity of their crops. As per a recent study, small farms were found to account for 84% of all farms worldwide but they operate only on around 12% of all agricultural land and produce about 35% of world’s food [109]. Use of UAVs for gathering and leveraging data for assessing crop growth and dynamics has proven to be crucial for farmers to take timely and appropriate corrective measures to maintain or increase productivity. A recent study in South Africa [110] evaluated the utility of multispectral UAV imagery and random forest machine learning (ML) algorithm to estimate maize chlorophyll content at various growth stages and created a chlorophyll variation map capturing the spatial heterogeneity of chlorophyll in the field thereby helping the farmers to take management actions. Considering the strategic role of sustainable intensification towards the food production goals of Sub-Saharan Africa [111], such RS- and GIS-based diagnostics and interventions are critical for smallholder farmers. Another study assessed crop health using different chlorophyll indices in addition to modified vegetation index by leveraging data from two different satellites and ArcMap (of ArcGIS) for geospatial analytics [112] resulting in insights that could be used for managing nutrient applications towards improving crop productivity. Two essential prerequisites to implement location-specific management practices and interventions are the availability of an accurate acreage map of crop of interest and the cropping systems of a given area and technologies for predicting yield before the reproductive phase or harvesting of the crop. The use of RS and GIS technologies can help achieve both goals. NDVI, a commonly used vegetation index, serves the dual purpose of assessing crop health and predicting crop yield while GIS tools can provide the spatial context. Several studies abound that leveraged NDVI and GIS for yield predictions and a few examples are discussed here. In a study that measured NDVI values at different growth stages of rice, several linear regression-based yield prediction models were developed using NDVI values and narrowed down to a model that had the highest prediction potential and was also able to predict yield well ahead of harvesting time [113]. Such a model can help the farmers to implement changes to the fertilization, water, pest, and disease management practices towards realizing improved productivity. Using time-series data of SPOT vegetation and two key spectro-agrometeorological variables, rainfall estimate (RFE) and NDVIactual (NDVIa), that are highly correlated to maize yield, [114] have developed an operational model with high predictive ability for yield forecasting in Ethiopia. By leveraging both RS and GIS, this model enabled yield forecast at flowering season which is more than two months earlier than the forecast by conventional method thus providing an advantage of early intervention towards crop productivity and crucial data for the authorities for crop production estimates [113]. In a field study aimed at developing an efficient model for predicting potato tuber yield using RS and GIS techniques two vegetation indices, NDVI and soil adjusted vegetation index (SAVI), generated from images acquired by Landsat-8 and Sentinel-2 satellites were found to be highly effective in yield prediction [115]. In addition, the indices enabled them to create maps of the study area that has clearly shown zones differing in productivity. This is very useful information for both farmers for implementing necessary management practices and for authorities in arriving at accurate production estimates. While manly researchers have used RS-based vegetation indices and GIS for predicting crop yields, several researchers have combined GIS with crop simulation or physiological models and demonstrated their strong performance in yield prediction [116, 117, 118, 119]. Crop simulation models came into prominence due to their utility in designing management practices, assessing the role of climate variations on crop performance, and predicting yields [120, 121]. Similarly, physiological crop models have evolved from their original applications in farm management to measuring the impact of climatic changes on crop productivity. The ability to incorporate spatial variability of the inputs that go into physiological or simulation models makes them even more powerful for determining the interactions between climatic variation and crop productivity while highlighting the spatial heterogeneity. In a study that integrated RS data, crop growth model, and GIS, it was found that yield estimates from RS images were more precise compared to another approach where GIS climate layers and soil attributes were integrated into Oryza 2000 rice crop model highlighting the superiority of combining RS, GIS, and crop model for estimating crop yields [118]. To capture the spatial variability of input variables and their influence on yield estimates, [116] have linked RS and GIS with a growth model of soybean. The results demonstrated spatial variability in simulated yield estimates and the variability was primarily attributed to soil characteristics and rainfall. The availability of such spatial patterns from the simulated yield estimates is very helpful in productivity estimates in areas prone to abiotic stresses, for example, droughts, as well as providing insights into factors contributing to yield. Efforts also exist that have created web-based decision support systems based on a combination of simulation model and GIS towards making agronomic decisions [119]. In yet another approach, the Erosion Productivity Impact Calculator (EPIC), a model for the analysis of the relationship between soil erosion and crop yield at field level, was integrated with GIS and an Inference Engine (IE) towards global estimation of crop productivity [122]. While the integration of GIS expands the application of EPIC to regional or global level, the availability of IE helps in determining potential crop combinations for given growing conditions. This study not only demonstrated the ability of GIS-based EPIC for crop productivity simulations at global level but also delivered predictions for future yields and how they are adversely affected by global climate change underscoring the importance of the development of climate-resilient varieties of crops. Since traditional crop productivity simulations are based on site-specific crop models, [123] developed an operational crop model that can be utilized at the regional level, North China, by integrating USDA EPIC model with NASA MODIS LAI product from Earth Resources Observation System (EROS), ancillary ground data, and GIS [123]. Applications also exist where a combination of GIS and RS was used for assessing damage in some high-value crops. Cranberry is one such crop that exhibits extreme crop yield variations due to soil characteristics which in turn influence water and nutrient availability. Using GIS, GPS, and RS, [124] have created a spatial variation map for the crop enabling the analysis of crop losses within zones in a field or at the whole field level.
\nPrecision Farming, also called Precision Agriculture (PA) or site-specific crop management (SSCM), is the application of technologies and principles to manage spatial and temporal variability associated with all aspects of agricultural production [125]. Earl et al. [126] defined it as a system that integrates information with crop production that is designed to increase long-term, site-specific as well as whole farm production efficiency, productivity, and profitability while minimizing unintended impacts on wildlife and the environment [126]. The operational goals of precision farming include better management of inputs such as seeds, fertilizers, pesticides, herbicides, and water using right amounts of inputs at the right place, and at the right time. Several crucial tools and systems such as GPS, GIS, and RS are required for the collection of timely geospatial information on soil-plant-animal requirements towards mining insights followed by leveraging those insights for prescribing and applying site-specific treatments towards improving agricultural productivity while contributing to sustainability and protecting the environment [127, 128, 129]. The role of different tools and technologies as well as the applications of precision farming are described in several review articles and references therein [9, 128, 130]. While GPS, GIS, and RS are vital for obtaining and analyzing the data for deriving insights, a key technology that implements the precision applications by leveraging the input of these three tools is variable rate technology (VRT). VRT systems take all the required information about a field such as soil maps, yield, infestation of pests, diseases, and weeds, and they determine the quantities of fertilizers, pesticides, herbicides, and other inputs and ensure their application at the right place and at the right time saving the input costs. The integration of GIS, GPS, and VRT technologies thus provides farmers an unprecedented ability to view field maps and apply input where and when needed towards ensuring crop productivity. Precision farming can be broadly divided into three steps or stages depending on data collection or site-directed or specific activities happening during, before, and after the crop growth period [131]. These are Preparatory or Pre-planting stage, Crop growth stage, and Harvesting stage. Role of GIS in each of these stages is discussed below.
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Renewable sources of energy are crucial to achieving climate change and sustainability goals. Agricultural residues are a promising source of biomass-based energy the demand for which is rapidly increasing around the globe. One challenge with agricultural residues for efficiently channeling them for energy production is the fact that their availability is seasonal and is geographically widely distributed. A solution that can address this spatio-temporal variability, seasonal fluctuations in biomass supply levels, and identification and transport of residues to power plants is a critical prerequisite for biomass-based energy generation. GIS, in combination with remote sensing, can be a great tool for precise identification and assessment of the crop residues and for planning a given region’s feedstock material for renewable energy and its economical transportation to power plants. GIS-based estimation of bioenergy potential enables a technologically advanced solution for leveraging the residues from existing cropping practices that promise even more benefits as the farmers shift from conventional to smart farming [139]. Some of the efforts in leveraging GIS and its partner technologies to this end are discussed below. Methods that can predict biomass potentials of a given region containing weather and crop production variations are of high value for enabling an efficient supply chain from biomass to power plants. By using BioSTAR, a carbon-based crop model, [140] have calculated biomass potentials for maize, triticale, and cup plant, and linked them with a GIS map of the soil dataset of Hannover region in Germany and demonstrated the utility of this method for predicting agricultural potentials under diverse environmental and crop management practices and conditions [140]. In a study that mapped rice cropland in a rural area in India, images from WorldView-2 satellite were used and the resulting map along with agricultural production statistics was analyzed in GIS for assessing the availability of rice straw as a feedstock for generating bioenergy [141]. In addition, the study also estimated the annual rice straw availability and the electrical power it could generate, thus providing valuable information for energy developers and policymakers for planning. Since the success and sustainability of a biomass-based energy generation project depend on several factors that include the feedstock resource, logistics, and environmental considerations, the role and value of GIS and key associated tools and technologies need to be understood prior to establishing the supply chain and the power plants. Two tools can help to address this task: GIS and life cycle assessment (LCA). While GIS is critical for assessing the resources dispersed in small or large areas, LCA is useful in evaluating the environmental impacts of bioenergy production projects. A comprehensive review on the application of LCA, especially spatial LCA, in understanding the impact of biomass-based energy generation on different ecosystem services and the value of integrating LCA and GIS to conduct a holistic assessment of environmental benefits in connection with bioenergy production recommended the inclusion of LCA as an essential component in planning bioenergy projects [142]. To assess the spatial and temporal availability of crop residues and to pinpoint locations for ideal power plants along with cost considerations, an integrated GIS-based biomass, site optimization, and logistics cost model was developed by using soil erosion, soil conditioning index (SCI), and crop residue yield indicators [143]. To estimate crop residues, prediction models based on artificial neural networks (ANNs) were developed for each of these indicators and were implemented on a GIS platform. The utility of this model was also demonstrated using a sustainable assessment of cotton stalks (CS) that are used to produce fuel pellets. An advantage of this model is that its use can be extended to assessment of multiple types of crop residues [143]. Models based on GIS and multi-criteria inclusion-exclusion analysis and facility location-allocation were also developed for the identification of sustainable crop biomass at larger spatial and longer temporal scales and to suggest ideal biogas plants along with cost considerations for biomass delivery [144].
\nGIS technology has proved to be of great value in understanding and optimizing agricultural supply chains and its use is being extended to diverse crops and locations. For ease of understanding its impact on supply chains can be discussed using the following three categories.
\nGIS technology has the potential to assist the successful transition of traditional agriculture systems to smart systems. While there are many studies that have thoroughly investigated the role of big data analytics in supply chains of diverse industries, such studies are lacking in the application of big GIS analytics (BGA) in agriculture. To this end, a systematic review of recent literature examined the role of BGA in agricultural applications and has proposed a framework for supply chains where BGA can play even a bigger role in improving the quality of GIS applications in agriculture [145]. The proposed framework serves as a useful reference for scientists and authorities for the successful management of big GIS data and leveraging it for improving productivity. The utility of Geographical Information Technologies (GITs) for improving the complex supply chain management process in the cotton crop was explored and was found to be of great value since the GITs framework enables visualization of current states as well as alternative options and what-if analyses for all steps that require decision making [146]. Another important application for which GIS was used is the analysis of supply chain patterns and description of spatial components of safe crop product (SCP) in China [147]. By using the spatial functions provided by GIS such as representation, location, analysis, traceability coding, and other techniques, tracing and retracing of the quality of safe crop product (SCP) was achieved. This system was also successfully demonstrated in a real supply chain for (re)tracing of SCP. By developing a GIS-based constrained linear programming model for minimizing transportation and storage costs for soybean and its byproducts, and further optimizing this model using General Algebraic Modeling System (GAMS), [148] have identified the lowest cost supply chains. The origin to destination cost matrices and geographic data maps required for the model development and optimization was developed by ArcGIS Network Analyst and ArcMap, respectively. This study demonstrated the combinatorial utility of ArcGIS, ArcMap, and GAMS for developing optimal supply chains that are of value to the players in the process [148].
\nProduction of biofuels from renewable sources such as agricultural residues can reduce the usage of fossil fuels thereby helping in the reduction of greenhouse gases. Identification of ideal locations for establishing biofuel facilities and designing a cost-effective supply chain for transferring biomass to the facility is highly desirable. To this end, in one approach a decision support system (DSS) has been developed by integrating a GIS-based method and two modeling methods, simulation, and optimization [149]. While GIS-based method was used for selecting facility sites, the selected sites were run through simulation and optimization modeling, and together these three methods provided an integrated DSS for assessing the cost, energy use, and emissions for the facility candidates as well as minimizing supply chain costs. In another approach, an intelligent spatial decision support system (ISDSS) was proposed to overcome the drawbacks of GIS in enabling creation of a knowledge base that supports decision making. The ISDSS combines GIS and intelligent systems and has spatial data mining capability through IoT devices [150].
\nThe sustainability of a biomass-based power plant depends on, among other things, a consistent supply of the feedstock, an economical supply chain, and an optimal location of the facility. The GIS-based analysis enables the identification of an ideal location for the plant and in making valid decisions related to the supply chain development. Using open-source GIS software, Latterini et al. [151], have simulated the identification of suitable locations for a small size power plant in Lazio region of Italy that can use olive prunings as the feedstock. This user-friendly and low-cost procedure, which can also be extended to other feedstocks, also provided supply chain costs for the evaluation of different sites and can serve as a useful tool for stakeholders in the development of economical biomass-based end-to-end supply chains [151]. In another study, an integrated approach combining GIS-based analysis with optimization modeling was developed resulting in a support system for decision-makers in comparing facility candidates and in minimizing supply chain costs [152]. The system developed could also be used for similar supply chains such as low capital biodiesel plants.
\nThe use of GIS in agriculture has increased at a rapid pace during the recent decades and the number of applications and the prominence of GIS has further amplified in the recent years due to advances in digital technologies that have been leveraging GIS as an essential partner technology for assessing crops, soils, and their environments. As discussed in this chapter, GIS is being used at all stages of agricultural value chain. In addition to the historical, current, and popular uses of GIS in land suitability/use planning and management of water, soil, and biotic and abiotic stresses, the advent of digital agricultural tools and technologies has increasingly leveraged the capabilities of GIS in new and emerging applications in high fidelity crop monitoring, yield prediction, precision farming, and supply chain management for both primary produce and biomass utilization towards energy production. The multitude of capabilities and insights provided by GIS, including the recent enhancements to collect and analyze data in real time, has further elevated its importance in providing location/spatial intelligence needed for improving the productivity and profitability of farms through precision practices. With the current and emerging applications, in combination with existing and newer partner technologies, GIS has a pivotal role in achieving sustainable agricultural productivity.
\nWe would like to thank Anu Swatantran for the support and helpful discussions, Chris Seifert, Andrea Arias, and Brian Lutz for support and funding, and Jochen Scheel, Steve Callistein, Janae Lehman Bell, and Nicole Janovick for rapid review and approval of the manuscript.
\nThe authors declare no conflict of interest.
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This chapter focuses on investigating the commonly used manufacturing technologies and process chain for the prototyping and mass production of microfluidic chips. The rapid prototyping technologies comprising of PDMS casting, micro machining, and 3D-printing are firstly detailed with some important research findings. Scaling up the production process chain for microfluidic chips are discussed and summarized with the perspectives of tooling technology, replication, and bonding technologies, where the primary working mechanism, technical advantages and limitations of each process method are presented. Finally, conclusions and future perspectives are given. Overall, this chapter demonstrates how to select the processing materials and methods to meet practical requirements for microfluidic chip batch production. 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One of the most plausible applications of nanotechnology is to produce nanoparticles of high thermal conductivity and mixing with the base fluids that transfer energy forming what is called nanofluids. Adding of nanoparticles to the base fluid shows a remarkable enhancement of the thermal properties of the base properties. Nanotechnology has greatly improved the science of heat transfer by improving the properties of the energy-transmitting fluids. A high heat transfer could be obtained through the creation of innovative fluid (nanofluids). 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