Isotherm constants for BB 9 and BY28 in single and binary systems.
\r\n\tHomeostasis is brought about by a natural resistance to change when already in the optimal conditions, and equilibrium is maintained by many regulatory mechanisms. All homeostatic control mechanisms have at least three interdependent components for the variable to be regulated: a receptor, a control center, and an effector. The receptor is the sensing component that monitors and responds to changes in the environment, either external or internal. Receptors include thermoreceptors and mechanoreceptors. Control centers include the respiratory center and the renin-angiotensin system. An effector is a target acted on to bring about the change back to the normal state. At the cellular level, receptors include nuclear receptors that bring about changes in gene expression through up-regulation or down-regulation and act in negative feedback mechanisms. An example of this is in the control of bile acids in the liver.
\r\n\tSome centers, such as the renin-angiotensin system, control more than one variable. When the receptor senses a stimulus, it reacts by sending action potentials to a control center. The control center sets the maintenance range—the acceptable upper and lower limits—for the particular variable, such as temperature. The control center responds to the signal by determining an appropriate response and sending signals to an effector, which can be one or more muscles, an organ, or a gland. When the signal is received and acted on, negative feedback is provided to the receptor that stops the need for further signaling.
\r\n\tThe cannabinoid receptor type 1 (CB1), located at the presynaptic neuron, is a receptor that can stop stressful neurotransmitter release to the postsynaptic neuron; it is activated by endocannabinoids (ECs) such as anandamide (N-arachidonoylethanolamide; AEA) and 2-arachidonoylglycerol (2-AG) via a retrograde signaling process in which these compounds are synthesized by and released from postsynaptic neurons, and travel back to the presynaptic terminal to bind to the CB1 receptor for modulation of neurotransmitter release to obtain homeostasis.
\r\n\tThe polyunsaturated fatty acids (PUFAs) are lipid derivatives of omega-3 (docosahexaenoic acid, DHA, and eicosapentaenoic acid, EPA) or of omega-6 (arachidonic acid, ARA) and are synthesized from membrane phospholipids and used as a precursor for endocannabinoids (ECs) mediate significant effects in the fine-tuning adjustment of body homeostasis.
\r\n\t
\r\n\tThe aim of this book is to discuss further various aspects of homeostasis, information that we hope to be useful to scientists, clinicians, and the wider public alike.
The growth of humanity and the development of science and technology are causing an environmental disorder due to the pollution of water by a number of pollutants including synthetic dyes, which have a complex molecular structure, which makes them more stable and difficult. These dyes are discharged with the liquid effluents, which are most of the time directly discharged into watercourses without prior treatment [1]. These colored discharges pose a great threat to human health and the environment because many of the dyes are toxic. Therefore, it is very important to develop efficient methods for the removal of dyes from aqueous media.
Traditional processes such as biological processes give unsatisfactory results due to the composition of these releases of toxic and dyestuffs, which are difficult to biodegrade; on the other hand, physicochemical processes, which include coagulation-flocculation, oxidation, and membrane filtration, have been widely studied and have revealed a high efficiency in water discoloration [2, 3]. These techniques have proven to be very effective, but their high cost has prompted many researchers to try other cheaper and abundant materials. Adsorption, as a flexible, simple, and inexpensive approach, can be used for the removal of pollutant chemical species. Every day researchers describe new ways and elaborate a new adsorbent on the basis of good selectivity for a chemical species or for the low cost of the process. The elimination of dyes in aqueous solutions by adsorption on different solid materials, in particular on activated carbon, has been the subject of much work [4, 5, 6]. The adsorption of dyes on activated carbon has been found to be very effective, but its use remains limited due to the difficulties of its regeneration and its high cost [7].
For this reason, the use of clay as an adsorbent is of great interest because of its effectiveness, its accessible cost, and its abundance. From this perspective, lot of research has been done on clays and all have shown their depollution efficiency toward dyes [8, 9]. The importance given to these materials is granted to their abundant availability in nature and their great capacity of retention of various pollutants, which is offered by their structure in sheets, which gives a large specific surface toward the adsorption [10, 11].
In this context, our choice focused on clay, which is an adsorbent material found in abundance in Morocco, is effective and more economical. The use of clays for the depollution of waters contaminated by dyes requires a good knowledge of their mineralogical characteristics and of the mechanism of adsorption of these pollutants. On the other hand, Natural Safiot Clay (NSC) materials could be an attractive alternative for the adsorption of various pollutants from wastewater due to their low cost, their lamellar structure, which provides high specific surface areas, thermal stability, high cation exchange capacity, abundance, and high adsorption capacity [12, 13, 14].
Generally, most of the studies carried out to eliminate cationic dyes are carried out on single dyes [15], which lead us to study the possibility of eliminating a mixture of two dyes at the same time, close to the real conditions of effluents in the environment, because industrial discharges are a complex mixture of several pollutants.
Recently and with computer development, quantum chemistry calculations are widely used in studies of dye adsorption [16, 17]. A lot of research has been carried out on this theoretical approach; in particular since the appearance of their efficiencies in the study of adsorption mechanisms on the one hand, and the study of behavior of dyes with respect to the adsorbent surface on the other hand [18, 19]. The theoretical reactivity indices based on density functional theory (DFT) have become a powerful and informative tool for studying organic reactivity and for describing intermolecular interactions [20, 21].
The purpose of this study was to demonstrate the ability of Natural Safiot Clay to remove some mixture industrial dyes such as Basic blue 9 and Basic yellow 28 from single and binary aqueous solutions. This allows optimizing the cost of the process and the quantity of the adsorbent used in the adsorption process to have a multiple removal efficiencies of different pollutants instead of one [22, 23]. In this study, the possibility of adsorption of Basic blue 9 (BB9) and Basic yellow 28 (BY28) in the single and binary system has been studied experimentally and theoretically using density functional theory (DFT) and molecular dynamics simulations (MDS), In order to explain the competitiveness between the two dyes on active sites and their reactivity, check if the experimental results are in good correlation with the theoretical results.
The Natural Safiot Clay (NSC) used in this work is collected from a natural basin in the region of Safi in Morocco for removal of Basic blue 9 and Basic yellow 28 from aqueous solutions and used without any prior activation. Samples were ground and sieved to obtain very fine particle sizes and washed with distilled water to ensure the removal of dust and any soluble impurities may exist.
Basic blue 9 (BB9) and Basic yellow 28 (BY28) as representative cationic dyes were purchased from Sigma-Aldrich with a purity of 99% and used without further purification. The chemical structures of the studied dyes are given in Figure 1.
Chemical structures of BB9 and BY28.
For single and binary system, several stock solutions 100 mg/L of BB9 and BY28 dyes have been prepared by mixing calculated volumes of the stock solutions of each dye and accurately diluting it with distilled water. The NSC and mixture of dyes are shaken in batch experiments at various parameters such as the amount of natural safiot clay (5 mg–35 mg), initial dye concentration (10 mg–40 mg/L), and initial solution pH (2–12). The working solutions’ pH was adjusted to the desired values with dilute HCl (0.1 M) or NaOH (0.1 M) using a pH-Meter HANNA 5222. After stirring of a prescribed contact time, the solution is filtrated using filter syringe, and the maximum absorbance value of BB9 and BY28 is measured using spectrophotometer UV–Visible (JENWAY 6300) at 663 and 438 nm, respectively, as shown in Figure 2.
Absorption spectra of BB9, BY28, and their mixture.
The percentage removal and the quantity adsorbed qe (mg/g) of dye on NSC were calculated using the following equation:
Where C0 (mg/L) and Ce (mg/L) represent the concentration of BB9 and BY28 at initial and equilibrium, respectively, V (L) is the volume of solution, and W (g) is the weight of adsorbent used.
The surface morphology, chemical composition, and the nature of functional groups responsible for adsorption of dyes onto the NSC adsorbent were studied by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX), X-Ray Diffraction (XRD), and Fourier Transform Infrared (FT-IR).
Density functional theory (DFT) is one of the most important tools of quantum chemistry of understanding popular qualitative chemical concepts such as energy of highest occupied molecular orbital (EHOMO) and the lowest unoccupied molecular orbital (ELUMO), dipole potential (μ), hardness (η), softness (S), electrophilicity index (ω), and local reactivity descriptors such as Parr function P(r) [24, 25]. All computations are carried out with the Gaussian 09 program. The geometries of dyes, BB9 and BY28, are optimized using density functional theory (DFT) at the B3LYP/6 G-31G (d) level. Optimizations are carried out using the Berny analytical gradient optimization method. The geometries optimized are characterized by positive vibrational frequency definite Hessian matrices [26].
When the values of EHOMO and ELUMO are known, one can determine through the following expressions [27] the values of the electronic chemical potential μ, the absolute hardness η, and the softness S as:
The global softness (S) introduced is the inverse of the global hardness [28]:
Using Parr’s definition [29], the electrophilicity ω index is given by:
Based on this idea, Domingo et al. [30] have introduced an empirical (relative) nucleophilicity index N, based on the HOMO energies obtained within the Kohn–Sham scheme and defined as:
The HOMO energy of Tetracyanoethylene is −0.3351 a.u. at the same level of theory.
The electrophilic
The adsorption progress of the studied dyes on kaolinite surface is performed using Materials Studio (MS) 8.0 software developed by Accelrys Inc. The kaolinite crystal was optimized (a = 5.196 Å, b = 9.007 Å, c = 7.372 Å, and α = 93.029°, β = 105.983°, γ = 89.866) and cleaved along the (001) plane, a vacuum slab with 10 Å thickness was built. The final structure was enlarged to (4 × 2 × 1) to provide a large surface for the interaction of the dyes [31].
The spectrum of chemical constitution of natural safiot clay adsorbent is given in Figure 3. The EDX spectrum of Figure 2 presents well-defined peaks, confirms the presence of the following chemical elements: Si, Al, Mg, Fe, K, P, S, O, Ca, C. These results confirm those found by the analysis XRF (Table 1), which also reveals the presence of these elements in the form of oxides: SiO2, Al2O3, Fe2O3, MgO, Na2O, CaO, K2O, TiO2. The atomic and mass percentages of the elements are summarized in Table 2. The predominance of silicon and oxygen peaks is clearly observed, which confirms the majority presence of kaolinite and quartz in the sample studied.
EDX spectrum of natural safiot clay.
Dyes | Langmuir | Freundlich | Dubinin–Radushkevich | |||||||
---|---|---|---|---|---|---|---|---|---|---|
qmax | RL | R2 | KF | 1/n | R2 | qmax | KD-R | E | R2 | |
BB9 (S) | 68.49 | 0.038 | 0.992 | 37.89 | 0.314 | 0.852 | 59.03 | 1 10−7 | 2236 | 0.925 |
BB9 (B) | 41.15 | 0.015 | 0.996 | 25.71 | 0.222 | 0.566 | 44.78 | 1 10−7 | 2236 | 0.897 |
BY28 (S) | 166.67 | 0.363 | 0.761 | 10.50 | 0.976 | 0.955 | 75.33 | 2 10−6 | 500 | 0.994 |
BY28 (B) | 21.32 | 0.064 | 0.986 | 10.07 | 0.260 | 0.718 | 18.18 | 6 10−7 | 912.8 | 0.512 |
Isotherm constants for BB 9 and BY28 in single and binary systems.
Eléments | % atomique | % massique |
---|---|---|
O | 57.87 | 46.70 |
C | 12.96 | 7.85 |
Na | 0.59 | 0.68 |
Mg | 1.25 | 1.53 |
Al | 7.50 | 10.20 |
Si | 13.78 | 19.52 |
K | 2.18 | 4.30 |
Ca | 1.70 | 3.43 |
Ti | 0.19 | 0.47 |
Fe | 1.72 | 4.85 |
Atomic and mass percentage of the natural safiot clay constituents.
Fourier transform infrared (FTIR) analysis was applied to determine the functional groups present on the surface of natural safiot clay and understand its adsorption mechanism. FT-IR spectra of NS clay in the range of 400 cm–4000 cm−1 are taken to obtain information on the nature of functional groups at the surface of the adsorbent. The spectrum of natural safiot clay is shown in Figure 4. The band that stretches between 3200 and 3700 cm−1 shows a peak with two shoulders at 3407 cm−1 and 3610 cm−1 corresponding to the vibrations of elongation of the hydroxyl group –OH linked to the water of constitution. In addition to the vibrations of deformation of the O-H bond due to the water molecules adsorbed between the sheets located at 1639 cm−1. The bands that appear approximately around 3430 cm−1 and 1630 cm−1 correspond respectively to the vibrations of elongation and deformation of the OH group of the adsorbed water [32]. While the characteristic bands of carbonates are detected at 1436 cm−1 and 2521 cm−1 [33].
FT-IR spectrum of natural safiot clay.
An intense absorption band at 900 cm–1200 cm−1 is centered on 1030 cm−1, it characterizes the valence vibrations of the Si-O bond [34]. The bands between 795 and 748 cm−1, coming from the Si-O-Al bond, also give way to a band around 778.4 cm−1 [35]. The absorption band located at 1030 cm−1 is in agreement with the X-ray fluorescence indicating the presence of kaolinite in natural clay. However, the absorption bands at 423, 480, 534, 694, and 797 cm−1 correspond to quartz [36]. These results are in agreement with those found from XRF. They confirm the presence of quartz, carbonate, kaolinite, and dolomite in the clay studied.
Scanning electronic microscopy (SEM) technique was carried out in order to observe the morphology, structure, and distribution of the grains of our adsorbent material studied. Figure 5a and b show the SEM micrographs of natural safiot clay before and after adsorption. The scanning electron microscope image (Figure 5a) shows aggregates of kaolin grains in spherical form and of heterogeneous size, the interstices between the grains form pores. We also observe large irregularly shaped cavities; this confirms the heterogeneous composition of our clay revealed by the XRD (kaolinite + calcite + vermiculite). In contrast, Figure 5b shows that the NSC surface is more homogeneous and saturated after adsorption.
SEM micrograph of the natural Safi clay before (a) and after (b) adsorption.
X-ray diffraction analysis allows us to identify the different mineralogical phases contained in our material. The X-ray diffraction patterns of NSC adsorbent are illustrated in Figure 6. This result demonstrated the principal presence of kaolinite characterized by an intense peak at 2θ = 26.63° (2θ = 26.63°; d = 3.343 A°) and a series of peaks with varying intensities at 12.33°, 19.75°, 40.85° and those of quartz at 20.87°, 37.92°, and 42. 440°. In addition, the reflections spectrum at 2θ = 8.62°, 29.47°, and 30.81° confirm the presence of illite, calcite, and dolomite, respectively. We see that the diagram also shows the presence of the peak corresponding to the following minerals: kaolinite, calcite, and vermiculite, which implies that our clay is heterogeneous.
X-ray diffraction of natural safiot clay.
The pH zero-point charge (pHzpc) plays an important role in the adsorption process. The point of zero charge (PZC) of our clay was determined using the pH drift method [37]. Six vials containing solutions of pH in the range of 2–12 (pHi) and 50 mg of NSC are shaken for 24 h at room temperature, and the final pH was measured. The difference between the initial and final pH (ΔpH = pHi − pHf) was plotted against the initial pH (pHi) and the point where ΔpH = 0 was taken as the point of zero charge. As shown in Figure 7, the pHpzc of NSC was determined to be 7.2.
pHzpc values for natural safiot clay.
It is recognized that the effect of the adsorbent dose on the adsorption process is also considered to be one of the most important parameters that must be optimized, since the mass of adsorbent has an effect on the adsorption capacity for a given initial concentration of the adsorbate under the operating conditions. The adsorption of BB9 and BY28 in single and mixture on natural safiot clay is studied by varying the mass of adsorbent from 5 to 35 mg in 50 mL solution of 20 mg/L dye concentration at a constant stirring rate of 60 minutes. From Figure 8, it can be observed that removal efficiency of the dye increases from 49.17% to 97.03% for BB9 and from 59.97% to 85.32% for BY28 as adsorbent dose is an increase from 5 to 35 mg. This is because of the extra number of adsorption sites accessible with an increase in the adsorbent dose. On the other hand, the dye uptake capacity reduces from 98.34 to 27.72 mg/g and from 119.9 to 24.38 mg/g for BB9 and BY28, respectively. This can be attributed to the unsaturation of adsorption sites through the adsorption reaction with increasing adsorbent dosage [38, 39]. Another important reason is that at high adsorbent dosage, the available dye molecules are deficient to completely cover the available binding sites on the natural safiot clay, which results in low solute uptake [40, 41]. Similar results have been reported previously by other researchers for the adsorption of dyes by different material [42, 43, 44]. The optimum adsorbent dose is fixed conveniently at 30 mg per 50 mL of solution dye for the following studies.
Effect of adsorbent amount on the removal efficiency and adsorption capacity of basic blue 9 and basic yellow 28.
The effect of initial concentrations of BB9 and BY28 dyes is examined at different initial concentrations ranging from 10 to 40 mg/L on the adsorption capacity and removal efficiency onto natural safiot clay. As seen from Figure 9, the adsorption capacity increases from 22.58 to 58.45 mg/g for BB9 and of 12.68 to 58.89 mg/g for BY28. In this case, the % removal decreases from 98.97% to 87.67% and from 87.67% to 70.08% for BB9 and BY28, respectively. These results indicate that the adsorption sites of NSC adsorbent for dyes adsorption are still unsaturated within the dye concentration range. In addition, increasing initial dyes’ concentrations increases the number of collisions between dyes ions and the surface area of NSC adsorbent, which enhances the adsorption process [45]. Similar results had been reported by Auta and Hameed [46] for MB dye removal onto modified ball clay chitosan composite.
Effect of the initial dye concentration on the adsorption capacity and removal efficiency of BB9 and BY28, in single (S) and binary (B) system onto NSC.
The initial pH of the aqueous solution is important parameter controlling the adsorption process, where it affects both the degree of ionization of the dye and the surface properties of the adsorbent. The effect of initial pH of dye solution on the percentage removal of dye is studied by varying the initial pH from 2 to 12 under constant process parameters onto NSC, and results are presented in Figure 10. It is revealed that pH has practically a small effect on the percentage removal of the two basic dyes in simple system. In binary system, BY28 percentage removal is increased from 34.74% to 71.14% when pH is varied from 2 to 12 and also for BB9 percentage removal increase from 90.86% to 96.75%. These results are explained by pH zero-point charge pHZPC, the pHzpc of any adsorbent is a very important characteristic that determines the pH at which the surface has net electrical neutrality. In explaining this behavior by the fact that the negative charge dominates the adsorbent’s surface in the basic medium. Thus, an electrostatic attraction exists between the negative charges of OH deposited on the clay surface and the positive charges of the dyes. Moreover, an electrostatic attraction between BB9 and BY28 dyes and the positive charge on the surface of NSC at low pH are evident [47, 48].
Effect of initial dye pH on the removal efficiency (%) of BB9 and BY28 dyes in single and binary solutions.
Consequently, the mechanism proposed can be described by the following equation.
Similarly, the montmorillonite, bentonite clay, and montmorillonite/CoFe2O4 composite adsorption capacities were studied as a function of pH, and it was observed that maximum basic blue 9 dye adsorption was in acidic pH range [49, 50, 51].
BB9 and BY28 adsorption in single and binary adsorption systems onto NSC is studied and is illustrated in Figure 11. The removal efficiency decreased in binary systems (as compared with single dye systems), the reduction was from 96.20% to 95.57% and from 85.32% to 61.48% for BB9 and BY28, respectively. For these results it is clear in the binary system, BB9 dye is most dominant and BY28 is most recessive dye. The values of R% also show that adsorption of BB9 and BY28 is reduced by the presence of other dyes in solutions within reduction percentage of 0.66% and 27.94% for BB9 and BY28, respectively. This behavior can be explained by the competitive adsorption between BB9 and BY28 for active sites with that BB9 dye is the first to be adsorbed in the active sites and by that BB9 is more electrophilic than BY28. This result will be demonstrated by following quantum chemicals study.
Adsorption competition of BB9 and BY28 onto NSC adsorbent sites.
Adsorption isotherms play an important role in the determination of the maximum adsorption capacity and the identification of the type of adsorption. The results of the adsorption experiments were analyzed per the well-known models of Langmuir, Freundlich, and Dubinin–Radushkevich (D-R):
The Langmuir isotherm is valid for monolayer adsorption on surface containing a finite number of identical sites [52]. The linear form of the Langmuir isotherm can be represented by the following equation:
Where Ce (mg/L) represents the equilibrium concentration of the adsorbate, qe the amount adsorbed at equilibrium (mg/g), KL (L/mg) and qm (mg/g) are the Langmuir constant and the maximum amount of adsorbate, respectively.
To confirm the favorability of the adsorption process, the separation factor RL was calculated by the following Equation [53]:
where the adsorption process is unfavorable (RL > 1), linear (RL = 1), favorable (0 < RL < 1), or irreversible (RL = 0). Here, RL values for the adsorption of dyes are less than 1 and greater than 0, indicating favorable adsorption.
Freundlich isotherm model is an empirical equation based on sorption on a heterogeneous surface or surface supporting sites of varied affinities [54]. The linearized Freundlich model is represented by the following equation:
where Kf (mg/g) is the measurement of adsorption capacity, and 1/n is the adsorption intensity of the adsorbent.
The Dubinin–Radushkevich model is a more generalized model as compared with the Langmuir isotherm and often used to estimate the characteristic porosity and the apparent free energy of adsorption [55]. The linearized Dubinin–Radushkevich (D-R) isotherm model is represented by the following equation:
where qm is the theoretical saturation capacity (mg/g), B is the D-R constant related to the sorption energy (mol2/kJ2), and ε represents the Polanyi potential (J/mol), which is determined by:
R is the universal gas constant (8.314 J mol−1 K−1), and T is the absolute temperature (K). The mean free energy of adsorption E calculated from B using the following relation:
The main parameters, characterizing each model as well as the coefficients of determination (R2), are grouped in Table 1. Comparison with Freundlich and Dubinin–Radushkevich model shows the high correlation coefficient of Langmuir isotherm for both dyes in single and mixture systems. This result suggests that the dye was homogeneously adsorbed on a monolayer surface of the adsorbent.
The value of parameter 1/n of the Freundlich equation gives an indication of the validity of the adsorption of the adsorbent adsorbate system. The values of 1/n presented in Table 1 are between 0 and 1 indicating that the adsorption of the two dyes on our prepared adsorbent material (NSC) is favorable.
The magnitude of E is useful for estimating the type of adsorption process. The found values of E for BB9 and BY28 in the single and binary system are less than 8 kJ mol−1, knowing that energy values less than 8 kJ mol−1 indicate physisorption and energy values varying from 8 to 16 kJ mol−1 indicate chimisorption. Therefore, the adsorption type of BB9 and BY28 onto NSC has been defined as physical adsorption (physisorption). This confirms the results following the study of the influence of pH.
The kinetic of adsorption is an important characteristic in evaluating the efficiency of adsorption process. Three kinetics models (pseudo-first order, pseudo-second order, and intraparticle diffusion) were utilized to test the experimental data and predict the controlling mechanism of dye adsorption process.
The linearized form of pseudo-first-order rate expression is given as:
Where qe and qt are the amount of dye adsorbed on sorbent (mg/g) at equilibrium and time t, respectively, k1corresponds to the reaction rate constant of pseudo-first-order (min−1), and t is time (min) [56]. The values of qe and k1 were calculated from the slope and intercept of the plots of the log (qe-qt) vs. t.
Pseudo-second-order rate expression reaction model is expressed as (linearized form) [57]:
Where k2 is the pseudo-second-order rate constant (g/mg.min). A plot of t/qt and t should give a linear relationship if the biosorption follows pseudo-second-order model. The qe and k2 can be calculated from the slope and intercept of the plot.
The intraparticle diffusion model is based on the theory proposed by Weber and Morris [58]. The Weber and Morris equation is (18):
Where qt is the adsorption capacity (mg/g) at time t (min), kid is the intraparticle diffusion rate constant (mg/g.min), and C (mg/g) is a constant, which provides the information regarding the thickness of the boundary layer. The values of Kid and C were calculated from the slope and intercept of the plots of the qt against t1/2.
The conformity between the experimental data and the predicted model is based on the values of the correlation coefficients (R2), hence the value R2 closest to unity will indicate the adequate model to correctly describe the kinetics of adsorption of the dye.
Table 3 summarizes the rate constants and correlation coefficients (R2) of the three kinetic models. The fitting of the kinetic data in the pseudo-second-order equation showed excellent linearity with high correlation coefficient (R2 > 0.999), and the good agreement between the experimental and calculated equilibrium adsorption for the pseudo-second-order model confirms that this one describes correctly the adsorption kinetics. Similar results have been observed in the adsorption of basic dyes onto Moroccan Clay [59] and in the adsorption of Methylene Blue (MB) by montmorillonite clay [60].
Dyes concentration (mg/l) | Pseudo-first order | Pseudo-second order | Intraparticle diffusion | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
qe,exp | qe,cal | K1 | R2 | qe,cal | K2 | R2 | Ki | C | R2 | ||
BB9 (S) | 10 | 15.48 | 7.998 | 0.028 | 0.752 | 15.78 | 0.198 | 0.999 | 0.095 | 16.20 | 0.965 |
20 | 32.07 | 0.857 | 0.037 | 0.668 | 31.34 | 0.156 | 1.000 | 0.114 | 30.24 | 0.976 | |
30 | 47.72 | 1.795 | 0.074 | 0.403 | 47.85 | 0.109 | 1.000 | 0.306 | 45.66 | 0.794 | |
40 | 58.45 | 8.707 | 0.061 | 0.783 | 58.82 | 0.022 | 0.999 | 0.927 | 51.51 | 0.971 | |
BY28(S) | 10 | 12.69 | 0.129 | 0,112 | 0,485 | 12.73 | 0,470 | 1.000 | 0,100 | 12.04 | 0,542 |
20 | 28.39 | 0.823 | 0,070 | 0,826 | 28.49 | 0,246 | 1.000 | 0,098 | 27.72 | 0,798 | |
30 | 43.14 | 2.960 | 0,023 | 0,913 | 42.37 | 0,071 | 0,999 | 0,316 | 39.86 | 0,905 | |
40 | 58.89 | 96.11 | 0,285 | 0,858 | 59.88 | 0,020 | 0,999 | 1.499 | 49,17 | 0,825 | |
BB9(B) | 10 | 15.80 | 23.22 | 0.041 | 0.484 | 15.82 | 1.051 | 1.000 | 0.036 | 15.54 | 0.756 |
20 | 32.01 | 1.376 | 0.054 | 0.892 | 32.05 | 0.191 | 1.000 | 0.217 | 30.45 | 0.843 | |
30 | 43.82 | 8.586 | 0.064 | 0.937 | 44.05 | 0.031 | 0.999 | 0.881 | 37.27 | 0.982 | |
40 | 48.00 | 51.54 | 0.059 | 0.812 | 50.76 | 0.003 | 0.960 | 4.923 | 9.712 | 0.954 | |
BY28(B) | 10 | 12.08 | 3.088 | 0.177 | 0.813 | 12.16 | 0.209 | 0.999 | 0.131 | 11.19 | 0.873 |
20 | 14.37 | 12.58 | 0.217 | 0.851 | 14.45 | 0.200 | 0.999 | 0.112 | 13.58 | 0.879 | |
30 | 19.49 | 12.36 | 0.165 | 0.898 | 19.68 | 0.087 | 0.999 | 0.423 | 16.69 | 0.649 | |
40 | 20.49 | 1.173 | 0.170 | 0.756 | 20.57 | 0.332 | 1.000 | 0.167 | 19.42 | 0.502 |
Kinetics parameters for the adsorptive removal onto NSC adsorbent of BB9 and BY28 dyes and their mixture.
The global chemical reactivity descriptors, energy gap (ΔE), dipole moment (μ), hardness (η), softness (S), nucleophilicity (N), and electrophilicity index (ω) witch calculated from HOMO and LUMO energies and are obtained at the level of theory B3LYP/6 G-31G(d) and summarized in Table 4.
Dyes | BB9 | BY28 |
---|---|---|
ET (u.a) | −1643.18 | −1754.96 |
μ (Debye) | 11.351 | 7.573 |
EHOMO (eV) | −4.373 | −5.585 |
ELUMO (eV) | −3.210 | −2.387 |
μ (eV) | −3.791 | −3.986 |
η (eV) | 1.163 | 3.198 |
S (eV) | 0.859 | 0.312 |
ω (eV) | 6.178 | 2.480 |
N (eV) | 4.747 | 3.535 |
Quantum chemical parameters of the studied dyes calculated at B3LYP/6 G-31G (d).
Energy gap (ΔEgap = EHOMO – ELUMO): The energy gap between the HOMO and LUMO is very important in determining the chemical reactivity of the molecule dyes toward the adsorption on the adsorbent surface. On the other hand, the decrease in the value of ∆Egap increases the reactivity of the molecule, which facilitates adsorption and increases the adsorption efficiency. It can be seen from Figure 12 that the BB9 dye shows a lower ΔEgap (ΔEgap = 1.163 eV) compared with the BY28 dye, which has a difference of 3.198 eV, which clearly means that the molecule of BB9 is more reactive than BY28, Therefore, the BB9 dye will be adsorbed firstly. This conclusion is in agreement with the experimental results.
Highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) density of BB9 (a) and BY28 (b) BY DFT at the B3LYP/6 G-31G(d).
Dipole moment (μ): The dipole moment (μ) is another important electronic parameter, provides information on the polarity of the whole molecule. The high molecular polarity probably gives rise to great chemical reactivity. It is clearly established in the literature that molecules with high dipole moments are more reactive, and their action results in a significant elimination efficiency. In our case the high dipole moment value of BB9 (11.351 D) probably increases the adsorption between the BB9 dye and the surface of natural clay compared with BY28 (7.573 D), which explains the adsorption efficiency higher than BB9 when compared with BY28 and confirms the experimental results.
The high percentage of elimination of a molecule is linked to a low value for chemical hardness and a high value for softness. In the present work, the values of the global hardness (η) and the softness (σ) presented in Table 5 clearly show that the BB9 dye has the lowest value of the hardness (η = 1.163 eV) and the higher value of the softness (S = 0.859 eV), which explains their significant elimination percentage compared with BY28, these results are in good agreement with the experimental results.
Dye | N° of atoms | Atoms | ||||
---|---|---|---|---|---|---|
BB9 | 1 | C | 0.117052 | −0.091655 | 0.72314726 | — |
2 | C | −0.083178 | 0.109989 | — | 0.52134786 | |
3 | C | 0.123453 | −0.0193705 | 0.76269263 | 0.09195176 | |
4 | C | −0.0825946 | 0.0874818 | −0.51026944 | 0.4152761 | |
5 | C | 0.1346605 | −0.0391588 | 0.83193257 | 0.18588682 | |
6 | C | −0.044701 | 0.0617533 | −0.27616278 | 0.29314292 | |
7 | C | −0.0622537 | 0.0077143 | −0.38459903 | 0.03661978 | |
8 | C | 0.1288878 | −0.049104 | 0.79626883 | 0.23309669 | |
9 | C | −0.0873976 | 0.0988698 | −0.53994237 | 0.46933494 | |
11 | C | 0.1294818 | −0.031639 | 0.79993856 | 0.15019033 | |
12 | C | −0.045136 | 0.063713 | — | 0.30199962 | |
13 | C | 0.1402246 | −0.0370003 | 0.86630758 | 0.17564042 | |
19 | N | 0.3640112 | −0.0556138 | 2.24885996 | — | |
20 | S | 0.1232636 | −0.0137067 | 0.76152252 | — | |
21 | N | 0.0351644 | 0.09916571 | 0.21724566 | 0.47073963 | |
22 | N | 0.0643843 | 0.15539811 | 0.39776621 | 0.73767483 | |
23 | C | −0.0038013 | −0.006641 | — | — | |
27 | C | −0.0035767 | −0.00689 | — | — | |
31 | C | −0.0061814 | −0.005169 | — | — | |
35 | C | −0.0061321 | −0.010607 | — | — | |
39 | Cl | 0.0012179 | 0.5782508 | 0.00752419 | 2.74495655 | |
BY28 | 1 | C | 0.040255 | 0.066561 | 0.105689697 | 0.23709028 |
2 | C | −0.000773 | 0.00887 | — | 0.03159494 | |
3 | C | 0.045305 | 0.037015 | 0.118214337 | 0.13184743 | |
4 | C | −0.025946 | −0.014818 | — | — | |
5 | C | 0.066052 | 0.091588 | 0.172349484 | 0.32623646 | |
6 | C | −0.021701 | −0.037533 | — | — | |
7 | C | −0.036225 | 0.001143 | — | 0.00407137 | |
8 | C | 0.458878 | −0.089904 | 1.197350365 | — | |
13 | C | −0.173976 | 0.168698 | — | 0.60090228 | |
15 | C | −0.014488 | 0.091639 | — | 0.32641812 | |
16 | C | 0.022465 | 0.020003 | 0.058617925 | 0.07125069 | |
17 | C | 0.03011 | −0.006138 | 0.078566023 | — | |
18 | C | −0.011644 | 0.016571 | — | 0.05902590 | |
20 | C | −0.013843 | 0.039811 | — | 0.14180678 | |
22 | C | 0.026365 | 0.07067 | 0.068794195 | 0.25172654 | |
24 | N | 0.121797 | 0.125083 | 0.317804912 | 0.44554565 | |
25 | N | 0.362411 | −0.068898 | 0.945639022 | — | |
26 | N | 0.059207 | 0.13669 | 0.154488825 | 0.48688978 | |
27 | C | −0.006573 | −0.003929 | — | — | |
31 | C | 0.019834 | 0.001078 | 0.051752856 | 0.00383984 | |
35 | C | 0.016503 | 0.000443 | 0.043061278 | 0.00157797 | |
39 | O | 0.000958 | 0.10219 | 0.002499709 | 0.36400078 | |
40 | O | 0.00195 | 0.081069 | 0.005088135 | 0.28876778 | |
42 | C | −0.000314 | −0.005534 | — | — |
Theoretical prediction of reactive sites using Parr function for BB9 and BY28 dyes.
Global electrophilicity index (ω): The global electrophilicity index (ω) represents the capacity of the dyes to accept electrons. More reactive nucleophilic is characterized by lower value of ω, and conversely more reactive electrophilic is characterized by a higher value of ω. From Table 5, we notice that the electrophilicity value of BB9 (ω = 6.178) is greater than that of BY28 (ω = 2.48); this indicates that the molecule of BB9 is more electrophilic than that of BY28. Consequently, BB9 will be adsorbed first followed by BY28.
The local reactivity site of the studied dyes has been analyzed by evaluating Parr functions (PF). The PF is used to obtain the detail information of local reactivity of each atom in the molecule. Domingo proposed the Parr functions P(r) [61], which are given by the following equations:
With these electrophilic and nucleophilic Parr functions are at hand, the local electrophilicity ωK and the local nucleophilicity NK indices will be redefined as follows:
Table 5 shows that at the DFT level, the most susceptible site to a nucleophilic attack for BB9 is located on sulfur, nitrogen, and benzene ring. In the case of an electrophilic attack, the most reactive site is on Cl39. For BY28 the more susceptible sites to nucleophilic attacks are nitrogen and C8 atoms, while N26 and C13 are the most susceptible sites for electrophilic. The results indicated that the BB9 dye has more and strong electrophilic sites than BY28; consequently, BB9 has a high affinity for NSC than BY28.
In this study, Monte Carlo simulations were performed to study the adsorption and orientation of dyes on charged surfaces based on (001) kaolinite surface and all-atom models. The most stable low-energy adsorption configurations of the studied dyes are shown in Figure 13. It is clear that the three dyes examined BB9, BB41, and BY28 are adsorbed almost parallel to the plane to maximize surface and contact coverage. These adsorption configurations indicate that there are strong interactions between the studied dyes and the kaolinite atoms. This facilitates their adsorption to the surface of the kaolinite (001) by blocking a maximum of sites and ensuring a great influence on the removal efficiency.
The most stable low-energy configuration for the adsorption of the dyes on kaolinite surface obtained through the Monte Carlo simulations.
The outputs and descriptors calculated by the Monte Carlo simulation are presented in Table 6. The parameters presented in Table 6 include total energy, in kcal mol−1, of the substrate–adsorbate configuration. As can be seen from Table 6, BB9 gives the maximum adsorption energy in negative value found during the simulation process. High values of adsorption energy indicate that BB9 molecule will give the highest removal efficiency and strong interaction between a kaolinite substrate and the studied dye. These results are in good agreement with experimental findings.
System | Total energy | Adsorption Energy | Rigid adsorption energy | Deformation energy | Ead/Ni Dye |
---|---|---|---|---|---|
BB9/(001) | −111.4 | −150.77 | −166.5 | −317.53 | |
BY28/(001) | −213.8 | −146.92 | −117.07 | −29.89 | −147.18 |
Outputs and descriptors calculated by the Monte Carlo simulations for the lowest adsorption configurations of tested dyes on kaolinite (001) surface (in kcal/Mol).
To further confirm our results, we have performed the energy fluctuation curves as obtained from MD simulations; the equilibration of the system is confirmed by the stable mean values of energy fluctuations, as shown in Table 7. The mean square displacement (MSD) and the diffusion coefficient were calculated after 100,000 steps. The obtained data included in Table 7 show that the diffusion coefficient of the free water molecules was more pronounced (5.85 × 10−6 cm2/s) than the water with BB9 and BY28 molecules. Much smaller diffusion coefficients obtained for water with BB9 were caused by the strong interaction between (water + BB9) and the kaolinite surface, which decreased the mobility of the water [62].
Conditions | Diffusion coefficient of water (10−6 cm2/s) |
---|---|
Free water molecules | 5.850 |
Water with BY28 | 2.430 |
Water with BB9 | 0.450 |
Calculated diffusion coefficient of free water and water with dyes in kaolinite surface.
The results obtained in this research indicated that natural safiot clay was considered low cost, eco-friendly, and a promising alternative adsorbent for removing BB9 and BY28 dyes in single and binary systems from an aqueous solution. The percentage of dye removed increased with increasing adsorbent dosage, decreased with increasing initial dye concentration, and varied with dye solution pH. The rate of adsorption was found to obey pseudo-second-order kinetics model with a good correlation coefficient indicating toward chemisorption, and the Langmuir isotherm represented the equilibrium adsorption and shows monolayer homogeneous surface of adsorbent for single and binary mixture.
All the theoretical parameters of DFT calculations show that the two dyes can be adsorbed and that the BB9 molecule has the best percentage of elimination. From this calculation, we note that: The dye removal efficiency increases with the highest values of ω, ELUMO, μ, and S and the lowest values of ∆Egap, EHOMO and η. Another important conclusion that we can draw from these calculations is that the theoretical results from reactivity descriptors show that nitrogen, sulfur, carbon, and nitrogen atoms of BB9 and BY28 are the main adsorption sites, respectively. Finally, this study displays a good correlation between the theoretical and experimental data, which confirm the reliability of the quantum chemical methods to study the competitive adsorption of two cationic dyes onto NSC surfaces, in the single and binary system. DFT calculations, Monte Carlo method, and Molecular dynamics simulations support the experimental findings.
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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In this paper, the basic concepts of robust optimization are developed, the different types of robustness are defined in detail, the main areas in which it has been applied are described and finally, the future lines of research that appear in this area are included.",book:{id:"6587",slug:"nature-inspired-methods-for-stochastic-robust-and-dynamic-optimization",title:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization",fullTitle:"Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization"},signatures:"José García and Alvaro Peña",authors:[{id:"227809",title:"Ph.D.",name:"Jose",middleName:null,surname:"Garcia",slug:"jose-garcia",fullName:"Jose Garcia"},{id:"240407",title:"Dr.",name:"Alvaro",middleName:null,surname:"Peña",slug:"alvaro-pena",fullName:"Alvaro Peña"}]},{id:"51131",doi:"10.5772/63785",title:"Survey of Meta-Heuristic Algorithms for Deep Learning Training",slug:"survey-of-meta-heuristic-algorithms-for-deep-learning-training",totalDownloads:3140,totalCrossrefCites:15,totalDimensionsCites:24,abstract:"Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers. DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency due to very long training time required. Meta-heuristic aims to find good or near-optimal solutions at a reasonable computational cost. In this article, meta-heuristic algorithms are reviewed, such as genetic algorithm (GA) and particle swarm optimization (PSO), for traditional neural network’s training and parameter optimization. Thereafter the possibilities of applying meta-heuristic algorithms on DL training and parameter optimization are discussed.",book:{id:"5165",slug:"optimization-algorithms-methods-and-applications",title:"Optimization Algorithms",fullTitle:"Optimization Algorithms - Methods and Applications"},signatures:"Zhonghuan Tian and Simon Fong",authors:[{id:"1952",title:"Dr.",name:"Simon",middleName:null,surname:"Fong",slug:"simon-fong",fullName:"Simon Fong"},{id:"186166",title:"MSc.",name:"Zhonghuan",middleName:null,surname:"Tien",slug:"zhonghuan-tien",fullName:"Zhonghuan Tien"}]},{id:"40430",doi:"10.5772/50403",title:"Mathematical Modelling and Numerical Investigations on the Coanda Effect",slug:"mathematical-modelling-and-numerical-investigations-on-the-coanda-effect",totalDownloads:5145,totalCrossrefCites:15,totalDimensionsCites:23,abstract:null,book:{id:"2508",slug:"nonlinearity-bifurcation-and-chaos-theory-and-applications",title:"Nonlinearity, Bifurcation and Chaos",fullTitle:"Nonlinearity, Bifurcation and Chaos - Theory and Applications"},signatures:"A. 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The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.",book:{id:"10065",slug:"wavelet-theory",title:"Wavelet Theory",fullTitle:"Wavelet Theory"},signatures:"Karlton Wirsing",authors:[{id:"325178",title:"Dr.",name:"Karlton",middleName:null,surname:"Wirsing",slug:"karlton-wirsing",fullName:"Karlton Wirsing"}]},{id:"54366",title:"Solution of Differential Equations with Applications to Engineering Problems",slug:"solution-of-differential-equations-with-applications-to-engineering-problems",totalDownloads:6815,totalCrossrefCites:5,totalDimensionsCites:7,abstract:"Over the last hundred years, many techniques have been developed for the solution of ordinary differential equations and partial differential equations. While quite a major portion of the techniques is only useful for academic purposes, there are some which are important in the solution of real problems arising from science and engineering. In this chapter, only very limited techniques for solving ordinary differential and partial differential equations are discussed, as it is impossible to cover all the available techniques even in a book form. The readers are then suggested to pursue further studies on this issue if necessary. After that, the readers are introduced to two major numerical methods commonly used by the engineers for the solution of real engineering problems.",book:{id:"5513",slug:"dynamical-systems-analytical-and-computational-techniques",title:"Dynamical Systems",fullTitle:"Dynamical Systems - Analytical and Computational Techniques"},signatures:"Cheng Yung Ming",authors:[{id:"191017",title:"Dr.",name:"Cheng",middleName:null,surname:"Y.M.",slug:"cheng-y.m.",fullName:"Cheng Y.M."}]},{id:"56538",title:"Stochastic Resonance and Related Topics",slug:"stochastic-resonance-and-related-topics",totalDownloads:1695,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The stochastic resonance (SR) is the phenomenon which can emerge in nonlinear dynamic systems. In general, it is related with a bistable nonlinear system of Duffing type under additive excitation combining deterministic periodic force and Gaussian white noise. It manifests as a stable quasiperiodic interwell hopping between both stable states with a small random perturbation. Classical definition and basic features of SR are regarded. The most important methods of investigation outlined are: analytical, semi-analytical, and numerical procedures of governing physical systems or relevant Fokker-Planck equation. Stochastic simulation is mentioned and experimental way of results verification is recommended. Some areas in Engineering Dynamics related with SR are presented together with a particular demonstration observed in the aeroelastic stability. Interaction of stationary and quasiperiodic parts of the response is discussed. Some nonconventional definitions are outlined concerning alternative operators and driving processes are highlighted. The chapter shows a large potential of specific basic, applied and industrial research in SR. This strategy enables to formulate new ideas for both development of nonconventional measures for vibration damping and employment of SR in branches, where it represents an operating mode of the system itself. Weaknesses and empty areas where the research effort of SR should be oriented are indicated.",book:{id:"6128",slug:"resonance",title:"Resonance",fullTitle:"Resonance"},signatures:"Jiří Náprstek and Cyril Fischer",authors:[{id:"207472",title:"Dr.",name:"Jiri",middleName:null,surname:"Naprstek",slug:"jiri-naprstek",fullName:"Jiri Naprstek"},{id:"213311",title:"Dr.",name:"Cyril",middleName:null,surname:"Fischer",slug:"cyril-fischer",fullName:"Cyril Fischer"}]},{id:"74032",title:"Wavelets for EEG Analysis",slug:"wavelets-for-eeg-analysis",totalDownloads:1208,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and widely used frequency bands in EEG studies. The chapter then progresses to discuss the common artefacts that contaminate EEG signal while recording. With a short overview of wavelet analysis techniques, namely; Continues Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Wavelet Packet Decomposition (WPD), the chapter demonstrates the richness of CWT over conventional time-frequency analysis technique e.g. Short-Time Fourier Transform. Lastly, artefact removal algorithms based on Independent Component Analysis (ICA) and wavelet are discussed and a comparative analysis is demonstrated. The techniques covered in this chapter show that wavelet analysis is well-suited for EEG signals for describing time-localised event. Due to similar nature, wavelet analysis is also suitable for other biomedical signals such as Electrocardiogram and Electromyogram.",book:{id:"10065",slug:"wavelet-theory",title:"Wavelet Theory",fullTitle:"Wavelet Theory"},signatures:"Nikesh Bajaj",authors:[{id:"326400",title:"Dr.",name:"Nikesh",middleName:null,surname:"Bajaj",slug:"nikesh-bajaj",fullName:"Nikesh Bajaj"}]},{id:"70067",title:"Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm",slug:"analytic-prognostic-in-the-linear-damage-case-applied-to-buried-petrochemical-pipelines-and-the-comp",totalDownloads:2681,totalCrossrefCites:3,totalDimensionsCites:3,abstract:"In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set \n\nC\n\n of complex probabilities which is the summation of the set \n\nR\n\n of real probabilities and the set \n\nM\n\n of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in \n\nR\n\n and hence to evaluate all the probabilities. Consequently, the probability in the entire set \n\nC\n=\nR\n+\nM\n\n is permanently equal to one no matter what the stochastic distribution of the input random variable in \n\nR\n\n is; therefore the outcome of the probabilistic experiment in \n\nC\n\n can be determined perfectly. This is due to the fact that the probability in \n\nC\n\n is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences.",book:{id:"7751",slug:"fault-detection-diagnosis-and-prognosis",title:"Fault Detection, Diagnosis and Prognosis",fullTitle:"Fault Detection, Diagnosis and Prognosis"},signatures:"Abdo Abou Jaoude",authors:[{id:"248271",title:"Dr.",name:"Abdo",middleName:null,surname:"Abou Jaoudé",slug:"abdo-abou-jaoude",fullName:"Abdo Abou Jaoudé"}]}],onlineFirstChaptersFilter:{topicId:"163",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81892",title:"Perspective Chapter: Lattice Solitons in a Nonlocal Nonlinear Medium with Self-Focusing and Self-Defocusing Quintic Nonlinearity",slug:"perspective-chapter-lattice-solitons-in-a-nonlocal-nonlinear-medium-with-self-focusing-and-self-defo",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.104824",abstract:"The fundamental lattice solitons are explored in a nonlocal nonlinear medium with self-focusing and self-defocusing quintic nonlinearity. The band-gap boundaries, soliton profiles, and stability domains of fundamental solitons are investigated comprehensively by the linear stability spectra and nonlinear evolution of the solitons. It is demonstrated that fundamental lattice solitons can stay stable for a wide range of parameters with the weak self-focusing and self-defocusing quintic nonlinearity, while strong self-focusing and self-defocusing quintic nonlinearities are shortened the propagation distance of evolved solitons. Furthermore, it is observed that when the instability emerges from strong quintic nonlinearity, increasing anisotropy of the medium and modification of lattice depth can be considered as a collapse arrest mechanism.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mahmut Bağcı, Theodoros P. Horikis, İlkay Bakırtaş and Nalan Antar"},{id:"80350",title:"A Comparison of the Undetermined Coefficient Method and the Adomian Decomposition Method for the Solutions of the Sasa-Satsuma Equation",slug:"a-comparison-of-the-undetermined-coefficient-method-and-the-adomian-decomposition-method-for-the-sol",totalDownloads:41,totalDimensionsCites:0,doi:"10.5772/intechopen.101817",abstract:"This chapter will talk about the mathematical as well as numerical aspects of the Sasa-Satsuma equation that is the extended nontrivial version of nonlinear Schrödinger’s equation. The exact solution will be found out by the undetermined coefficient method. After that, the Adomian decomposition method is secure numerical simulations of computed analytical solutions. The error plots are given to see the accuracy of the results.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mir Asma"},{id:"79127",title:"Soliton Like-Breather Induced by Modulational Instability in a Generalized Nonlinear Schrödinger Equation",slug:"soliton-like-breather-induced-by-modulational-instability-in-a-generalized-nonlinear-schr-dinger-equ",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.100522",abstract:"We consider the nonlinear Schrödinger equation modified by a rational nonlinear term. The model appears in various studies often in the context of the Ginzburg-Landau equation. We investigate modulational instability by means of a linear stability analysis and show how the nonlinear terms affect the growth rate. This analytical result is confirmed by a numerical simulation. The latter analysis shows that breather-like solitons are generated from the instability, and the effects of the nonlinear terms are again clearly seen. Moreover, by employing an auxiliary-equation method we obtain kink and anti-kink soliton as analytical solutions. Our theoretical solution is in good agreement with our numerical investigation.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Saïdou Abdoulkary and Alidou Mohamadou"},{id:"79040",title:"Traveling Wave Solutions and Chaotic Motions for a Perturbed Nonlinear Schrödinger Equation with Power-Law Nonlinearity and Higher-Order Dispersions",slug:"traveling-wave-solutions-and-chaotic-motions-for-a-perturbed-nonlinear-schr-dinger-equation-with-pow",totalDownloads:92,totalDimensionsCites:0,doi:"10.5772/intechopen.100396",abstract:"This chapter aims to study and solve the perturbed nonlinear Schrödinger (NLS) equation with the power-law nonlinearity in a nano-optical fiber, based upon different methods such as the auxiliary equation method, the Stuart and DiPrima’s stability analysis method, and the bifurcation theory. The existence of the traveling wave solutions is discussed, and their stability properties are investigated through the modulational stability gain spectra. Moreover, the development of the chaotic motions for the systems is pointed out via the bifurcation theory. Taking into account an external periodic perturbation, we have analyzed the chaotic behavior of traveling waves through quasiperiodic route to chaos.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Mati Youssoufa, Ousmanou Dafounansou, Camus Gaston Latchio Tiofack and Alidou Mohamadou"},{id:"78957",title:"Resonant Optical Solitons in (3 + 1)-Dimensions Dominated by Kerr Law and Parabolic Law Nonlinearities",slug:"resonant-optical-solitons-in-3-1-dimensions-dominated-by-kerr-law-and-parabolic-law-nonlinearities",totalDownloads:96,totalDimensionsCites:0,doi:"10.5772/intechopen.100469",abstract:"This study investigates the optical solitons of of (3+1)-dimensional resonant nonlinear Schrödinger (3D-RNLS) equation with the two laws of nonlinearity. The two forms of nonlinearity are represented by Kerr law and parabolic law. Based on complex transformation, the traveling wave reduction of the governing model is derived. The projective Riccati equations technique is applied to obtain the exact solutions of 3D-RNLS equation. Various types of waves that represent different structures of optical solitons are extracted. These structures include bright, dark, singular, dark-singular and combined singular solitons. Additionally, the obliquity effect on resonant solitons is illustrated graphically and is found to cause dramatic variations in soliton behaviors.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Khalil S. 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The classical Schrödinger’s equation then occurs as the simplest equation among the set.",book:{id:"10959",title:"The Nonlinear Schrödinger Equation",coverURL:"https://cdn.intechopen.com/books/images_new/10959.jpg"},signatures:"Gérard Gouesbet"}],onlineFirstChaptersTotal:7},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:31,numberOfPublishedChapters:314,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:18,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:14,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. 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He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. 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He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",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. 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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",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]}]},openForSubmissionBooks:{paginationCount:3,paginationItems:[{id:"11601",title:"Econometrics - Recent Advances and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11601.jpg",hash:"bc8ab49e2cf436c217a49ca8c12a22eb",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 13th 2022",isOpenForSubmission:!0,editors:[{id:"452331",title:"Dr.",name:"Brian",surname:"Sloboda",slug:"brian-sloboda",fullName:"Brian Sloboda"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"12141",title:"Leadership - Advancing Great Leadership Practices and Good Leaders",coverURL:"https://cdn.intechopen.com/books/images_new/12141.jpg",hash:"85f77453916f1d80d80d88ee4fd2f2d1",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"July 1st 2022",isOpenForSubmission:!0,editors:[{id:"420133",title:"Dr.",name:"Joseph",surname:"Crawford",slug:"joseph-crawford",fullName:"Joseph Crawford"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"12139",title:"Global Market and Trade",coverURL:"https://cdn.intechopen.com/books/images_new/12139.jpg",hash:"fa34af07c3a9657fa670404202f8cba5",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"July 21st 2022",isOpenForSubmission:!0,editors:[{id:"243649",title:"Dr.Ing.",name:"Ireneusz",surname:"Miciuła",slug:"ireneusz-miciula",fullName:"Ireneusz Miciuła"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{},subseriesFiltersForOFChapters:[],publishedBooks:{},subseriesFiltersForPublishedBooks:[],publicationYearFilters:[],authors:{}},subseries:{item:{},onlineFirstChapters:{},publishedBooks:{},testimonialsList:[]},submityourwork:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],subseriesList:[],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/24447",hash:"",query:{},params:{id:"24447"},fullPath:"/chapters/24447",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)}()