Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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\r\n\tFood is the basic necessity, which sustains active and health life style. Everybody should have an access towards adequate amount of food that can be ensured through food security. Therefore, the concept regarding the food security has utmost importance for developed and developing nations. It measures that every individual has access to the food that fulfils the food safety and quality standards. Food availability, access, utilization and stability are the pillars of the food security. These pillars are being affected due to various factors such as natural disasters, poor agricultural and post-harvest practices, climate change and poor manufacturing and marketing strategies. The role of all these factors will aim to fall in the scope of this book. \r\n\tFood insecurity results in fear of hunger and starvation that ultimately affects one’s ability to work for sustainability and economic growth of the country. In addition to this, food insecurity results in various chronic diseases due to reduce immunity that ultimately, a burned on the county economy. Therefore, this book will intend to discuss in detail about the food insecurity challenges and their effect on the quality of life. This book will also aim to provide an overview about the new trends and future prospective that help to resolve the food security issues.
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1. Introduction
OH radicals produced by ozone decomposition in water are the second strongest oxidizers after fluorine. Therefore, ozone water is used in a wide range of applications, such as the sterilization of medical instruments, the oxidation and detoxification of wastewater, the deodorization and decoloration of well water, the cleaning of electronic components, and food treatment [1, 2]. For example, the possible applications of ozone water to the control of microbiological safety and the preservation of food quality are well summarized in a review paper [3]. OH radicals can also kill various bacteria, such as O157, MRSA, Pseudomonas aeruginosa, Bacillus subtilis, Bacillus anthracis,\n\t\t\t\tBacillus cereus and Legionella pneumophila [4].
There are two major processes for ozone water production as shown in Fig. 1. One is a gas phase ozone production followed by mixing ozone with water [Fig. 1(a)]. In this process ozone gas is produced from oxygen gas using electric discharge, and then mixed with water using hollow fibers or mechanical mixers. Although this process can supply ozone water at a low electric cost, it requires a high-voltage power supply, an oxygen cylinder, and a mixing process. Therefore, the system is large and cumbersome in operation, and the facility cost is high. The other process is direct water electrolysis using a polymer electrolyte membrane (PEM) [Fig. 1(b)]. In this process, water is introduced to the anode side of the electrolysis cell, electrolytically decomposed, converted to ozone, and mixed with the ozone gas formed in the compact cell. Such apparatuses can enable ubiquitous and low-voltage operation as home electronics.
The ozone formation reactions and standard electrode potentials in the direct water electrolysis are as follows:
Anode:
2H2O→O2+ 4H++ 4e−\tE0= 1.229 VE1
\n\t\t\t
3H2O→O3+ 6H++ 6e−\tE0= 1.511 VE2
\n\t\t\t
O2+ H2O→O3+ 2H++ 2e−\tE0= 2.075 VE3
\n\t\t\t
Cathode:
2H++ 2e−→H2\t\tE0=0 V.E4
\n\t\t\t
These reactions are fundamental in electrochemistry and have been extensively studied. Ozone gas evolves by a six-electron reaction in Eq. (2) at a voltage higher than 1.511 V, accompanied by oxygen evolution. By increasing voltage to above 2.075 V, the oxidation of O2 gas to form O3 is also expected as shown in Eq. (3). Since O2 evolution occurs at lower potential than O3 evolution, the production rate and electric power consumption in O2 evolution are much higher than those in O3 evolution. Therefore, catalytic activity in O3 evolution reactions is indispensable for electrolysis cell design.
Current efficiency or charge yield, ΦO3e(%), and power efficiency or specific energy consumption, wO3e(kWh/kg-O3), were calculated using
ΦO3e=5×F×Q3×MO3×I×100(%),E5
\n\t\t\t
wO3e=I×U1000×Q(kWh/kg-O3)E6
,
Figure 1.
Two types of representative ozone water production systems.
where F is Faraday’s constant (9.6485 x 104 C/mol), Q is the O3 production rate (kg/h), MO3is the molecular weight of O3 (48), I is the current (A), and U is the potential difference between the anode and the cathode (V). The derivation of Eq. (5) is as follows:
When the current I (A) is applied to the cell, the electron flow rate per hour is written as
IF×3600(mol/h).E7
O3 produced per hour, Q (kg/h), can be converted to mol/h using
QMO3×103(mol/h).E8
Because six electrons are required to produce one O3 molecule, 1/6 of the electron flow rate calculated using Eq. (7) becomes the theoretical and maximum O3 molecule production rate, which corresponds to 100 % efficiency. Therefore, real current efficiency is defined by
ΦO3e=QMO3×103IF×3600×16×100(%).E9
Calculating the right-hand side of the equation above, we obtain
ΦO3e=5×F×Q3×MO3×I×100(%).E10
The electrochemically produced O3 molecules decompose in anode water to form OH radicals. The decomposition reaction is very complicated, and more than 30 elemental reactions are involved in the process. The reaction set proposed by Wittmann, Horvath and Dombi (WHD model) consists of 33 elemental reactions [5]. Although many reactions are taken into consideration, the decomposition behavior of O3 in neutral pH water cannot be reproduced adequately using the WHD model.
The actual half-life of O3, i.e., about 2 h in ion-exchanged water and 4 h in pure water, are much longer than the calculated values. To improve the accuracy of the elemental reaction set, Mizuno et al. proposed new rate constants in a few elementary reactions [6]. They reduced the rate constants in OH- formation reactions up to three orders of magnitude to fit their experimental data. We also noticed a discrepancy between the actual and estimated half-lifes, and obtained findings similar to those by Mizuno et al. However, we were not as radical as them. We would not write a paper, because we did not obtain experimental evidence to confirm the small rate constants of OH- formation reactions.
For readers of this chapter, a simplified image of the OH radical formation reactions is presented below:
O3 decomposition is started by the reaction of O3 with OH-, forming HO2- as in Eq. (10). The initiation of O3 decomposition is pH-dependent, because OH- concentration determines the reaction rate in Eq. (10). Therefore, water pH strongly affects the half-life of O3, i.e., O3 has a shorter half-life in alkaline water and a longer one in acidic water. HO2- reacts with another O3 molecule to form O3- [Eq. (11)]. Finally, O3- is converted to a OH radical by reacting with H2O [Eq. (12)]. Eq. (13), the summation of Eqs. (10) to (12), indicates that one OH radical is produced from two O3 molecules, which means that OH radical formation is a second-order reaction of O3. Therefore, four times higher OH radical concentration will be obtained by doubling the O3 concentration in water. The bactericidal power of ozone water is also the second order of the O3 concentration in water.
The development of electrochemical ozone production (EOP) systems using a PEM has shown significant progress. The main interest in studies of EOP systems has been in the preparation of effective anodes aiming at high catalytic activities for O3 evolution. Many anode substances have been tested in a PEM system. The first demonstration of highly concentrated and efficient EOP using a PEM system was reported by Stucki et al. [7], who used a PbO2 layer as an anode catalyst. They obtained a current efficiency of more than 15 %. Since then, studies on ozone water production using PEM systems have become an active area. Feng et al. utilized an Fe-doped PbO2 catalyst electrodeposited on a Ti tube, and obtained a current efficiency of more than 12 % in 0.52 M K2PO4/0.22 M KH2PO4 buffer containing 2.5 mM KF [8]. Santana et al. reported that an IrO2-Nb2O5 catalyst deposited on a Ti anode shows a current efficiency of 18 % in 3 M H2SO4 + 0.03 M KPF4 solution, but its lifetime is only about 1 h: the corrosion of the anode catalyst prevents the system from longer operation [9]. Chang et al. utilized an Sb-doped SnO2 anode catalyst, and obtained a current efficiency of more than 15 % in 0.1 M HCl solution [10]. They improved the efficiency up to 36.3 % in 0.1 M H2SO4 solution by changing the anode catalyst to Ni- and Sb-codoped SnO2 [11]. Awad et al. showed a current efficiency of 11.7 % using a TaOx/Pt/Ti anode electrode in tap water, and reported that the system could be operated for as long as 65 h [12]. B-doped diamond substrates have been used as an anode, and Arihara et al. reported a stable operation with a current efficiency of 29 % in 1 M H2SO4 solution [13]. Recently, Kitsuka et al. have reported that a high current efficiency of 9% was achieved at a low current density of 8.9 mA cm−2 in 0.01 M HClO4 at 15°C using n-type TiO2 thin films deposited on Pt/TiOx/Si/Ti substrates [14].
The generation mechanism of OH radicals in a B-doped diamond electrode system was investigated by Marselli et al. by ESR spectroscopy in the presence of the spin trap 5,5- dimethyl-1-pyrroline-N-oxide (DMPO), and the formation of OH radicals during water electrolysis was confirmed [15].
However, several problems remain unsolved for the commercial applications of the PEM system: Some catalysts, such as PbO2 and Sb, are harmful to the human body and cannot be used for systems where contact of the human body with ozone water is inevitable; the use of an electrolyte, such as HCl or H2SO4, is also undesirable for the same reason; diamond substrates are inferior in forming water flow channels that enable good contact between ozone and water in the apparatus; and no catalyst lifetime longer than 500 h has been reported yet. Therefore, improvements in the performance and stability of the PEM system are still necessary.
Pt electrodes have been a classic substance for water electrolysis, and thus we have reinvestigated the potential of a Pt anode used in a conventional PEM system. There is an encyclopedia of studies of O2 formation by the electrochemical splitting of water using Pt electrodes [16]. However, research on EOP is a rather limitted. The current efficiencies of Pt electrodes were reported in the above-mentioned studies, because researchers measured the efficiencies of Pt electrodes as references for comparison with those obtained using their original anode catalysts.
The current efficiency range of the Pt anode is from 0.7 to 7 %, and strongly depends on the system setup. If a high efficiency and a long lifetime could be achieved using a Pt electrode, we would eventually obtain a reasonably priced and safe EOP system.
In our research, an EOP system using Pt mesh electrodes combined with a conventional PEM, i.e., Nafion 117 membrane, was investigated in terms of current efficiency and lifetime. We found that Pt particles that dissolve from the Pt anode migrate through the membrane, and are dendritically deposited on the cathode surface in contact with the membrane. The deposited Pt particles decompose the membrane, decreasing the current efficiency and lifetime of the EOP system. As a solution to solve this problem, one of the key improvements is achieved in our EOP system: A quartz felt separator is inserted between the membrane and the cathode. Pt particles are captured and isolated in the separator. Therefore, no decomposition occurs in the membrane [17].
Another important improvement is realized by the appropriate selection of a cathode electrolyte (catholyte). When a NaCl catholyte with concentrations higher than 0.085 M is circulated on the cathode side of the cell, the formation rate of Pt oxide on the anode surface is markedly reduced. Cl- ions that migrated from the catholyte to the anode surface inhibit PtO2 formation. The anode surface is kept clean during the EOP operation, and the clean Pt surface provides both good current efficiency and long lifetime [18]. As a result, the new EOP system achieved a high current efficiency of more than 25 % and a lifetime longer than 2,000 h. These findings and experimental results are presented in this chapter.
2. Conventional electrolysis system and its performance
2.1. Structure of conventional electrolysis cell
Photographs of electrolysis cells for the EOP system are shown in Fig. 2. A small-capacity cell that can produce ozone water up to a flow rate of 1 L/min is shown in Fig. 2(a), and a large-capacity cell for 20 L/min ozone water production is shown in Fig. 2(b). Both cells are designed and manufactured by FRD, Inc. The electrolysis cells consist of a Nafion 117 membrane sandwiched by Pt #80 mesh electrodes and have Ti and stainless terminal plates.
The cross-sectional cell configuration and the cathode structure of the small cell are shown in Figs. 3(a) and 3(b), respectively. The #80 Pt mesh electrodes are 4 x 2 cm2 in area, 0.2 mm in thickness, and 0.3 mm in pitch distance. To form a waterway, a Ti #40 mesh is inserted between the Ti plate and the Pt mesh on the anode side, and a SUS304 #100 mesh is inserted between the SUS304 plate and the Pt mesh on the cathode side. The electrodes are held by SUS304 and Ti plates, which act as the cathode and anode terminals, respectively. The use of the mesh and plate made of Ti is important, since SUS304 cannot withstand oxidative corrosion caused by anode electrolysis reactions.
Figure 2.
O3 water production cells using Pt electrodes and Nafion membrane. Copyright: FRD, Inc.
Figure 3.
Cross-sectional views of 1 L/min electrolysis cell [17]. Reproduced with permission from The Electrochemical Society.
The flowchart of the EOP system is shown in Fig. 4. Tap water was passed through Na-type ion-exchange resin (Rohm and Haas, Amberlite IR-120B Na) to eliminate Ca2+ ions that lead to Ca2CO3 deposition on the anode and decrease the efficiency of the system. The ion-exchanged water is introduced into the anode and cathode at flow rates of 300-900 mL/min. Since the solubility and decomposition rate of ozone in water depend on water temperature, the temperature of the supplied water was controlled to 20 ± 1 ºC using a heat exchanger (HB Corporation, TEC1-127B). However, temperature control is unnecessary for the practical use of the system.
The voltage and current of the electrolysis cell are controlled by a programmable power supply (TDK-Lambda Corporation, ZUP20-40) and monitored by using an A/D converter (Pico Technology, ADC-16). Ozone water and gas phase ozone were sampled in a 1-cm-optical-length cell, and ozone concentration was spectroscopically determined using a compact UV-VIS absorptiometer (Shimazu UV mini-1240). Ozone-containing gas was sampled by replacing water in the optical cell. Although water droplets remained in the cell after the gas sampling, the absorption and scattering caused by the droplets were measured by replacing the captured O2 and O3 gas mixture with air, and they were then subtracted from the gas spectra. To convert the absorbance of ozone to ozone concentration, 2950 mol-1cm-1 at 258 nm was used as the absorption coefficient of ozone in the gas and liquid phases [6].
Figure 4.
Flowchart of conventional EOP system
2.2. Efficiency of conventional electrolysis cell
The conventional cell can produce highly concentrated ozone water in the initial operation period. The obtained ozone water shows clear absorption by O3. An example of O3 decay in the anode water is shown in Fig. 5. In this experiment, O3 electrochemically produced in the anode water was sampled in the optical cell, set in the UV-VIS absorptiometer, and measured of its decay every 10 min after the sampling time. As is evident from the spectra, ozone decay in the anode water is slow enough to neglect the sampling and scan times of the absorptiometer: The 1 min required for the absorption measurement does not affect the correct determination of ozone concentration. The typical half-life of ozone, 2 h in ion-exchanged water and 4 h in pure water, are in good agreement with those reported by Mizuno et al. [6].
Figure 5.
Time dependence of ozone absorption spectra [17]. Reproduced with permission from The Electrochemical Society.
To investigate the current efficiency of O3 production with the conventional cell configuration and to check its run time dependence, ion-exchanged water was electrochemically decomposed in the cell at 5 A (0.625 A/cm2) for 100 h. The flow rate of the anode water was 348 mL/min. The pH at the exit of the anode water was 6.0. The current efficiency of O3 production in anode water is plotted as a function of run time in Fig. 6(a). No gas phase ozone was included in the efficiency calculation; only water-phase ozone was accounted for.
Figure 6.
Time dependence of efficiency and reciprocal O3 concentration obtained using conventional cell [17]. Reproduced with permission from The Electrochemical Society.
As current efficiency decreased, voltage was increased from 6 V to 13 V during the experiment. The initial efficiencies of the system were markedly high: 13 to 15 % were observed in the first 1 h of operation. However, current efficiency decreased with run time to as low as 2 % after a 100 h operation. The initial ozone concentration was about 10 mg/L, but it decreased to 1.5 mg/L at the end of the run. It is clear that the lifetime of a conventional cell is on the order of 10 h. The O3 concentrations in Fig. 6(a) are converted to the reciprocal O3 concentration, 1/[O3] (L/mg), and are shown in Fig. 6(b). Such a figure is commonly used to estimate the order of reactions. The relation between 1/[O3] and run time was found to be linear except for the initial 2-3 h of operation. A rapid degradation of the system occurs in the initial operation, and a steady degradation of the system starts after the rapid one. The linear increase in 1/[O3] with run time after 2-3 h suggests the existence of a second-order reaction of the membrane with O3 or its byproducts.
2.3. Mechanism of Nafion decomposition
The Nafion 117 membrane is a transparent film before the EOP operation. However, this membrane became black after the time dependence experiment. The image of the degraded Nafion 117 membrane is shown in Fig. 7. The cathode side of the membrane has a severely damaged (black) part and a slightly damaged (metallic silver) part.
Figure 7.
Degraded Nafion 117 membrane after 100-h operation at 0.625 A/cm2 (view from cathode side).
FESEM images of the degraded Nafion 117 membrane are shown in Fig. 8. The anode side of the Nafion 117 membrane showed no morphological change, as shown in Fig. 8(a). However, holes were observed where the Nafion 117 membrane made contact with the Pt cathode, as shown in Fig. 8(b). The holes seem to be a result of the decomposition of the Nafion 117 membrane, and dendritic Pt nanoparticles were observed in the hole, as shown on the lower left side of Fig. 8(c).
EPMA measurements revealed that the Pt anode surface was covered with flakes of films consisting of Pt oxides, as shown in Fig. 9. The existence of Pt was also confirmed in the black region of the Nafion 117 membrane, but the composition could not be determined by EPMA because of the larger diameter of the probe beam of EPMA than of Pt nanoparticles. O, C, F and S signals originating from the Nafion 117 membrane also masked the real composition of Pt nanoparticles. However, semiquantitative XRF analysis indicated that the Pt concentration on cathode side of the Nafion 117 membrane is about four times higher than that on the anode side. It is clear that the decomposition of the Nafion 117 membrane and the subsequent formation of holes decrease the efficiency of ozone production in the system.
Figure 8.
FESEM images of degraded Nafion 117 membrane after time dependence experiment [17]. Reproduced with permission from The Electrochemical Society.
Figure 9.
Surface of anode Pt mesh after time dependence experiment.
To determine the distribution of Pt black nanoparticles in the Nafion 117 membrane, four Nafion 117 membranes were set in the cell in piles, and the electrolysis of ion-exchanged water was performed for 50 h. A constant voltage of 12 V was applied, and current decreased from 17 A to 7 A according to the degradation of the cell. The ozone production efficiency calculated from the ozone concentration in the anode water was 10 % in the initial state, which decreased to 2 % at the end of the run. After the experiment, the Nafion 117 membrane on the anode side was light gray and almost transparent, but that on the cathode side was completely black. The sheets between them showed gradual tanning toward the cathode side.
From the above observations, we concluded that the rapid degradation is caused by the oxidation of the Pt anode to form Pt oxides, such as PtO2. The formation of Pt oxides on the anode has been confirmed by the EPMA measurements. From the phase diagram of Pt in electrochemical equilibrium, PtO2 and PtO3 are expected to be stable substances in the electrolysis process where a voltage higher than 2 V is applied [19]. The poorer catalytic activity of Pt oxides than that of Pt probably decreases the current efficiency in this stage. During the oxidation of the Pt anode, ionic species, such as Pt(OH)x+, can be formed where the local potential is in the range of 0-1 V, since Pt(OH) is also stable in this lower-potential region [19]. The ionic species could be released from the Pt anode surface, electrophoresed through the membrane, and concentrated on the cathode side of the Nafion 117 membrane. Finally, the ionic species are reduced by hydrogen and may work as a catalyst to decompose the Nafion 117 membrane by a mechanism similar to that reported in a fuel cell system [20]. An image of such a degradation mechanism is shown in Fig. 10.
H2O2 formed either in the self-decomposition process of O3 or in the reduction of dissolved O2 by H2 should be a candidate source of OH radicals via Fenton’s reaction on reduced Pt particles that are deposited on the cathode in contact with the Nafion membrane. To check the former possibility, O3 self-decomposition simulation was performed using the WHD model [5]. A couple of the rate constants were adjusted to reproduce the O3 decay profiles observed in our experiments (Fig. 5). The H2O2 concentrations calculated in the self-decomposition of saturated ozone water are very low: 4.38 x 10-12 mol/L at 1 sec and 4.35 x 10-11 mol/L at 10 sec. Moreover, the concentrations of H2O2 at 1 sec and 10 sec linearly increase with increasing initial O3 concentration, and the second-order dependence of H2O2 formation with O3 concentration cannot be reproduced. Therefore, it is clear that H2O2 formed via the self-decomposition of ozone is irrelevant to the degradation mechanism of the system.
The electrochemical generation of H2O2 during water electrolysis is reported by Kusakabe et al. [21] and Yamanaka et al. [22]. In particular, the Nafion 117 membrane was used as the solid polymer electrolyte, and the successful reduction of dissolved O2 by H2 to form H2O2 was reported in the latter’s paper. Although the potentials applied in the latter’s paper, 0-0.6 V, are much lower than those in our experiments, the inevitable formation of H2O2 can be expected in our system. Moreover, both the O2 and H2 concentrations should be proportional to the O3 concentration in our degraded system, since a lower O3 production leads to a lower production of both O2 and H2. Therefore, the second-order formation of H2O2 with ozone concentration can be expected, and the second-order degradation of the system with ozone concentration can be explained by the electrochemical generation of H2O2.
Although we tried to check the H2O2 concentration in the cathode solution, the H2O2 concentration was too low to prove the existence of H2O2. The detectable limit of H2O2 in our lab is 0.02 mg/L, i.e., 6x10-7 mol/L. A detailed elucidation of the degradation mechanisms of the system was not possible in this study. Therefore, we note the constants obtained from the linear part of Fig. 6(b) for future study: the intercept of the y-axis, which corresponds to the initial ozone concentration in the steady-degradation mode, was 7.7 mg/L; the slope, which indicates the rate constant of the second-order reaction of the Nafion 117 membrane with ozone, was 0.0057 L mg-1 h-1.
Figure 10.
Mechanism of Nafion decomposition in electrolysis cell.
3. New EOP system
With the hypothesis that Pt nanoparticles deposited on the cathode side of the Nafion 117 membrane decompose the membrane, a new EOP cell was designed and tested for EOP operation. We inserted a quartz felt separator that captures Pt particles and prevents the degradation of the Nafion 117 membrane. Fundamental data for understanding the characteristics of the new electrolysis system were acquired during its first 50 h of operation, and the time dependence of current efficiency was measured in the subsequent experiment.
Figure 11.
Configuration of new electrolysis system
3.1. Insertion of quartz felt separator
The new electrolysis cell consists of a Nafion 117 membrane and a quartz felt separator sandwiched by Pt #80 mesh electrodes. Two sheets of quartz filters (Whatman, QM-A 2-335-05) are stacked in piles, and used as the separator. The thickness of the separator is about 1 mm under ambient condition. The configuration of the new cell is illustrated in Fig. 11(a). The quartz filter is labeled as “SiO2 microfiber filter” in the figure. To compensate for the low conductivity of the quartz separator, guaranteed reagent-grade NaCl and Na2SO4 (Kanto Chemical Co. Inc.) are used as cathode electrolytes (catholytes) without further purification. The purities of NaCl and Na2SO4 are more than 99 % and 99.5 %, respectively. These substances are dissolved in pure water, diluted to 0.01-1 M, and circulated through the cathode electrode at flow rates of 300-1000 mL/min by a water pump (Kamihata Fish Industries, Rio 50). The flowchart of the new system is shown in Fig. 11(b). The conductivities of the pure water and ion-exchanged water are 0.1 μS/cm and 240 μS/cm, respectively.
3.2. Characteristics of new EOP system
The O3 production rates in the anode water and in the gas phase obtained with two catholytes are shown in Fig. 12(a) as functions of current. The current efficiencies calculated from these data in Fig. 12(a) are plotted in Fig. 12(b). In the case of 0.01 M catholytes, water electrolysis did not proceed. When the concentrations of NaCl and Na2SO4 were increased above 0.1 M, efficient water electrolysis was observed regardless of the solute and concentration. However, as is shown in Fig. 12(b), the current efficiencies at currents lower than 4 A obtained with Na2SO4 solutions were inferior to those obtained with NaCl solutions. Therefore, the 0.5 M NaCl solution was used in the time dependence experiment. The origin of such a discrepancy is further investigated, and explained in section 4. O3 production rates in the anode water were about two times higher than those in the gas phase regardless of the solute and concentration, and they showed a saturation behavior in the high-current region, while those in the gas phase increased in contrast [Fig. 12(a)].
Current efficiencies of more than 20 % were obtained at currents higher than 6 A. The efficiency is higher than those reported by other researchers who used Pt or PbO2 electrodes, and close to that obtained using diamond electrodes. The power efficiency at 6 A and 7.74 V using the 0.5 M NaCl catholyte was 220 kWh/kg-O3.
Figure 12.
O3 production rates and current efficiencies of new electrolysis cell [17]. Reproduced with permission from The Electrochemical Society.
Since both current and power efficiency are functions of water flow rate and applied current, high efficiencies can be expected when water flow rate and current increase. An example of the relationship between water flow rate and current efficiency calculated from the ozone concentration in the anode water is shown in Fig. 13. In the experiments, the 0.5 M NaCl catholyte was circulated on the cathode side of the cell. Current efficiency increases with increasing water flow rate and current. At currents higher than 10 A, O3 concentration starts to saturate in the anode water, and the differences in current efficiency become difficult to detect. If the water flow rate is increased to more than 1000 mL/min, and if O3 becomes unsaturated in the anode water, the differences in current efficiency between 10 A and 14A would be apparent. A high voltage, such as above 7 V, applied to our system gave sufficient overpotential for O3 production, and was probably one of the origins of good current efficiency. However, ozone concentration decreases with increasing water flow rate. Therefore, appropriate conditions should be chosen according to the required ozone concentration and water flow rate for practical use.
One of the possible efficiency loss is due to the solution resistance and/or contact resistance in the system. To measure such a loss, the overall applied voltage and potential difference between the Pt anode and cathode side of the Nafion 117 membrane were measured by placing electric wires on them using tiny Kapton tapes. The overall voltage and potential difference between the electrodes were measured at various currents using 0.5 M NaCl catholyte. Representative results are shown in Fig. 14. The potential difference between the anode and cathode side of the membrane was roughly 30 % smaller than the overall voltage applied to the anode and cathode terminals of the cell. Since observable O3 evolution occurs at a current of 1 A, as shown in Figs. 12 (a) and (b), nearly 4 V of the potential difference is required for EOP in the new system, suggesting that the involvement of the reaction in Eq. (3) is important for effective EOP.
Figure 13.
Current efficiency of O3 formation in anode water as a function of water flow rate and current [17]. Reproduced with permission from The Electrochemical Society.
Figure 14.
Relationship between applied voltage and potential difference of the electrodes
3.3. Lifetime of new system
After the characterization of the new system for 50 h, the accelerated degradation experiments were carried out using the 0.5 M NaCl catholyte. The flow rates of the anode water and catholyte were both 500 mL/min. A high current of 14 A was applied to accelerate the degradation of the system. The O3 concentration in the anode water showed little degradation for 150 h of operation, as shown in the upper plots in Fig. 15. O3 concentrations of 12 to 15 mg/L and a current efficiency of 18 to 20 % were maintained during the operation with a quartz felt separator. In this figure, the data in Fig. 6(a), which were obtained at a mild current of 5 A, are also plotted to compare the O3 concentrations in the conventional and new cells. The separator works to maintain the high efficiency of the system and supports our hypothesis concerning the degradation mechanism. Provided that the degradation is a second-order process with O3 concentration and that the O3 concentration becomes one third at 4A compared with that at 14 A, as shown in Fig. 12(a), the lifetime at 4 A will be nine times longer than that at 14 A. Therefore, a stable operation of more than 1350 h (150 h multiplied by 9) could be expected under low-current operation.
Figure 15.
Run time dependence of O3 concentration obtained in conventional and new cells [17]. Reproduced with permission from The Electrochemical Society.
The concentrations of ionic species in the anode water sampled in the middle of the run time dependence measurement were analyzed by ion chromatography (IC) and atomic absorption spectrometry (AAS), and the results are summarized in Table 1. Ion concentrations at the exit of the anode water were almost the same as those at the exit of the ion-exchange resin, i.e., no significant change in ion concentration was detected except for small changes in Cl- and Na+ concentrations: Cl- concentration slightly increased from 11.3 ppm at the exit of the ion exchange resin to 13.4 ppm at the exit of the anode water; on the other hand, Na+ concentration decreased from 31.5 ppm at the exit of the ion exchange resin to 24.5 ppm at the exit of the anode water. The pHs of the anode water and cathode water in the middle of the run were 5.8 and 13.2, respectively. These changes were caused by the electrophoretic migration of ionic species.
The migration of the anode water associated with proton transfer through the membrane to the cathode side was also confirmed. The volume change in the circulated cathode solution was measured after operating for 42 h at 10 A. The volume increased from 1 L at the initial stage to 2 L after the operation. Therefore, the adjustment of the volume and pH of the cathode water is required at one or two day intervals during long runs of the system.
Ion
Tap water
Exit of ion- exchange resin
Exit of anode
(tw ppm) Method of analysis
Cl-
11.5
11.3
13.4
IC
NO3-
8.1
7.7
7.8
IC
SO42-
20.4
21.0
21.1
IC
Na+
7.6
31.5
24.5
AAS
K+
1.9
0.1
0.1
AAS
Mg2+
4.0
0.0
0.0
IC
Ca2+
19.9
0.0
0.0
IC
NH4+
0.0
0.2
0.2
IC
Table 1.
Ion concentrations in tap water, exit of ion-exchange resin, and exit of anode water (IC: ion chromatography, AAS: atomic absorption spectrometry) [17]. Reproduced with permission from The Electrochemical Society.
4. Selection of catholyte
The key questions arising from the experiments in the above sections are as follows:
We noticed that the lifetime of the EOP system using the Na2SO4 catholyte is much shorter than that using the NaCl catholyte. How do these catholytes affect the lifetime of the system?
How does the NaCl catholyte concentration affect the efficiency and lifetime of the system?
The dissolution of the anode Pt electrode will be the limiting factor for the lifetime of the NaCl catholyte system after eliminating the problem of the decomposition of the membrane using the quartz felt separator. What is the dissolution rate of the Pt anode, and how long can the system be operated without changing the anode?
The contaminant concentrations in the ozone water produced from ion-exchanged water are lower than the allowable levels for drinking water. If pure water is introduced into the anode instead of ion-exchanged water, what will be the contaminant concentrations? Will the contaminants be at allowable concentrations for the sterilization of medical instruments and for the cleaning of electronic components?
To answer the above important questions, the dependences of catholyte composition and concentration on the efficiency and lifetime of the EOP system were investigated, and the dissolution rate of the Pt anode and the contaminant concentration in ozone water were measured. All the experiments were carried out using pure anode water instead of ion-exchanged water, because the analysis of ions that migrated from the catholyte to the anode Pt surface seemed to be essential to understand the effects of the catholyte on the efficiency and lifetime of the system. The other experimental setup and measurements used are the same as these in the last section (see Fig. 11).
4.1. Effects of catholytes on EOP efficiency
Pure water produced by passing tap water through Na-type ion-exchange resin (Rohm and Haas, Amberlite IR-120B Na) and reverse osmosis (Dow Chemical, FILMTEC TW30-2514) is introduced into the anode at a flow rate of 300 mL/min. The conductivity of the pure water is less than 0.1 μS/cm. The residence time and flow speed of the anode water in the cell are estimated to be 0.085 s and 0.94 m/s, respectively. The O3 concentrations obtained using 0.5 M NaCl and 0.5 M Na2SO4 catholytes as functions of current density are shown in Figs. 16(a) and (b), respectively. In these experiments, current was slowly increased and held for 20 min to stabilize the system at each measurement point. Therefore, 2 h was required to obtain a series of NaCl or Na2SO4 data. The holding time should be long enough to observe the degradation of the system, if any.
When the NaCl catholyte was used, the O3 concentrations in the anode water and gas phase increased with current, as shown in Fig. 16(a). The concentration of ozone water was higher than 20 mg-O3/L-water (mg/L) at a current higher than 1 A/cm2 (8.0 V); and it was 24.8 mg/L at 1.25 A/cm2 (9.1 V). On the other hand, the O3 concentrations obtained using the Na2SO4 catholyte were lower than those obtained using the NaCl catholyte in the entire current range in Fig. 16(b). No O3 concentration in the anode water higher than 20 mg/L was obtained using the Na2SO4 catholyte. Moreover, the O3 concentrations in the anode water and gas phase decreased at 1.25 A/cm2, suggesting that the system was degraded within 2 h of the measurement.
Figure 16.
O3 production rates in anode water and gas phase
The current efficiencies calculated from the data in Fig. 16 are plotted in Figs. 17. As seen in Fig. 17(b), the current efficiencies obtained using the Na2SO4 catholyte were lower than those obtained using the NaCl catholyte [Fig. 17(a)]. Although the electric conductivity of a Na2SO4 electrolyte, 8.23 Sm-1, is higher than that of a NaCl electrolyte, 7.06 Sm-1, at 1 M and 20 ºC, no improvement in current efficiency was observed in the experiments. The origin of the differences observed in the EOP efficiencies caused by changing the catholyte will be discussed in section 4.4.
Figure 17.
Current efficiency of O3 production
The current efficiencies shown in Fig. 17(a) were higher than those reported in the previous data in section 3.2. The current efficiency of 29 % at 0.5 A/cm2 (6.6 V) was markedly high, and efficiencies higher than 25 % were obtained in the entire current range. The O3 production rates in the anode water and gas phase at 0.5 A/cm2 were measured to be 274 mg/h and 52 mg/h, respectively. From these data, the power efficiency of the system at 0.5 A/cm2 was determined to be 76 kWh/kg-O3, which is lower than that (220 kWh/kg-O3) obtained in a previous experiment and those reported using other anode materials in EOP systems. The theoretical electric power required for a perfect EOP system is estimated to be 5.06 kWh/kg-O3, supposing 100 % consumption of the current for producing O3 and the standard voltage of O3 formation, 1.511 V. Further improvement in power efficiency is possible by introducing other measures, such as O2 gas recycling into the cathode water [23]. However, the cost of electricity is reasonably low even in the present EOP system. The NaCl catholyte system can produce 1 kg-O3 at an electricity cost of 7 US dollars assuming that the price of electricity is 10 cents/kWh. 100 kL of 10 mg/L-ozone water will be produced from 1 kg-O3.
4.2. Effects of catholytes on lifetime
To observe the time dependence of the O3 concentration and to check the morphological change of the Pt anode after a long operation, accelerated degradation experiments were performed. During the experiments, the flow rates of the anode water and the catholytes were both set to 300 mL/min, and a high current of 1.25 A/cm2 (10 A) was applied to accelerate the degradation. The concentrations of the NaCl and Na2SO4 catholytes were set to 0.5 M. The concentrations of Na+, Cl- and SO42- in the anode water were measured in the middle of the accelerated-degradation experiments, and are summarized in Table 2.
Ion
Na2SO4 0 A
Na2SO4 10 A
NaCl 10 A
(wt ppm) Method of analysis
Cl-
0.06
0.01
7.4
IC
SO42-
0.14
1.15
-
Na+
0.11
-
0.02
K+
0.04
-
-
Ca2+
0.02
0.01
0.01
NH4+
0.01
0.01
0.01
Pt
0.023 wt ppb
0.13 wt ppb
0.62 wt ppb
ICP-MS
Table 2.
Impurity concentrations in anode water sampled during electrolysis at 0 A and 10 A using the Na2SO4 and NaCl catholyte. (Method of analysis: IC, ion chromatography; ICP-MS, inductively coupled plasma mass spectrometry)
The ion concentrations at the exit of the anode water sampled at 0 A/cm2 using the Na2SO4 catholyte should be the same as those in pure water, except for slight increases in SO42- and Na+ concentrations. These ions came from the catholyte by diffusing through the Nafion 117 membrane. SO42- concentration increased to 1.15 wt ppm at an electrolysis current of 10 A (1.25 A/cm2) using the Na2SO4 catholyte. Cl- concentration also increased to 7.4 wt ppm, and Na+ concentration decreased to 0.02 wt ppm at 10 A (1.25 A/cm2) using the NaCl catholyte. These changes were caused by the electrophoretic migration of these ionic species.
The results of the degradation experiments are shown in Fig. 18. Note that the O3 production in the gas phase is not included in the plots. When the 0.5 M NaCl catholyte was used, the O3 concentration showed only a slight degradation during the operation for 130 h, as is shown in the upper plots in Fig. 18. A high O3 concentration ranging from 20 to 25 mg/L was maintained during the experiment (the Cl- concentration in the anode water was 7.4 ppm). The slight decrease in the concentration is probably due to the dissolution of the Pt anode electrode resulting in poor contact between the anode and the Nafion 117 membrane. However, the use of the 0.5 M Na2SO4 catholyte caused a rapid decrease in O3 concentration: The initial concentration of 18 mg/L decreased to 7 mg/L after operating the system for 60 h, as shown in the lower plots in Fig. 18. In this experiment, only 0.01 wt ppm Cl- was found in the exit of anode water. The degradation data reported in section 3.3 using ion-exchanged water and the 0.5 M NaCl catholyte are also shown in the middle plots in Fig. 18. A stable EOP was observed in our previous experiment (the SO42- and Cl- concentrations in the anode water were 21.1 ppm and 13.4 ppm, respectively) [17].
Figure 18.
Run time dependence of O3 concentration in anode water. The data set indicated by “*” is the same as that in Fig. 15.
After the accelerated-degradation experiments, the anode surface in contact with the Nafion 117 membrane became brown when the 0.5 M Na2SO4 catholyte was used. In contrast, the anode surface was clean and only a small portion of that in contact with the Nafion 117 membrane became yellow when the 0.5 M NaCl catholyte was used. SEM images of the Pt anode electrodes after the degradation experiments are shown in Fig. 19. As is clear in Fig. 19(c), the surface of the anode electrode in contact with the Nafion 117 membrane was covered with an approximately 10-μm-thick oxide layer, when the system was operated using the 0.5 M Na2SO4 electrolyte. The surface of the anode electrode remained clean [Fig. 19(a)], and only a thin layer of oxide deposit was observed in a small area [Fig. 19(b)], when the system was operated using the 0.5 M NaCl catholyte.
The above oxide layers were assigned to amorphous PtO2 (a-PtO2) by microscopic Raman spectroscopy, in which the characteristic broad spectrum of a-PtO2 peaking at 600 cm-1 with an FWHM of 200 cm-1 was observed. No sharp peak originating from PtO or α-PtO2 crystals was observed [24]. EDX measurements indicated that 3.5 wt % Cl was present in the a-PtO2 layer formed using the 0.5 M NaCl catholyte [Fig. 19(b)]. Because our EDX apparatus could not detect oxygen, the rest of the material measured in the oxide layer was 96.5 wt % Pt. No Cl was observed in the oxide layer formed using the 0.5 M Na2SO4 catholyte. The high concentration of Cl adsorbed onto the a-PtO2 layer suggests that Cl- that migrated on the anode surface inhibited the formation of the a-PtO2 layer. When chloride is added to a sulfuric medium, its adsorption inhibits the formation of oxide films in the low-voltage range, i.e., at voltages lower than 1.5 V vs RHE [25, 26]. Because the anode voltages applied in our EOP system were much higher than those in the literature, a more efficient adsorption of chloride is expected.
SEM images of the captured Pt particles on the cathode side of the quartz separator are shown in Fig. 20. Small and spinous Pt particles were captured in the separator using the 0.5 M NaCl catholyte, as shown in Fig. 20(a). On the other hand, granular Pt aggregates were observed using the 0.5 M Na2SO4 catholyte, as shown in Fig. 20(b). The difference in the shape of the captured Pt particles suggests that the precursors of the particles may be different. However, further analysis should be required to determine the origin of the difference in the shape of the captured Pt particles.
Figure 19.
SEM images of Pt anodes after accelerated degradation experiments
Figure 20.
SEM images of captured Pt particles in quartz felt separator
4.3. Effects of NaCl concentration
The effects of catholyte concentration on the current efficiency and lifetime of EOP were measured by changing the NaCl catholyte concentrations from 0.003 M to 0.085 M. These measurements were performed at a constant current density of 0.625 A/cm2 (5 A) for 20 h. The results are shown in Fig. 21. The current efficiency of EOP increases with increasing concentration of the NaCl catholytes probably owing to the increase in catholyte conductivity. The decrease in the current efficiency with run time was observed for NaCl concentrations lower than 0.028 M. The efficiency linearly decreases with run time using these dilute catholytes. However, in the case of the 0.085 M catholyte, the degradation speed of the system became lower, and the efficiency was kept higher than 23 % for 20 h. This efficiency is similar to that obtained using the 0.5 M NaCl catholyte [Fig. 17(a)], and the low degradation speed is indicative of the realization of the long and stable operation of the system. Therefore, a NaCl concentration higher than 0.085 M is adequate for the EOP system.
Figure 21.
Concentration dependence of catholyte on the efficiency and lifetime measured at 0.625 A/cm2 (5 A).
4.4. Inhibition mechanism of Pt oxide formation
From these observations, we conclude that Cl- that migrated from the catholyte to the anode surface inhibited the formation of the a-PtO2 layer. As a result, the anode surface was kept clean during the accelerated degradation experiments. The poor performance of the system using the Na2SO4 catholyte, shown in Figs. 16(b), 17(b) and 18, suggests that the anodes were rapidly oxidized from the beginning of the experiments owing to the absence of Cl-. Therefore, Cl- that migrated from the catholyte to the anode surface is indispensable for a long and stable operation of the EOP system, whereas SO42- concentration is probably irrelevant for long lifetime as long as sufficient amount of Cl- is supplied to the anode, as shown in the middle plots in Fig. 18.
The standard electrode potentials for chlorination and oxidation reactions of Pt are as follows [27]:
Pt + 4Cl−→[PtCl4]2−+ 2e−\tE0=0.758 VE15
\n\t\t\t\t
[PtCl4]2−+ 2Cl−→[PtCl6]4−+ 2e−\tE0=0.726 VE16
\n\t\t\t\t
Pt + H2O→PtO + 2H++ 2e−\tE0=0.980 VE17
\n\t\t\t\t
PtO + H2O→PtO2+ 2H++ 2e−\tE0= 1.045 V.E18
Because chlorination reactions take place at lower potentials than oxidation reactions, Pt is chlorinated, ionized, and dissolved in anode water in the presence of Cl- ions. Thus, the formation of Pt oxide films can be inhibited.
If the efficiency of the system is degraded by the formation of the a-PtO2 layer on the anode surface using the Na2SO4 catholyte, an important question arises: Does the a-PtO2 layer have a lower catalytic activity than Pt in EOP reactions? The formation mechanisms and resultant structures of oxide layers on a Pt anode have been extensively studied to understand the electrocatalytic properties of such layers [28-34]. Pt oxide layers are considered to have slightly beneficial effects on oxygen evolution reactions. Shibata reported that the oxidation treatment of a Pt anode surface with 1 M H2SO4 solution for 28 h enables a stable electrolysis activity of the Pt anode for more than 30 h [28]. Tremiliosi-Filho et al. reported that the rate of O2 evolution increases with the period of oxide film formation at various electrolysis potentials [33]. Gottesfeld et al. found that Pt oxide films formed after a long-term polarization of potential between 2.1-2.25 V vs. RHE in 0.5 M H2SO4 solution could be identified as film β (it is now assigned to a-PtO2), and that the films shows good electric conductivity as well as an improvement in the performance of oxygen evolution reaction with the lowering of the oxygen evolution voltage from 2.1 V to 2.0 V at a current of 10 mA/cm2 [31]. Therefore, we did not concern ourselves with the negative effect of Pt oxides on EOP reactions.
If Pt oxide layers significantly decrease oxygen overpotential in water electrolysis reactions and lead to higher O2 evolution, the efficiency of O2 formation would increase and the negative effect on EOP could be expected. To determine the effects of a-PtO2 layers on EOP, we are now conducting experiments to measure the electric properties and electrocatalytic activities of a-PtO2 layers by fabricating such layers on the Pt anode. We have found that a-PtO2 layers have a much lower catalytic activity than pure Pt in EOP reactions; the results will be reported soon [35].
4.5. Current balance
The current balance of the 0.5 M NaCl catholyte system was measured and checked by comparing the calculated currents for O3 production in the anode water and gas phase as well as those for O2 production. In this experiment, the concentration of dissolved O2 in anode water was measured using an electrochemical O2 sensor (Hach Ultra, Orbisphere 3600 and 31124). The O2 sensor was calibrated to match the current balance of the system at high current density operation. The gas phase O2 production rate was obtained by subtracting O3 production from the overall anode gas production. To double-check the current balance, we tried to measure H2 production rate. However, it was not possible to capture H2 gas in a cylinder, because H2 formed very tiny bubbles that dispersed in the catholyte. Current balances calculated at seven currents are summarized in Fig. 22. The calculated currents are in good agreement with the measured ones, i.e, the 100 % line in Fig. 22, except that in the current range lower than 0.25 A/cm2, where the low gas production rates could be the source of error. As is clear from Fig. 22, the system has neither an apparent current leakage nor markedly abundant products other than O3, O2, and H2. More than 70 % of the current was consumed in producing O2. It is also clear from Fig. 22 that the current consumption for O2 formation in anode water decreases with applied potential, while that for O3 formation in gas phase and anode water increases. Therefore, it is suggested that the oxidation of O2 in anode water to O3 [Eq. (3)] is the dominant process for O3 formation. However, more detailed kinetic studies on O2 and O3 formation reactions, such as the branching ratio of Eqs. (2) and (3), will be required for the further understanding of the system.
4.6. Pt dissolution rate and lifetime of new system
The weight of the Pt anode mesh was about 0.4 g before the experiments. They decreased by 5.4 mg and 7.7 mg, corresponding to dissolution rates of 5.4 μg/Ah and 6.9 μg/Ah, after the accelerated experiments using the Na2SO4 and NaCl catholytes, respectively. These values are in agreement with the reported one, 5 μg/Ah, in which the corrosion rate of the Pt electrode with a 1 cm2 surface area was measured in the H2SO4 electrolyte up to 1 M [36]. It is clear from Fig. 19(a) that the anode surface in contact with the Nafion 117 membrane dissolved during the operation, and that the surface area of our Pt anode mesh in contact with the Nafion 117 membrane was also on the order of approximately 1cm2. Although the accuracy was not sufficient for the precise comparison of Pt concentrations in anode water listed in Table II because of the infinitesimal quantity of Pt determined by ICP-MS, the tendency coincides with the results of the weight measurements: The Pt concentration obtained using the 0.5 M NaCl catholyte is higher than that obtained using the 0.5 M Na2SO4 catholyte. From these observations it can be assumed that the Pt anode dissolves in anode water faster when the NaCl catholyte was used than when the Na2SO4 electrolyte is used. In the case of electrolysis using the NaCl catholyte, Pt mass balance calculation indicates that 20 % of the dissolved Pt is carried out of the system in anode water, and the rest of the dissolved Pt diffuses and migrates to the cathode side. The lifetime of the system can be estimated in terms of Pt weight loss that leads to poor contact between the anode and the Nafion 117 membrane. Because the Pt anode is pressed against the membrane surface using a Ti #40 mesh and a Ti terminal plate, the contact can be maintained fairly well when the Pt weight loss is small. If a 10 % loss in Pt weight is allowed to maintain good contact, the system can be operated up to a weight loss of 40 mg which corresponds to an operation period of 5800 Ah, i.e., 2900 h of operation at 0.25 A/cm2, using the NaCl catholyte. 6 mg-O3/L ozone water will be obtained at a current density of 0.25 A/cm2, and the system can be operated for 8 h a day for 365 days without changing the anode.
Figure 22.
Current consumption balances calculated from production rates of O3 and O2 (0.5 M NaCl catholyte was used).
5. Impurities in ozone water
Impurity levels in the exit of the anode water obtained using ion-exchanged water are comparable to those in drinking water, as shown in Table 1. Therefore, ozone water should be safe enough for family use, such as gargles and washing hands. On the other hand, the concentrations of the impurities listed in Table 2, such as Na+, Ca2+, and NH4+ in anode water using the 0.5 M NaCl catholyte, are on the order of 0.01-0.02 wt ppm. They are much lower than those in ion-exchanged water (see Table I). The ozone water obtained in the pure water system consists of highly concentrated O3 and 7.4 wt ppm Cl-. Therefore, ozone water can be used in various processes, such as the sterilization of medical instruments. Because Cl- is undesirable in the cleaning processes of electronic components, Cl- washout will be required after using the ozone water for such processes.
6. Conclusion
The efficiency of ozone production using a conventional Pt electrode system decreased with run time. The linear increase in 1/[O3] with run time after 2-3 h suggests the existence of a second-order reaction of the Nafion 117 membrane with ozone or its decomposition products. The degradation was caused by Pt particles concentrated on the cathode side surface of the Nafion 117 membrane. The particles catalyze the reaction between the Nafion 117 membrane and ozone to produce holes on the membrane’s cathode side surface. To prevent the decomposition of the membrane, a quartz felt separator was inserted between the Nafion 117 membrane and the cathode electrode. As a result, a high current efficiency of more than 20 % and a lifetime longer than 150 h have been achieved in the new electrolysis system. The quartz felt separator succeeded in capturing Pt particles and preventing the membrane from decomposition.
Then the effects of the NaCl and Na2SO4 catholytes on the current efficiency and lifetime of the new EOP system were investigated using pure anode water. When a 0.5 M NaCl catholyte was used, a high current efficiency of 29 % and a high power efficiency of 76 kWh/kg-O3 were achieved at an electrolysis current of 0.5 A/cm2 (6.6 V). The accelerated degradation experiments indicated that the combined use of pure anode water and the 0.5 M NaCl catholyte kept the Pt anode surface clean and enabled an efficient operation longer than 130 h. On the other hand, the Pt anode surface in contact with the Nafion 117 membrane was covered with a 10-μm-thick a-PtO2 film and the efficiency decreased from 13 % to 5 % after the accelerated experiments for 60 h using the Na2SO4 catholyte. It is suggested that the formation of a-PtO2 films decreases the electrocatalytic activity of the Pt anode in the O3 formation reaction. The NaCl concentration dependence measurements indicate that NaCl concentrations higher than 0.085 M will be adequate for the EOP system. Pt dissolution rates of 5.4 μg/Ah and 6.9 μg/Ah were measured using the Na2SO4 and NaCl catholytes, respectively. If a 10 % loss in Pt weight is allowed to maintain good contact between the anode and the Nafion 117 membrane, the system using the NaCl catholyte can be operated for 5800 Ah, i.e., 8 h per day for 365 days at 0.25 A/cm2 without changing the anode. The long lifetime, good current and power efficiencies, very low impurity concentrations, the compactness of equipment and low-voltage operation achieved by the new system will enable a wide expansion of the application of ozone water.
7. Future research
Important breakthroughs remain to be achieved. The authors plan to develop the following advanced EOP technologies: a direct tap water electrolysis system that does not require the use of ion-exchange resin or reverse osmosis; a once-through catholyte flow system that can eliminate the formation and discharge of a strongly alkaline catholyte solution; an effective utilization system of gas phase ozone; a new and effective anode catalyst system. Such kinds of improvements will be achieved soon, because we have ideas and strategies for realizing advanced systems.
To clarify the effectiveness of ozone water for sterilization processes, we are now conducting experiments and developing a simulation model to obtain the orders and rate constants of the reactions of O3 with bacteria. The results will be reported soon [37].
Acknowledgement
The authors thank Prof. Y. Yamaguchi, Associate Prof. Y. Tsuji and Assistant Prof. S. Inasawa of the Department of Chemical Systems Engineering, University of Tokyo, for their kind help in the instrumental analysis and for the fruitful discussion. Measurements of the impurity concentrations in water were conducted by the analytical laboratory group in the 5th building of the School of Engineering, University of Tokyo. The content in section 4 was published in Electrochim. Acta 78, pp. 495-501, K. Naya and F. Okada, “Effects of NaCl and Na2SO4 cathode electrolytes on electrochemical ozone production” [18], Copyright Elsevier (2012).
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Introduction",level:"1"},{id:"sec_2",title:"2. Conventional electrolysis system and its performance",level:"1"},{id:"sec_2_2",title:"2.1. Structure of conventional electrolysis cell",level:"2"},{id:"sec_3_2",title:"2.2. Efficiency of conventional electrolysis cell",level:"2"},{id:"sec_4_2",title:"2.3. Mechanism of Nafion decomposition",level:"2"},{id:"sec_6",title:"3. New EOP system",level:"1"},{id:"sec_6_2",title:"3.1. Insertion of quartz felt separator ",level:"2"},{id:"sec_7_2",title:"3.2. Characteristics of new EOP system",level:"2"},{id:"sec_8_2",title:"3.3. Lifetime of new system",level:"2"},{id:"sec_10",title:"4. Selection of catholyte ",level:"1"},{id:"sec_10_2",title:"4.1. Effects of catholytes on EOP efficiency",level:"2"},{id:"sec_11_2",title:"4.2. Effects of catholytes on lifetime",level:"2"},{id:"sec_12_2",title:"4.3. Effects of NaCl concentration",level:"2"},{id:"sec_13_2",title:"4.4. Inhibition mechanism of Pt oxide formation",level:"2"},{id:"sec_14_2",title:"4.5. 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Meeting of Japanese Society for Functional Water, in Japanese.'},{id:"B5",body:'WittmannG.HorvathI.DombiA.2002Ozone Science & Eng. 24: 281.'},{id:"B6",body:'MizunoT.TsunoH.YamadaH.2007Ozone Sci. & Eng. 29: 55.'},{id:"B7",body:'StuckiS.TheisG.KotzR.DevantayH.ChristenH. J.1985J. Electrochem. Soc. 132(2): 367.'},{id:"B8",body:'FengJ.JohnsonD. C.LoweryS. N.CareyJ. J.1994J. Electrochem. Soc. 141: 2708.'},{id:"B9",body:'Santana MHP, De Faria LA, and Boodts FC2004Electrochim. Acta 49: 1925.'},{id:"B10",body:'Cheng SA, and Chan KY2004Electrochem. and Solid-State Lett. 7(3): 134.'},{id:"B11",body:'WangY. H.ChengS.ChanK. Y.LiX.XY(2005J. Electrochem. Soc. 162: D197.'},{id:"B12",body:'AwadM. I.SetaS.KanedaK.IkematsuM.OkajimaT.OhsakaT(2006Electrochem. Commun. 8: 1263.'},{id:"B13",body:'AriharaK.TerashimaC.FujishimaA.2006Electrochem. and Solid-State Lett. 9(8): D17.'},{id:"B14",body:'KitsukaK.KanedaK.IkematsuM.IsekiM.MushiakeK.OhsakaT.2010J. Electrochem Soc. 157: F30.'},{id:"B15",body:'MarselliB.Garcia-GomezJ.MichaudP. A.MARodrigoComninellisC.2003J. Electrochem. Soc. 150(3): D79.'},{id:"B16",body:'Edby.AllenJ.Bard1974Encyclopedia of Electrochemistry of the Elements, Vol. II, Chapter II-5: Oxygen”: M. Dekker, New York.\n\t\t\t'},{id:"B17",body:'OkadaF.NayaK.2009J. Electrochem. Soc. 156: E125.'},{id:"B18",body:'NayaK.OkadaF.ElectrochimActa.'},{id:"B19",body:'PourbaixM.1966Atlas of Electrochemical Equilibria in Aqueous Solutions”: Pergamon Press.'},{id:"B20",body:'MiyakeN.WakizoeM.HondaE.OhtaT.2005ECS Transactions 1(8): 249.'},{id:"B21",body:'KusakabeK.KawaguchiK.MaeharaS.TanedaM(2007J. Chem. Eng. Japan 40(6): 523.'},{id:"B22",body:'YamanakaI.MurayamaT.AngrewChem(2008Int. Ed. 47: 1900.'},{id:"B23",body:'OndaK.OhbaT.KusunokiH.TakezawaS.SunakawaD.ArakiT.2005J. Electrochem. Soc. 152: D177.'},{id:"B24",body:'McBride JR, Graham GW, Peters CR, and Weber WH1991J. Appl. Phys. 69: 1596.'},{id:"B25",body:'BirssV. I.ChangM.SegalJ(1993J. Electroanal. Chem. 355: 181.'},{id:"B26",body:'Novak DM and Conway BE1981J. Chem. Soc. Faraday Trans. I, 77: 2341.'},{id:"B27",body:'Ed.by the Electrochemical Society of Japan (2000Denki-Kagaku Binran 5th Edition”: Maruzen, in Japanese.'},{id:"B28",body:'ShibataS.1963Bull. Chem. Soc. Japan 36: 525.'},{id:"B29",body:'ShibataS.1964Bull. Chem. Soc. Japan 37: 410.'},{id:"B30",body:'ShibataS.1976Electrochimica Acta 22: 175.'},{id:"B31",body:'GottesfeldS.YarivM.LaserD.SrinivasanS.1977J. Phys. Colloques 38: C5145'},{id:"B32",body:'GottesfeldS.MaiaG.FlorianoJ. B.Tremiliosi-FilhoG.TicianelliE. A.GonzalezE. R.1991J. Electrochem. Soc. 138: 3219.\n\t\t\t'},{id:"B33",body:'Tremiliosi-FilhoG.JerkiewiczG.ConwayB. E.1992Langmuir 8: 658.'},{id:"B34",body:'JerkiewiczG.VatankhahG.LessardJ.SoriagaM. P.ParkY. S.2004Electrochimica Acta 49: 1451.'},{id:"B35",body:'OkadaF.TsujiY.NayaK.in preparation.'},{id:"B36",body:'OtaK.NishigoriS.KamiyaN.1988J. Electroanal. 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1. Introduction
In this modern era, there has been a lot of development and upgradation in the field of Internet of things (IoT). The development has been actively growing in various sectors, for instance, industries, healthcare, education, agriculture, just to name a few. Consequently, it creates more employment opportunities. Thus, IoT should come into intensive usages with very careful awareness. The development of IoT programs can be done in various ways into the society. Although there have been a plenty of IoT development and applications, only very few academic institutions throughout the world are fully IoT equipped in their laboratories.
Since 2019, the COVID-19 pandemic has resulted in shutdown of educational institutions across the world. Globally, over 1 billion children are out of the classroom. As a result, education has changed dramatically with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. While lecture sessions have been quite well organized, the laboratory sessions might be still challenging and dependent on the types of laboratories. To overcome this problem for an IoT design class, a virtual laboratory can be implemented. With a virtual laboratory, a laboratory session can be launched with flexibility of time. Self-learning process can be achieved by becoming more self-determining. It also supports collaborative group works and helps us develop critical thinking skills while doing individual assignments. The most important advantage is that it can be accessed from any location through Internet. Thus, any materials from the Internet can also be used. Nevertheless, there are some challenges in the virtual laboratory, which includes setting up the virtual laboratory environment, lack of social interaction between students and instructors, accessibility to various laboratory required technologies, software requirement and software inconsistency, student’s issues while teaching simultaneously, etc. A visual programming language (VPL) is any programming language that lets users create programs by manipulating program elements graphically rather than by specifying them textually. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. Many VPLs known as dataflow programming are based on the idea of boxes and arrows, where boxes or screen objects are treated as entities connected by arrows, lines, or arcs representing relations. Different prototyping boards have its own programming language, such as C, Python, Java, and similar. To develop an IoT system, at least one of these programming languages is required. However, it is fortunate that during these days, several VPLs have been developed to help us start programming without knowing the programming language. These IoT visual programming tools have a user-friendly approach of programming. It has a graphical user interface (GUI), where the user can just drag and drop moving code blocks and execute a simple piece of logic.
Node-RED VPL is an open-source software, which is a flow-based development tool originally developed by International Business Machines (IBM) for wiring hardware devices together with application programming interface (API) and few online services as part of the IoT. Node-RED can be used flexibly under the Apache2 license. Some developed their own services based on Node-RED, while others changed to their own user interface (UI) and deployed it as built-in. It can be established as a platform where we can publish our own developed node so that anyone can use it. The open-source software is an alternative to interact with the class. Students can practice, study, and understand with basic engineering skills or even develop an IoT-based system with artificial intelligence (AI) capabilities, e.g., prediction, regression, clustering, and classification. It can be installed locally on a personal computer (PC) or a laptop. With its simplicity for learning and using as well as several built-in entities, it can be used in evolving innovative platforms providing ability to assign code to all interfaces with less coding complexity. In addition, it supports several IoT prototyping board such as Arduino, Raspberry, and Android as well as cloud-based platforms.
The objectives of the book chapter are as follows.
To describe the environment and features of Node-RED software with its abilities of numerous functions.
To deliver a complete outlook on each function of Node-RED that can be applied for an IoT-based virtual laboratory.
To talk over the suitability and rightness of machine learning and deep learning centered explanations in numerous practical fields.
To arrange for a broad opinion on machine learning algorithms, which can be put to build-up the abilities of a data-driven approach.
To highlight and summarize the possible study for smart systems including cloud technology.
To bring out the virtual awareness/importance of IoT including laboratory sessions.
The rest of the book chapter is organized as follows. Section 2 presents the past related existing works on VPL and NodeRED. In Section 3, the step-by-step methodology of the work is described in detail, which includes getting started, configuration, utilization, and machine learning (ML) package for Node-RED. The experimentational examples are provided along with the corresponding results in Section 4. Section 5 discusses about the results. Lastly, the conclusion is drawn in Section 6.
2. Related work
IoT has been playing an important role throughout the globe. It is a combination of both software and hardware tools. With the support of VPL, IoT can be applied into our day-to-day essential things. According to the survey by Ray in 2017, there were 13 VPLs for the usage and for the upgradation of IoT [1]. The VPL is classified into two types, namely open-source and proprietary-source with the four following main features, i.e., programming atmosphere, license, project source, and platform support [1]. Students can use open-source VPL such as Node-RED to learn and implement an IoT system or device with basic engineering skills and low-code programming. It helped many programmers to develop new software [2, 3]. Node-RED was initiated and was developed by IBM for connecting hardware devices with web-based editors [2, 3]. It was then applied further in the field of IoT. Rajalakshmi and Shahnasser came with a problem while making a cloud-system for IoT devices [4]. This is difficult to update the firmware by reinstalling the devices. To solve this issue, Node-RED was used without reinstalling the device and changing the programming code with quick setup [4]. In 2018, a model based on LoPy, which is a MicroPython triple-network development platform doubling up as a long-range (LoRa) Nano gateway, was connected to a system that uses Node-RED for interfacing with a local actuator and the external data with protocols [5, 6]. Their IoT development could be done in fog/clouds. The introduction of the IoT in education allows Internet-based communications to occur between things, sensors, and actuators. This has improved educational institutions [7, 8, 9, 10, 11, 12, 13, 14]. In 2015, Giang et al. implemented a distributed Node-RED (D-NR) framework for building various types of IoT applications, which can work efficiently with simple designing process and less time [15]. Abdel-Basset et al. could make an efficient framework by new ideas with lower cost and greater security [16]. The advantages in maximizing IoT became more for the institutions, i.e., bringing out an affluent knowledge, better quality of working efficiency, and gaining real-time experiences [17]. In addition, they could keep track record of all the university resources with secured data accessibility. Using an open-source platform such as Node-RED, it helps for the creation of new ideas, i.e., linking up with many other specialized courses, for instance, information and communication technologies (ICTs), embedded system design, humanities, agricultural, just to name a few, yielding a good IoT program of study for students to learn and make research [16]. Another model was proposed by Marquez et al. as an IoT educational platform for virtual academic communities [17]. In 2020, Torres et al. used VPL with Node-RED to improve their IoT system by reducing the time taken for the development, i.e., reducing the number of failed attempts while deploying an IoT system [18]. Home automation consisting of water heater, cooling systems, electrical outlets was raised all over the IoT using cloud-platform where Node-RED is used to make fast setup for remote monitoring and control of data with a mutual communication [19]. In 2021, David et al. worked on indoor crop agriculture by monitoring parameters such as humidity, temperature, and light intensity with the help of IBM-Bluemix, which is the IBM open cloud platform providing mobile and web developers access to IBM software for integration security transaction and other key functions [20]. The data was transformed into Node-RED platform through a mobile application for tracking purpose of the farmers. Thuluva et al. made a solution for interoperability problem for IoT semantic web technologies in industrial field using semantic Node-RED models with feasibility and scalability approach [21]. A rapid and low-cost IoT prototype was developed by Ferencz and Domokos within less time on Node-RED using the combination of various cycle power plant dataset [22]. A low-power wide area network (LPWAN) technology called LoRa with its medium access control (MAC) layer protocol called LoRaWAN was deployed by Fox et al. in 2019, and these end devices interact with a gateway using Node-RED connection to an IBM-IoT platform [23]. Node-RED was applied and analyzed by Olsson and Eric in terms of modeling and security with misuse of API and providing security guarantees [24]. Clerissi et al. made a model for testing and developing IoT platforms using Node-RED with the functional behavior and the static view of the system. The class diagrams were used by testing, defining, and generating in java script using a Mocha test framework [25]. Proper regulations and rules for all the Node-RED developers were given by Clerissi et al. [26]. They are about the comprehensibility issues, which can be used to increase efficiency by reducing errors and time to complete tasks.
VPL is any programming language that lets users create program by manipulating program elements graphically rather than by specifying them textually [27, 28]. A VPL allows programming with visual expressions, spatial arrangements of text and graphic symbols used either as elements of syntax or secondary notation. For instance, many VPLs known as dataflow or diagrammatic programming are based on the idea of “boxes and arrows,” where boxes or other screen objects are treated as entities, connected by arrows, lines, or arcs representing relations [29, 30, 31]. There are several different VPLs, which can be divided into different fields of applications, such as education (23 languages), multimedia (26 languages), video games (18 languages), systems (34 languages), automation (4 languages), data warehousing (8 languages), legacy (5 languages), and miscellaneous (10 languages) [29, 30, 31]. The difference between a regular programming and visual programming, in terms of type, nature, flexibility, speed, efficiency, interface, learning complication, space usage, and example, is shown in Table 1.
No
Context
Regular programming
Visual programming
1
Type
Use only text
Use only graphics
2
Nature
Not user-friendly
User-friendly
3
Customizable
Very high
Moderate
4
Flexibility
High
Low
5
Speed
Very high
Low
6
Efficiency
Very high
Moderate
7
Interface
Not good
Great
8
Learning Complication
Take time
Easy to learn
9
Space Usage
Less
High
10
Example
Python, Java, etc.
Drakon, Helix, etc.
Table 1.
Difference between regular programming and visual programming.
Node-RED is preinstalled in many devices, such as Raspberry Pi, Intel, Fujitsu, just to name a few. Moreover, there are several different cloud services including Cisco, Nokia, IBM, Hitachi, etc., using Node-RED as a VPL and a dataflow programming.
3. Methodology
This section describes the complete scenario of methodology process. As can be seen in Figure 1, the first stage is installation, which involves the three following types, i.e., local machine, Raspberry Pi, and cloud services. After the correct installation, the Node-RED is configured involving all the basic nodes required to make a workflow with deployment activities. Next, the utilization is the main stage in which the development of the flows, handling errors of all the core nodes are explained. Accordingly, when the utilization process is completed, any type of experiments can be implemented with the expected and accurate outcomes.
Figure 1.
Block diagram representing methodology workflow.
3.1 Getting started
Getting started by installing Node-RED can be done on various criteria, which are explained as follows.
Running on a local machine for Windows 7 and above
Windows 7 and above versions have the capability of installing Node.js, Node-RED, and node package manager (npm) using command prompt (cmd) or Powershell. The procedure is described as follows.
Node.js (version 14.x long term support (LTS) or above) is installed.
All the local administrators are given their rights.
Cmd or Powershell is opened and the following code is run to check if it is installed correctly or not installed.
“node –version; npm –version” (Powershell),
“node –version && npm –version” (cmd)
Node-RED is installed by the command “npm install -g –unsafe-perm node-red.”
As soon as installed, the cmd and type “node-red” is opened. As an output, a terminal log of Node-RED is displayed on the screen.
Running on a Raspberry Pi or any other IoT module
Buster is the supported version for Node-RED at present for all the Raspberry Pi Operating Systems (OS).
The subsequent command is used to install node.js, npm and node-red on any Raspberry Pi.
(This command is used on any Red-Hat packet management (RPM) based OS like Red Hat, Fedora, CentOS, Oracle Linux.)
Running on different cloud services (IBM cloud, Amazon web services, Microsoft Azure)
Node-RED is used on different cloud-based services with many features depending on the user or the client requirements.
For International Business Marketing (IBM) cloud: It is highly virtualized with high power, storage, networking, security, data management, analytics, developer tools, IoT, and integration and migration of virtual servers.
For Microsoft Azure: It is also having the same abilities like IBM cloud with some extra added features such as replacing as an supplement for many other on-premise virtual servers with best recovery support.
For Amazon web services (AWS): It has various new features when compared with many other cloud-services for developing new innovative smart devices, where it includes all the required API with less cost for the third-party usage.
3.2 Configuration
This section describes about various types of configurations that are needed for the deployment of each different application with default parameters in the default file directory.
Normal application: Entire structure is loaded by default settings file, which is a built-in source.
Embedded application: It is passed into property called RED.init(), where embedded.
Run-time configuration: It defines the time-value of each node during the deployment.
Logging configuration: Only console logging is supported in Node-RED.
Node configuration: It is in the hierarchical format used for the application deployment.
External module configuration: It defines about the run-time handling external npm modules and decides whether the editor allows new node modules to be installed such as the function node to have their own dynamic configured dependencies.
Editor configuration: It is a set of files with different coding styles with various text editor plugins.
adminAuth: It permits security for user in the editor and admin API.
paletteCategories: It describes the sequence of types in the palette. By default, the pattern is subflow, common, function, network, sequence, parser, storage.
The run-time configuration is explained as follows.
Flow file: It is used to store the flows.
Userdir: It is used to store user data, credentials, and library data.
Nodesdir: It is used to search additional installed nodes.
Uihost: It is an interface to listen all connection on IPv4.
Uiport: It is a port to serve ui editor.
Httpadminroot: It is the root url, which contains both API and editor UI.
httpAdminAuth: It allows HTTP validation on the editor UI.
httpAdminMiddleware: It is an array of all functions, which is added to all admin routes.
httpNodeRoot: It is the node root for all the urls that run HTTP at all endpoints.
httpNodeAuth: It enables HTTP Basic Authentication
httpRoot: It enables the root url to run on both admin and node endpoints through overriding httpAdminRoot and httpNodeRoot value.
https: It permits https.
httpStaticAuth: It supports basic confirmation and validation of HTTP with the static content.
httpNodeCors: It is source distribution for the nodes, which are responsible for HTTP endpoints with cross-origin authentication.
httpNodeMiddleware: It permits custom processing. For example, validation is required for the node.
Various stages of logging configuration used in Node-RED are described as follows.
Fatal: Errors that make application unworkable are tracked.
Error: Tracked errors for requests and fatal errors.
Warn: Record of the problems about non-fatal and fatal errors.
Info: Tracked information of application, warnings, error, and fatal errors.
Debugging (Debug): Tracked information, which is more verbose than information, warnings, error, and fatal errors.
Tracing (Trace): Tracks about all complete logging, debugging, info, warnings, error, and fatal errors.
The node configuration is explained as follows.
Function Node: It is for gathering bits and pieces to attach into universal functions.
functionExternalModules: It allows adding additional modules that are available to the function.
Debug Node: Any message directed to the debug sidebar tab with maximum size and characters.
MQTT Nodes: If the link is misplaced, how much time to pause in milliseconds before trying to connect.
Serial Nodes: How much time to pause in milliseconds before making an effort to revive into serial port
socketReconnectTime: How much time to pause in milliseconds before trying to connect again.
socketTimeout: How much time to pause in milliseconds before scheduling out any port.
3.3 Utilization
3.3.1 Core nodes
The important nodes used for the basic functioning of the Node-RED are called “core nodes.” The main six types of core nodes are explained as follows.
Inject: It starts any flow manually by clicking the inject button. It can be at required intervals or any time within the editor.
Debug: It is used to show messages in the Debug sidebar in the editor, and the control on the node can be used to permit or restrict its outcome.
Function: It permits JavaScript code to run beside the messages delivered from it.
Change: It is used to transform message properties and fix context properties without changing a Function node with multiple operations such as set, change, move, delete.
Switch: It permits messages to be routed into different divisions of a flow by means of calculating set of instructions compared with each message with rules values, sequence, expression, otherwise property.
Template: It is used to produce text by message properties to fill the template.
3.3.2 Developing flows
It is the section in which all the necessary placement and arrangement of the nodes are done for the successful execution on the Node-RED.
Flow structure: It helps us organize flows, approaches for splitting into smaller, reusable components, and how to modify them to make use in different platforms.
Message design: It helps how to design messages to create nodes and flows, which can work together with any number of nodes and are easier to maintain.
Documenting flows: It helps about making or providing documentation on what tools and techniques Node-RED provides.
3.3.3 Handling errors
It is described as tracing out the bug or an error, which helps to reduce the developing time and correct the errors easily. The different types of errors on Node-RED are explained as follows.
Logging error: It displays the error with the date and time of the error and the node, which is noted as an error.
Catchable error: It will not be logged, but it informs about run-time error. Then, the Catch node will be used to produce a flow which can handle it.
Sub-flow error: It will not be logged but it informs about run-time error. Then, the Catch node will be used to produce a flow, which can handle it.
Uncatchable errors: If the error is written in the log, then a message is seen in the Debug sidebar and log outcome. But creation of a flow is not possible to handle it.
Uncaught errors: It causes the Node-RED run-time to shut down and cannot be controlled in the flow as they are produced by bugs in nodes.
Status changing errors: It is used to control modifications in node position by including the position property which provides the data about the position with the node that caused the incident.
3.4 Machine learning (ML) package for node-RED
This node-red-contrib-machine learning module for Node-RED contains a set of nodes offering machine learning functionalities. Such nodes have a python core that takes advantage of common ML libraries such as SciKit-Learn and Tenserflow. Classification and outlier detection can be performed using this package.
3.4.1 Usage
These flows create a dataset, train a model, and then evaluate it. Models, after training, can be used in real scenarios to make predictions.
Flows and test datasets are available in the “test” folder. We need to make sure that the paths specified inside nodes’ configurations are correct before trying to execute the program. “node-red” can be run from the folder “.node-red/node-modules/node-red-contrib-machine-learning” and the paths will be automatically correct. The flow shown in Figure 2 loads a csv file, shuffles it, and creates a training and a test partition.
Figure 2.
Flow for loading a csv file, shuffling it, and creating a training and a test partition.
The flow shown in Figure 3 loads a training partition and trains a “decision tree classifier” and then saves the model locally.
Figure 3.
Flow for loading a training partition and training a “decision tree classifier”.
The flow shown in Figure 4 loads a test partition and evaluates a previously trained model.
Figure 4.
Flow for loading a test partition and evaluating a trained model.
Figure 5 shows the flow of how to use a trained model during deployment. Data is received via mqtt, predictions are made and then sent back.
Figure 5.
Flow for showing how to use a trained model during deployment.
4. Experimentation
In this section, various types of experiments related to IoT are provided. ESP32, which is a microcontroller chip manufactured by Espressif Systems, is applied. It consists of a low-cost and a low-power chip with features such as Wi-Fi (IEEE802. 11 b/g/n), Bluetooth, and built-in antenna. The distributed hash table (DHT11) is a basic, ultralow-cost digital temperature and humidity sensor. It uses a capacitive humidity sensor and a thermistor to measure the surrounding air and spits out a digital signal (using an 8-bit microcontroller unit (MCU)) on the data pin. An Arduino integrated development environment (IDE) is used to write and upload programs to the Arduino compatible boards supporting the languages C and C++. Message queuing telemetry transport (MQTT), which is a lightweight messaging protocol, is used on such a small microcontroller that allows messaging between device to cloud and cloud to device with supporting several IoT devices. By using MQTT commands can be sent to control outputs, data can be read and published from sensors. Therefore, communications between multiple devices can be established. We can send a command with a client to control outputs, or we can read data from a sensor and publish it to a client.
4.1 Experimental examples
Example 4.1.1 DHT11 Sensor Data on Node-RED using a Serial Port (ESP32).
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 6 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor is turned on.
The DHT11 sensor is programmed into the Arduino integrated development environment (IDE) and uploaded into the ESP32 to show the corresponding temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, we can click on the serial monitor to see the corresponding temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard and serial port, are installed.
A serial is inserted into the port node, one function node for temperature and another function node for humidity, which display the DHT11 sensor data.
To get the representation, two-gauge nodes for temperature and humidity are inserted and connected to their respective function nodes.
Finally, the overall workflow is deployed.
Figure 6.
ESP32 connection with DHT11 temperature sensor (serial port).
Figure 7.
Node-RED serial port connection for temperature and humidity.
Example 4.1.2 MQTT Broker Service (ESP32) on Node-RED with DHT11 Sensor Data.
The procedure of this task is described as follows.
The ESP32 is connected to the DHT11 sensor as shown in Figure 8 (GND TO GND, 3.3 V TO VCC, GPIO4 TO DATA PIN).
If the connections are correct, then a light present on the DHT11 sensor will be turned on.
The DHT11 sensor is programmed into the Arduino (IDE) and uploaded into the ESP32 to show the temperature and humidity.
If the required packages are absent, then the Arduino IDE shows an error while uploading the code.
After the code is successfully uploaded, click on the serial monitor to see the temperature and humidity readings of the DHT11 sensor.
The Node-RED software is installed into the system. Then, the local hosting address of the Node-RED is open. This can be run on any browsers.
The required nodes, i.e., dashboard, MQTT-in, and MQTT-out nodes, are installed.
Two MQTT-in nodes, one for temperature and another for humidity, are inserted.
This extracts the sensor data from the ESP32 through the MQTT broker service and gets into the Node-RED platform with the help of the local-host ip address.
To display this data, the one gauge is connected to the temperature node and the other is connected to the humidity node.
Finally, the overall workflow is deployed.
Figure 8.
ESP32 and DHT11 temperature sensor (MQTT broker service).
Figure 9.
MQTT broker service for temperature and humidity with node-RED.
Example 4.1.3 Live Camera Capture and Object Detection using Machine Learning (ML).
In this example, a user interface (UI) has a major role for making an interface between the user and the Node-RED dashboard by the means of a system camera and a UI-table. Figure 10 shows the procedure of this task, which is described as follows.
The Node-RED software is installed into the system. The local hosting address of the Node-RED is opened. This can be run on any browser.
The required nodes, i.e., random, dashboard, UI-table, UI-webcam, tfjs-coco-ssd, tf-model, tfjs-node (tf = tensorflow), are installed.
We connect the nodes by inserting button node = 2 (capture and clear), change node = 4 (set msg.capture, set msg.filename, set msg.image, set msg.payload), UI-webcam node = 1, UI-table node = 1, tfjs-coco-ssd = 1, debug node = 2 (msg.payload), file node = 1.
We check whether the camera of the PC/laptop is working or not.
After deploying successfully, we go to the hosting ip address followed by /ui, which opens another new webpage showing the output of the live web camera.
Figure 10.
Node-RED flow for live camera capture and object detection using machine learning (ML).
The object detection flow recognizes objects in an image and annotates objects with bounding boxes. An image can be loaded from a built-in camera, the file system, or by injecting the default image. We need to make sure that we have the node-red-contrib-browser-utils package installed for all these input nodes to work. This flow uses three of the custom nodes mentioned above (tf-function, tf-model, and post-object-detection). The loaded image is passed into the preprocessing node as msg.payload. The msg object is a JavaScript object that is used to carry messages between nodes. By convention, it has a payload property containing the output of the previous node. The preprocessing function node is an example of tf-function that directly calls the tf.node.decodeImage method with the predefined tf variable. The node produces a Tensor4D image representation as the payload and then passes it to the COCO SSD lite node, which is an instance of the tf-model custom node. This loads the COCO-SSD lite model.json from an external URL and runs inference on the model.
The result of the model goes through the postprocess node that returns an object array containing bbox, className, and score properties. The objects node combines an additional property, complete that is set to true, to the msg with the image object. Then, the bounding-box node draws bounding boxes on the input image and displays it in the browser.
4.2 Experimental results
Example 4.2.1 DHT11 Sensor Data on Node-RED using a Serial Port (ESP32).
Once after the successful deployment, we go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. The temperature and humidity readings are directly sent from the serial port to the Node-RED directly. Figure 11 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (serial port).
Figure 11.
Dashboard of temperature and humidity from DHT11 sensor (serial port).
Example 4.2.2 MQTT Broker Service (ESP32) on Node-RED with DHT11 Sensor Data.
Once after the successful deployment, go to the hosting ip address followed by:1880/ui, which opens another new webpage representing the gauge readings of the temperature and the humidity of the DHT11 sensor. Temperature and humidity readings are sent from the sensor to MQTT broker (as a cloud), then finally sent to the Node-RED as a third-party service. Figure 12 shows an example of the dashboard showing temperature and humidity from the DHT11 sensor (MQTT broker service).
Figure 12.
Dashboard of temperature and humidity from DHT11 sensor (MQTT broker service).
Example 4.2.3 Live Camera Capture and Object Detection.
When the deployment is successful, go to the hosting ip address followed by:1880/ui, which opens another new webpage asking for the permission to allow camera (Press allow). Then, we can see the live camera working and press the capture button to capture. It detects the object with the help of TensorFlow analysis node as earlier describe and the output can be seen on the debug panel with the accuracy score of its correctness. The examples of the single object and the dual object detection are shown in Figures 13 and 14, respectively.
Figure 13.
Debug message showing single object class and accuracy score.
Figure 14.
Debug message showing dual object class and accuracy score.
4.3 Implementing as a mobile application
From the given examples, they can also be made visible on a mobile phone for both iOS and Android. It is done through remote access just by installing the application named “RemoteRED” from the mobile app store. The steps are explained as follows.
Install the Remote-RED node in the Node-Red.
Open the Remote-RED settings and configure it to get the QR code.
Open the Remote-RED application from the mobile phone and scan the QR code.
Wait for a minute as it is asynchronous to get updated on the mobile phone.
Now we can see the same output of the Node-RED on the mobile phone Remote-RED application as shown in Figure 15.
This process is the same and it works for any Node-RED workflow.
Figure 15.
Connecting to remote-RED application (iOS/android).
5. Discussion
The results show that simulated systems developed using the Node-RED can be deployed in the real world without changing the system parameters. Moreover, the simulated system can be connected with hardware platforms easily. VPL-based hands-on tools are effectively smoothing the beginner’s learning curve on IoT. The stranded network protocols and IoT protocols are easy to understand using hands-on experience and deploy in the Node-RED environment. Node-RED system models are easy to debug compared with the traditional programming debug methods. The platform works well in both Internet and Intranet mode as all the components are virtually presented in Node-RED. After all, the laboratory instructors can effectively convey the basics of network and IoT concepts to the students through a VPL language including the Node-RED.
6. Conclusion
The book chapter described a complete outlook of Node-RED that can be applied for an IoT based virtual laboratory. The configuration, the flow development, the requirements, and the usage of the Node-RED were explained with respect to handling all the various types of errors. We modeled, implemented, and tested the IoT virtual laboratory using Node-RED. The implemented virtual laboratory system is currently serving for flexible postgraduate programs and broadcasting completely online. Students obtain great encouragement and motivation toward virtual IoT hands-on practices as they can manage their own and convenient time and a location. The virtual laboratory concept utilizes the available hardware (Wi-Fi IEEE802. 11 b/g/n routers, classical Bluetooth hardware, and system-on-chip (SoC) like MCU) at the student location. The virtual IoT laboratory concept was proved to help students to learn faster than a classical theory class or a video-recorded lesson. With the simplicity of Node-RED and its built-in entities a few examples, which can be used for an IoT-based virtual laboratory, were done to evolve innovative platforms with less coding complexity. It provides flexibility such that the remote laboratory can run on several operating systems or on a mobile application. The proposed solution is platform-independent, and therefore, it can be implemented on low-cost hardware for smaller systems, and the clients can run on mobile devices. The first and second examples are typical scenarios in IoT on Node-RED platform including Arduino and third-party cloud service, whereas the third example is live camera capture with object detection capability. Finally, it was also shown about how Node-RED can be used as a mobile application remotely. After the completion of the book chapter, the readers are supposed to be able to develop IoT systems using VPL-Node-RED, integrate IoT with Node-RED, to design various workflows for IoT on Node-RED, develop real-time IoT applications, and apply security features for IoT and Node-RED while using cloud-based services. Hence, IoT with Node-RED has the capability to change the entire education system, which makes better learning with good interaction and flexibility.
The variety of IoT devices in the future is expected to dramatically grow. Our Node-RED IoT platform was designed to be extendible. To model these future scenarios, we expect to support new IoT device types by using the generic framework for creating new devices. An example of such devices could be wearables that generate movement data. This type of data can be emitted at high volumes but small size. Another device type, which is expected, is an IoT device that is configurable at runtime. This would give us the ability to change the behavior of the device according to an operation plan. An example of this type of device would be a smart home thermostat. Furthermore, we also want to improve the intelligence of our smart testing framework providing machine learning libraries that can continuously learn from past data to improve prediction accuracy. Finally, empirical research can be done collecting a large amount of data in order to know the impact of virtual IoT laboratories on the students with their involvement as it helps the instructors to improve their laboratory sessions further.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"node-RED, visual programming language, open-source, Arduino MQTT, amazon cloud",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81242.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81242.xml",downloadPdfUrl:"/chapter/pdf-download/81242",previewPdfUrl:"/chapter/pdf-preview/81242",totalDownloads:29,totalViews:0,totalCrossrefCites:0,dateSubmitted:"February 22nd 2022",dateReviewed:"March 2nd 2022",datePrePublished:"April 13th 2022",datePublished:null,dateFinished:"April 13th 2022",readingETA:"0",abstract:"Due to the pandemic or any other tough situations, attending the student laboratory session physically is difficult or even impossible. To overcome this problem, a virtual laboratory can be introduced. In many universities, virtual teaching methodology is not implemented widely. While there are several existing visual programming languages developed for various applications, an open-source visual programming language named Node-RED, which is particularly used for Internet of Things (IoT)-based applications, is taken into consideration in the book chapter. A complete outlook of Node-RED, which can be applied for an IoT-based virtual laboratory, is described. Its simplicity providing many built-in entities makes it possible to evolve innovative platforms with less coding complexity. The configuration, the flow development, the requirements, and the usage of the Node-RED are explained with respect to handling all the various types of errors. A few experimental cloud-based services are implemented on an IoT platform using serial port and message queuing telemetry transport (MQTT) broker service as well as providing live camera capturing feature with artificial-intelligence-based object detection. The results show that real-time IoT sensor data can be efficiently measured and visualized in dashboard and live object detection can be done with good accuracy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81242",risUrl:"/chapter/ris/81242",signatures:"Kalapatapu Venkata Sai Krishna Raja Shiva Kumar, Attaphongse Taparugssanagorn and Arunya Prasantha Senadeera Senadiri Dumunnage",book:{id:"11198",type:"book",title:"Digital Transformation",subtitle:null,fullTitle:"Digital Transformation",slug:null,publishedDate:null,bookSignature:"Dr. Antonella Petrillo, Prof. Fabio De Felice, Prof. Monica Violeta Achim and Dr. Nawazish Mirza",coverURL:"https://cdn.intechopen.com/books/images_new/11198.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-763-2",printIsbn:"978-1-80355-762-5",pdfIsbn:"978-1-80355-764-9",isAvailableForWebshopOrdering:!0,editors:[{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work",level:"1"},{id:"sec_3",title:"3. Methodology",level:"1"},{id:"sec_3_2",title:"3.1 Getting started",level:"2"},{id:"sec_4_2",title:"3.2 Configuration",level:"2"},{id:"sec_5_2",title:"3.3 Utilization",level:"2"},{id:"sec_5_3",title:"3.3.1 Core nodes",level:"3"},{id:"sec_6_3",title:"3.3.2 Developing flows",level:"3"},{id:"sec_7_3",title:"3.3.3 Handling errors",level:"3"},{id:"sec_9_2",title:"3.4 Machine learning (ML) package for node-RED",level:"2"},{id:"sec_9_3",title:"3.4.1 Usage",level:"3"},{id:"sec_12",title:"4. Experimentation",level:"1"},{id:"sec_12_2",title:"4.1 Experimental examples",level:"2"},{id:"sec_13_2",title:"4.2 Experimental results",level:"2"},{id:"sec_14_2",title:"4.3 Implementing as a mobile application",level:"2"},{id:"sec_16",title:"5. Discussion",level:"1"},{id:"sec_17",title:"6. Conclusion",level:"1"},{id:"sec_21",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Ray PP. 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In: Protocols, Strands, and Logic. Sequence Number 1: Springer International Publishing; 2021. DOI: 10.1007/978-3-030-91631-2_1'},{id:"B25",body:'Clerissi D, Leotta M, Reggio G, Ricca F. Towards an approach for developing and testing node-red iot systems. In: Proceedings of the 1st ACM Sigsoft International Workshop on Ensemble-Based Software Engineering; November 2018. New York, NY, USA: ACM; 2018. pp. 1-8'},{id:"B26",body:'Clerissi D, Leotta M, Ricca F. A set of empirically validated development guidelines for improving node-red flows comprehension. In: Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE’20). SciTePress; 25-26 April 2022. Online Streaming Conference: 2020. pp. 108-119'},{id:"B27",body:'Jost B, Ketterl M, Budde R, Leimbach T. Graphical programming environments for educational robots: Open roberta - yet another one? In: Proceedings of the IEEE International Symposium on Multimedia; 10–12 Dec. 2014. Taichung, Taiwan: IEEE; 2014. pp. 381-386'},{id:"B28",body:'Kuhail MA, Farooq S, Hammad R, Bahja M. Characterizing visual programming approaches for end-user developers: A systematic review. IEEE Access. 2021;9:14181-14202. DOI: 10.1109/ACCESS.2021.3051043'},{id:"B29",body:'Repenning A. Moving beyond syntax: Lessons from 20 years of blocks programing in agentsheets. Journal of Visual Languages and Sentient Systems. 2017;3:68-91. DOI: 10.18293/vlss2017-010'},{id:"B30",body:'Johnston WM, Hanna JRP, Millar RJ. Advances in dataflow programming languages. ACM Computing Surveys. 2004;36(1):1-34. DOI: 10.1145/1013208.1013209'},{id:"B31",body:'Rekers J, Schürr A. Defining and parsing visual languages with layered graph grammars. Journal of Visual Languages and Computing. 1997;8(1):27-55. DOI: 10.1006/jvlc.1996.0027'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Kalapatapu Venkata Sai Krishna Raja Shiva Kumar",address:null,affiliation:'
School of Engineering and Technology, Asian Institute of Technology, Phatum Thani, Thailand
School of Engineering and Technology, Asian Institute of Technology, Phatum Thani, Thailand
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Bogolubov (Jr.) \n\nI would like to begin my reminiscences with a quite unique episode in my student life which many years later had a great impact on my future scientific and everyday life. As far back as early seventieth of the past century, being a student at the Physics Department of Lviv State University, walking along its corridors, I occasionally entered the Big Physical Hall amphitheater and found there a lot of young people sitting and listening attentively to a youthful speaker standing at the rostrum of the Hall and energetically explaining some overhanding visual aids filled\nwith mathematical formulae. I also took a vacant seat and tried to catch what he was speaking about. . .On the front wall of the Hall there hung a long advertisement in big bold letters: The All-Union Statistical Physics Conference Workshop - 1973 under the auspices of academician Nikolai N. Bogolubov. I understood that it could be interesting for me too, as I had then already taken lecture courses on quantum mechanics and statistical physics. I listened to the speaker until he finished and became a witness of a very hot and emotional discussion following the report. As during this discussion some people referred to the speaker - Professor Nikolai Bogolubov, I right away understood that the speaker, being then young enough, was in reality Professor Nikolai N. Bogolubov (Junior) contrary to the Workshop chair academician Nikolai N. Bogolubov\n(Senior)! It is appropriate to say here that I had been already familiar with the name Nikolai N. Bogolubov (Sr.) since my university mate Dmytro Petryna had already told me a few things of him with great enthusiasm and had shown me his three-volume "Collected oeuvres" recently published in Kyiv. Moreover, his uncle Professor Dmytro Ya. Petryna, living then in Kyiv, was a disciple of academician Ostap S. Parasyuk, who was actively working together with academician Nikolai N. Bogolubov (Sr.), and had tried to urge his nephew Dmytro Petryna to study some fundamental N.N. Bogolubov's (Sr.) works on statistical physics and quantum field theory. Having been intrigued by my friend's scientific passion and persistence I also started looking through\nBogolubov's monographs stored in the libraries of Lviv University and Academy of Sciences. This way I soon became aware of such N. Bogolubov's books as "Introduction to the theory of quantized fields", "Lectures on statistical mechanics" and "Axiomatic approach backgrounds to the quantum field theory" etc. and tried to understand something therein. The first lesson I was taught while reading these and other related books, consisted in clear understanding that my mathematical\neducation was quite insufficient for these books to be read with profit. Keeping deeply in mind this feeling, I started my "advanced" self-education by studying functional analysis and operator theory, differential geometry and topology, complex analysis and algebraic geometry, advanced algebra, differential equations and variational analysis etc. Being fascinated with very interesting problems in the quantum field theory and electrodynamics I secretly decided to try to start my graduate Ph.D. studies in the near future at the Institute for Theoretical Physics of NAS1 in Kyiv and continue my already imagined theoretical physics investigations of such puzzling physical phenomena as electron-positron annihilation, physical vacuum polarization and so on. Now I would\nlike to turn back to the episode of my occasional "participation" in the Statistical PhysicsWorkshop held in the Lviv State University: when the discussion, following the Nikolai N. Bogolubov's (Jr.) report, finished I was eager to approach him and ask whether he was also interested in quantum electrodynamics and what he thought of the electron-positron annihilation phenomenon that had bothered me for the last two years. To my regret, I failed to realize this intention because of people crowding the very narrow passages, when I descended along the bench rows to the rostrum, Prof. Nikolai N. Bogolubov (Jr.) had already disappeared in the neighboring rooms.\nNonetheless, I promised myself that I would do my best to meet Prof. Nikolai N. Bogolubov (Jr.) and in the future, upon the University graduation, start my postgraduate physics studies under his supervision. Having graduated from the Lviv State University and trying to make my beloved electrodynamics and quantum fields theory research, I soon became a non-formal disciple of academician Ostap S. Parasyuk, who was the head of Mathematical Department at the Institute for Theoretical Physics of NAS in Kyiv. To a great extent this became possible owing to the help of my friends Petro I. Holod (a former Ph.D. student of academician Ostap S. Parasyuk) and Ivan M. Kopych (a former Ph.D. student of Prof. Volodymyr P. Hachok from the Institute for Theoretical Physics of\nNAS in Kyiv and my Physics Department student-mate from Lviv). The scientific work under Prof. Ostap S. Parasyuk's supervision and friendly cooperation with Petro I. Holod had run successfully enough and I decided to enter the Institute for Theoretical Physics of NAS in Kyiv having applied for Ph.D. studies under Prof. Ostap S. Parasyuk's supervision. But very soon I was asked by Prof. Ostap S. Parasyuk to withdraw my Ph.D. application to him and instead apply for Ph.D. studies at the Institute of Mathematics of NAS in Kyiv under Nikolai N. Bogolubov's (Jr.) supervision, who worked in Moscow for the V. A. Steklov Mathematical Institute of RAS and had already agreed to take the duty of my supervisor. Thus, having soon passed my entrance exams with merit, I became\na Ph.D. student of Prof. N.N. Bogolubov (Jr.) just as I had dreamed some years before being a Physics Department student at Lviv State University. Soon enough, in five months, I was directed by the Institute of Mathematics authority to continue my studies in Moscow at the V.A. Steklov Mathematical Institute of RAS, where I carried out some interesting research devoted both to the study of exact solutions to classical two-dimensional Thirring type two-dimensional field theory models and to the old mathematical integrability by quadratures problem of ordinary differential Riccati equations, making use essentially of the algebraic-geometrical tools, devised recently\nby such mathematicians as S.P. Novikov, B.A. Dubrovin, V.B. Matveev, L.D. Faddeev, P. Lax, J. Moser, M. Adler and some others. It should be mentioned here that most of the problems I was then working at had been posed by Prof. Ostap S. Parasyuk and which appeared to be also of great interest to Prof. Nikolai N. Bogolubov (Jr.). Having soon defended these results as a Ph.D. thesis (in 1980), I began jointly with Prof. Nikolai N. Bogolubov (Jr.) to investigate the problems posed by academician N.N. Bogolubov (Sr.) in early fortieth of the past century that consisted in the description of algebraic-analytical properties of physical solutions to dynamical systems of quantum\nstatistical and quantum field theory physics making use of functional operator, Lie-algebraic and C*-algebra representation methods and techniques. Amongst the problems studied jointly with Prof. Nikolai N. Bogolubov (Jr.) it is necessary to mention the following: i) construction of a quantum analogue of the Bogolubov functional equations for many-particle distribution functions by means of the functional-operator methods and representation theory of the current algebras [1, 2]; ii) the Lie-algebraic proof of the Hamiltonian structure for the classical Bogolubov functional equations [2]; iii) the complete quantum integrability of new Schrodinger type nonlinear quantum dynamical systems with delta- and delta-prime many-particle interaction [1]; iv) devising an effective, so-called direct gradient-holonomic algorithm for studying the Lax type\niso-spectral and parametric integrability of a wide class of nonlinear dynamical systems on functional (two-dimensional) topological jet-manifolds [1, 3-6]; v) construction of the Delsart-Lions generalized transmutation operators by means of the de Rham-Hodge type theory to solve the multi-dimensional quantum inverse spectral transform\nproblem [4, 7, 8]; vi) application of Lie-algebraic and differential-geometric methods to construct quantum computer algorithms [9]; vii) symplectic theory analysis of helicity and vortex type invariants of classical hydrodynamical and magneto-hydrodynamical systems [10-12]. It should be mentioned here that a wide cycle of investigations was also carried out by Nikolai N. Bogolubov (Jr.) jointly with his Moscow students on the well known approximating Hamiltonian method in quantum statistical mechanics. His works in this field have brought an outstanding contribution to the development of rigorous methods of statistical mechanics. Having been originally called for solving the modelling problems connected with fermion operators, many results by Bogolubov N.N. (Jr.) have subsequently found application in a wider range of problems. The known work by Ginibre [13] in its essential part follows the Bogolubov's N.N. (Jr.) remarkable work [14]. Recently the approximating Hamiltonian method has been also applied to rigorous problems related to Bose systems [15, 16]. In 1954 Bogoluubov N.N.(Sr.) developed a new approach [17] for expressing physical observables as continual integrals. This approach was based on the representation of Green functions in terms of vacuum expectations of suitable chronological products. The averaging operation over the boson vacuum was interpreted as a functional integral. Later in 1981,\nBogolubov N.N. and Bogolubov N.N. (Jr.) [18] developed this construction within the framework of quantum statistical mechanics. The measure that arises in this approach is a Gaussian measure in an appropriate space of continuous functions. The Gibbs equilibrium averages of the chronological products of operators are expressed as functional integrals with respect to this measure. Subsequently some mathematical problems of integration with respect to the Bogolubov's measure were considered [19]. It was found that the Bogolubov-Bogolubov (Jr.) approach is highly fruitful in quantum statistical mechanics side by side with the Feynman functional integration. Unlike the\nFeynman approach, the Bogolubov-Bogolubov (Jr.) approach is based on the well defined Gaussian measure. (As is well known, the natural analogue of Wiener measure with complex variance parameter is not a countably additive complex measure.) Some of these studies were later defended as my doctor habilitation thesis in mathematics and physics sciences (in 1987) at the Laboratory of Theoretical Physics of the international Joint Institute for Nuclear Research in Dubna, Moscow region, of Russian Federation. During the next fifteen years I was much traveling abroad visiting research centers at the universities of Canada, USA and West Europe, and my contacts with Professor Nikolai N. Bogolubov (Jr.) had become more rare. The situation has abruptly changed for the past ten years owing to our joint research collaboration grants obtained from the International Center for Theoretical Physics and the SISSA-International school for Advanced Studies in Trieste, Italy. This period was marked by turning back our research efforts to studying the old and new problems of classical and quantum electrodynamics and gravitation. Jointly with Prof. Nikolai N. Bogolubov (Jr.) and some of our co-authors, we have analyzed in detail, having based on the modern symplectic theory methods, the classical Dirac-Fock-Podolsky problem [20, 21] of embedding the well known Lorentz constraint into the canonical Hamiltonian formalism, suitable for quantization of the related Maxwell equations. Concerning the space-time and physical vacuum based description of the classical Maxwell equations we have recently developed jointly with Prof. Nikolai N. Bogolubov (Jr.) a new, so-called vacuum\nfield theory approach [22-32] to the study of the Lorentz type particle charge interaction with external electromagnetic field. The obtained results have enabled us, in particular, to suggest a new physical interpretation of the inertial (dynamical) particle mass notion well fitting to be applied to the gravity theory, based on completely new no-geometry approach, and being free of well known singularity problems. Dropping the point finishing these recollections, I cordially express my sincere appreciation to my Teacher and Friend Prof. Nikolai N. Bogolubov (Jr.) for our many-year scientific relations and true friendship, owing to which I was able to do my beloved and God blessed Physics and Mathematics investigations of the beautiful Nature so freely, so happily and, I hope,\nfruitfully.\nThe author is cordially appreciated to my friend Prof. Dmitriy Sankovich for the discussion of a manuscript and help in presenting some important data and related references. \n\nReferences\n1. Bogolubov N.N. (Jr.), Mitropolski Yu.A., Prykarpatsky A.K., Samoylenko V.H., Integrable Dynamical\nSystems: Differential-Geometric and Spectral Aspects. Naukova Dumka, Kiev, 1987.\n2. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Phys. Part. Nuclei, 1986, 17, No. 4, p. 790-827.\n3. Bogolubov N.N. (Jr.), Blackmore D., Prykarpatsky A.K. On Benney type hydrodynamical systems and their Boltzmann-Vlasov equations kinetic models. Preprint of the Italy ICTP: IC/2006/006, 2006, p. 1-36 (Available at: publications.ictp.it).\n4. Bogolubov N.N. (Jr.), Prykarpatsky A.K., On generalized de Rham-Hodge complexes, the related characteristic Chern classes and some applications to integrable multi dimensional differential sytstems on Riemannian manifolds. Preprint of the ICTP: IC/2006/107, 2006, Trieste, Italy (Available at: publications.ictp.it).\n5. Bogolubov N.N. (Jr.), Blackmore D., Prykarpatsky A.K., The Lie-algebraic structures and integrability of differential and differential-difference nonlinear dynamical systems. Preprint of the ICTP: IC/2007/029, 2007, Trieste, Italy (Available at: publications.ictp.it).\n6. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., Gucwa I., Analytical properties of an Ostrovsky-Whitham type dynamical system for a relaxing medium with spatial memory and its integrabil-ity.\nPreprint of the ICTP: IC/2007/109, 2007, Trieste, Italy (Available at: publications.ictp.it).\n7. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Ukr. Math. J., 2007, 59, No. 3, 753-766.\n8. Bogolubov N.N. (Jr.), Prykarpatsky A.K., The characteristic chern-type classes and integrability of multi-dimensional differential systems on Riemannian manifolds. In: World Scientific book (http://www.worldscibooks.com/mathematics/7124.html), 2008, p. 743-759; Proc. of the ISAAC-2007 Congress: Further Progress in Analysis, 13-18 August, 2007, Ankara, Turkey.\n9. Samoilenko A.M., Prykarpatsky A.K., Prykarpatsky Ya.A., Math. Comput. Simulat, 20004, 35, No. 2, 734-753.\n10. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., A symplectic generalization of the Peradzynski helicity theorem and some applications. Preprint of the ICTP: IC/2007/118, 2007, Trieste, Italy (Available at: publications.ictp.it).\n11. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., Int. J. Theor. Phys., 2008, 47, 1919-1928.\n12. Bogolubov N.N. (Jr.), Golenia J., Pavlov M., Popowicz Z., Prykarpatsky A.K., A new Riemann type hydrodynamical hierarchy and its integrability analysis. Preprint of the ICTP: ICTP-IC/2009/095 (Available at: http://publications.ictp.it).\n13. Ginibre J., Commun. Math. Phys., 1968, 8, 26.\n14. Bogolubov N.N. (Jr.), Physica, 1966, 32, 933.\n15. Pulle J.V., Zagrebnov V.A., J. Phys. A: Math. Gen., 2004, 37, 8929.\n16. Bogolyubov N.N. (Jr.), Sankovich D.P., Ukr. J. Phys., 2010, 55, 104.\n17. Bogolubov N.N., Dokl. Akad. Nauk SSSR, 1954, 99, 225.\n18. Bogolubov N.N., Bogolubov N.N.(Jr.), Aspects of polaron theory, Report No. R1-81-65, JINR, Dubna, 1981.\n19. Sankovich D.P., Proc. Steklov Inst. of Math., 2005, 251, 1.\n20. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Taneri U., J. Phys. A: Math. Theor., 2009, 42, 165401.\n21. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Taneri U., The electromagnetic Dirac-Fock-Podolsky problem and symplectic properties of the Maxwell and Yang-Mills type dynamical systems. Preprint of the ICTP: IC/2009/005, 2009, Trieste, Italy (Available at: publications.ictp.it).\n22. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Taneri U., The vacuum structure, special relativity theory and quantum mechanics: a field theory no-geometry approach. Preprint of the ICTP: IC/2008/051, 2008, Trieste, Italy (Available at: publications.ictp.it); Preprint arXiv:0807.3691v5, 2008.\n23. Bogolubov N.N. (Jr.), Prykarpatsky A.K., The vacuum structure of vacuum, Maxwell equations and relativistic theory aspects. Preprint of the ICTP: IC/2008/091, 2008, Trieste, Italy (Available at: publications.ictp.it).\n24. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Taneri U., Theor. Math. Phys., 2009, 160(2), 1079-1095.\n25. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Ukr. J. Phys., 2009, 54, No. 8-9, 753-766.\n26. Bogolubov N.N. (Jr.), Prykarpatsky A.K., Condens. Matter Phys., 2009, 12, No. 4, 603.\n27. Bogolubov N.N. (Jr.), Prykarpatsky A.K., The relativistic electrodynamics least action principles revisited: new charged point particle and hadronic string model analysis. Part 3. Preprint of the ICTP: IC/2009/027 (Available at: http://publications.ictp.it).\n28. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., Taneri U., Introductive backgrounds of modern quantum mathematics with application to nonlinear dynamical systems. Preprint of the ICTP: IC/2007/108, 2007, Trieste, Italy (Available at: publications.ictp.it).\n29. Bogolubov N.N. (Jr.), Samoilenko A.M., Blackmore D., Prykarpatsky A.K., The differential-geometric aspects of integrable dynamical systems. Preprint of the ICTP: IC/2007/030, 2007, Trieste, Italy (Available at: publications.ictp.it).\n30. Bogolubov N.N. (Jr.), Prykarpatsky A.K., On the analytical structure of the Bogolubov generating functional method in classical statistical physics and related "collective" variable method. Preprint of the ICTP: IC/2006/106, 2006, Trieste, Italy (Available at: publications.ictp.it).\n31. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., Taneri U., Int. J. Theor. Phys., 2008, 47, 2882-2897.\n32. Bogolubov N.N. (Jr.), Golenia J., Prykarpatsky A.K., Taneri U., Introductory Background to Modern Quantum Mathematics with Application to Nonlinear Dynamical Systems. In: World Scientific book (http://www.worldscibooks.com /mathematics/7124.html), 2008, p. 760-780; Proc. of the ISAAC--2007 Congress: "Further Progress in Analysis", 13-18 August 2007, Ankara, Turkey.",institutionString:null,institution:{name:"AGH University of Science and Technology",institutionURL:null,country:{name:"Poland"}}},{id:"27072",title:"Dr.",name:"Irismar",surname:"Da Paz",slug:"irismar-da-paz",fullName:"Irismar Da Paz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/27072/images/2245_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Federal do Piauí",institutionURL:null,country:{name:"Brazil"}}},{id:"31169",title:"Ms.",name:"Olena",surname:"Shafalyuk",slug:"olena-shafalyuk",fullName:"Olena Shafalyuk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"31869",title:"Prof.",name:"Yuriy",surname:"Sirenko",slug:"yuriy-sirenko",fullName:"Yuriy Sirenko",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"31870",title:"Prof.",name:"Paul",surname:"Smith",slug:"paul-smith",fullName:"Paul Smith",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"44004",title:"Prof.",name:"Maria Carolina",surname:"Nemes",slug:"maria-carolina-nemes",fullName:"Maria Carolina Nemes",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"44005",title:"Prof.",name:"Jose Geraldo",surname:"Peixoto de Faria",slug:"jose-geraldo-peixoto-de-faria",fullName:"Jose Geraldo Peixoto de Faria",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"retraction-and-correction-policy",title:"Retraction and Correction Policy",intro:"
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
",metaTitle:"Retraction and Correction Policy",metaDescription:"Retraction and Correction Policy",metaKeywords:null,canonicalURL:"/page/retraction-and-correction-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"
IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\\n\\n
1. RETRACTIONS
\\n\\n
A Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\\n\\n
A formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\\n\\n
\\n\\t
Data fabrication
\\n\\t
Data recycling in a purportedly original research article
\\n\\t
Severe plagiarism - whether or not the plagiarism is to be deemed severe will be determined by the Academic Editor and verified by plagiarism checking software
\\n\\t
Double publication
\\n\\t
Copyright infringement - for example, if a Chapter uses copyrighted figures without permission.
\\n\\t
Unreliable findings
\\n\\t
Unethical research practices
\\n\\t
Any other practice or act considered potentially harmful to the scientific community.
\\n
\\n\\n
Publishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n\\n\\t
All relevant bibliographic information about a retracted Chapter will be given in the title.
\\n\\t
A Retraction Notice will be published as a regular book Chapter and will be given its own Chapter number.
\\n\\n\\n
\\n\\t
Authors shall be required to approve a proposed retraction of their Chapter. If Authors maintain that their Chapter should not be retracted, the Academic Editor may issue a Statement of Concern (see 2. below).
\\n
\\n\\n
1.2. REMOVALS AND CANCELLATIONS
\\n\\n
\\n\\t
Additionally, a Chapter retracted on grounds of copyright infringement (e.g. double publication) may be Removed by the publisher should the original copyright owner request such action. A Chapter retracted on grounds of its potential to harm the scientific community, for example, when a Chapter is defamatory in nature, may also be subject to removal.
\\n\\t
No formal Removal Notice will be published but a notice citing the reason for removal will be prominently displayed in place of a retracted and subsequently removed Chapter.
\\n\\t
Chapters published due to inadvertent production mistakes shall be canceled and the cancellation notice will be published.
\\n
\\n\\n
2. STATEMENTS OF CONCERN
\\n\\n
A Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
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\\n\\t
Authors refuse to approve a retraction proposed by the Academic Editor
\\n\\t
There is inconclusive evidence of scientific misconduct
\\n\\t
Authors and their respective institutions fail or refuse to provide adequate assistance in an investigation
\\n\\t
The publication of a Statement of Concern will adhere to the Retraction Notice guidelines outlined above
\\n\\t
An article PDF for which a Statement of Concern is published will remain available online without being edited or watermarked
\\n
\\n\\n
IntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n
3. CORRECTIONS
\\n\\n
A Correction will be issued by the Academic Editor when:
\\n\\n
\\n\\t
Only a small portion of a Chapter is flawed in a way that does not severely affect any findings.
\\n\\t
It is determined that the scientific community would be better served by a Correction rather than a Retraction.
\\n\\t
Corrections will be issued in one of two distinct forms -- ERRATUM or CORRIGENDUM, depending on the origin of a mistake.
\\n
\\n\\n
3.1. ERRATUM
\\n\\n
An Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\n
A published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n
3.2. CORRIGENDUM
\\n\\n
A Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n
4. FINAL REMARKS
\\n\\n
IntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\n
In the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\n
The general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n
1. RETRACTIONS
\n\n
A Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\n
A formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\n
\n\t
Data fabrication
\n\t
Data recycling in a purportedly original research article
\n\t
Severe plagiarism - whether or not the plagiarism is to be deemed severe will be determined by the Academic Editor and verified by plagiarism checking software
\n\t
Double publication
\n\t
Copyright infringement - for example, if a Chapter uses copyrighted figures without permission.
\n\t
Unreliable findings
\n\t
Unethical research practices
\n\t
Any other practice or act considered potentially harmful to the scientific community.
\n
\n\n
Publishing of a Retraction Notice will adhere to the following guidelines:
\n\n\n\t
All relevant bibliographic information about a retracted Chapter will be given in the title.
\n\t
A Retraction Notice will be published as a regular book Chapter and will be given its own Chapter number.
\n\n\n
\n\t
Authors shall be required to approve a proposed retraction of their Chapter. If Authors maintain that their Chapter should not be retracted, the Academic Editor may issue a Statement of Concern (see 2. below).
\n
\n\n
1.2. REMOVALS AND CANCELLATIONS
\n\n
\n\t
Additionally, a Chapter retracted on grounds of copyright infringement (e.g. double publication) may be Removed by the publisher should the original copyright owner request such action. A Chapter retracted on grounds of its potential to harm the scientific community, for example, when a Chapter is defamatory in nature, may also be subject to removal.
\n\t
No formal Removal Notice will be published but a notice citing the reason for removal will be prominently displayed in place of a retracted and subsequently removed Chapter.
\n\t
Chapters published due to inadvertent production mistakes shall be canceled and the cancellation notice will be published.
\n
\n\n
2. STATEMENTS OF CONCERN
\n\n
A Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\n
\n\t
Authors refuse to approve a retraction proposed by the Academic Editor
\n\t
There is inconclusive evidence of scientific misconduct
\n\t
Authors and their respective institutions fail or refuse to provide adequate assistance in an investigation
\n\t
The publication of a Statement of Concern will adhere to the Retraction Notice guidelines outlined above
\n\t
An article PDF for which a Statement of Concern is published will remain available online without being edited or watermarked
\n
\n\n
IntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n
3. CORRECTIONS
\n\n
A Correction will be issued by the Academic Editor when:
\n\n
\n\t
Only a small portion of a Chapter is flawed in a way that does not severely affect any findings.
\n\t
It is determined that the scientific community would be better served by a Correction rather than a Retraction.
\n\t
Corrections will be issued in one of two distinct forms -- ERRATUM or CORRIGENDUM, depending on the origin of a mistake.
\n
\n\n
3.1. ERRATUM
\n\n
An Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\n
A published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n
3.2. CORRIGENDUM
\n\n
A Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n
4. FINAL REMARKS
\n\n
IntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\n
In the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\n
The general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
\r\n
\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n
\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11411,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/40143",hash:"",query:{},params:{id:"40143"},fullPath:"/chapters/40143",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()