A molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization.

It is impossible to optimize a process for a target drug product with the desired profile without a proper understanding of the interplay among the material attributes, the process parameters, and the attributes of the drug product. There is a particular need to bridge the micro- and mesoscale events that occur during this process. Here, we propose а molecular engineering methodology for the continuous cocrystallization process, based on Raman spectra measured experimentally with a probe and from quantum mechanical calculations. Using molecular dynamics simulations, the theoretical Raman spectra were calculated from first principles for local mixture structures under an external shear force at various temperatures. A proof of concept is developed to build the process design space from the computed data. We show that the determined process design space provides valuable insight for optimizing the cocrystallization process at the nanoscale, where experimental measurements are difficult and/or inapplicable. The results suggest that our method may be used to target cocrystallization processes at the molecular scale for improved pharmaceutical synthesis.

Resource recovery toward sustainability through nutrient removal from landfill leachate.

This study investigated the feasibility of integrated ammonium stripping and/or coconut shell waste-based activated carbon (CSWAC) adsorption in treating leachate samples. To valorize unused biomass for water treatment application, the adsorbent originated from coconut shell waste. To enhance its performance for target pollutants, the adsorbent was pretreated with ozone and NaOH. The effects of pH, temperature, and airflow rate on the removal of ammoniacal nitrogen (NH3-N) and refractory pollutants were studied during stripping alone. The removal performances of refractory compounds in this study were compared to those of other treatments previously reported. To contribute new knowledge to the field of study, perspectives on nutrients removal and recovery like phosphorus and nitrogen are presented. It was found that the ammonium stripping and adsorption treatment using the ozonated CSWAC attained an almost complete removal (99%) of NH3-N and 90% of COD with initial NH3-N and COD concentrations of 2500 mg/L and 20,000 mg/L, respectively, at optimized conditions. With the COD of treated effluents higher than 200 mg/L, the combined treatments were not satisfactory enough to remove target refractory compounds. Therefore, further biological processes are required to complete their biodegradation to meet the effluent limit set by environmental legislation. As this work has contributed to resource recovery as the driving force of landfill management, it is important to note the investment and operational expenses, engineering applicability of the technologies, and their environmental concerns and benefits. If properly managed, nutrient recovery from waste streams offers environmental and socio-economic benefits that would improve public health and create jobs for the local community.

Chloroquine (antimalaria medication with anti SARS-CoV activity) solubility in supercritical carbon dioxide.

Unfortunately, malaria still remains a major problem in tropical areas, and it takes thousands of lives each year and causes millions of infected cases. Besides, on December 2019, a new virus known as coronavirus appeared, that its rapid prevalence caused the World Health Organization (WHO) to consider it a pandemic. As a potential drug for controlling or treating these two undesired diseases at the cellular level, chloroquine and its derivatives are being investigated, although they possess side effects, which must be reduced for effective and safe treatments. With respect to the importance of this medicine, the current research aimed to calculate the solubility of chloroquine in supercritical carbon dioxide, and evaluated effect of pressure and temperature on the solubility. The pressure varied between 120 and 400 bar, and temperatures between 308 and 338 K were set for the measurements. The experimental results revealed that the solubility of chloroquine lies between 1.64 × 10-5 to 8.92 × 10-4 (mole fraction) with different functionality to temperature and pressure. Although the solubility was indicated to be strong function of pressure and temperature, the effect of temperature was more profound and complicated. A crossover pressure point was found in the solubility measurements, which indicated similar behaviour to an inflection point. For the pressures higher than the crossover point, the temperature indicated direct effect on the solubility of chloroquine. On the other hand, for pressures less than the crossover point, temperature enhancement led to a reduction in the solubility of chloroquine. Moreover, the obtained solubility results were correlated via semi-empirical density-based thermodynamic correlations. Five correlations were studied including: Kumar & Johnston, Mendez-Santiago-Teja, Chrastil, Bartle et al., and Garlapati & Madras. The best performance was obtained for Mendez-Santiago-Teja's correlation in terms of average absolute relative deviation percent (12.0%), while the other examined models showed almost the same performance for prediction of chloroquine solubility.

Design and synthesis of drug hydrogels containing carboxymethylcellulose with honeycomb structure and pH-sensitivity as drug delivery systems for adriamycin, metformin, and naproxen.

The aim of this research is to prepare and identify functionalized carboxymethylcellulose/mesoporous silica nanohydrogels (CMC/NH2-MCM-41) for obtaining a pH-sensitive system for the controlled release of drugs. The beads of CMC/NH2-MCM-41 nanocomposites were prepared by dispersing NH2-MCM-41 in a CMC polymer matrix and crosslinking with ferric ions (Fe3+). The SEM analysis of samples revealed enhancement in surface porosity of the functionalized nanohydrogel beads compared to the conventional beads. Swelling of the prepared functionalized nanohydrogels was evaluated at various pH values including pH = 7.35-7.45 (simulated body fluid or healthy cells), pH = 6 (simulated intestinal fluid), and pH = 1.5-3.5 (simulated gastric fluid). The swelling of CMC/MCM-41 and CMC/NH2-MCM-41 nanohydrogels at the pH values of simulated body fluid and simulated intestinal fluid is much higher than that of simulated gastric fluid, indicating successful synthesis of pH-sensitive nanohydrogels for drug delivery. The drug loading results showed that drug release in the CMC/NH2-MCM-41 system is much slower than that in the CMC/MCM-41 system. The results of the survival studies for the manufactured systems showed a very good biocompatibility of the designed drug delivery systems for biological applications. By coating the surface of functionalized mesopores with CMC hydrogel, we were able to develop a pH-sensitive intelligent drug delivery system.

Efficient green Cr(VI) adsorbent from sorghum waste: Eco-designed functionalized mesoporous silica FDU-12.

This paper presents an eco-design approach to the synthesis of a highly efficient Cr(VI) adsorbent, utilizing a positively charged surface mesoporous FDU-12 material (designated as MI-Cl-FDU-12) for the first time. The MI-Cl-FDU-12 anion-exchange adsorbent was synthesized via a facile one-pot synthesis approach using sodium silicate extracted from sorghum waste as a green silica source, 1-methyl-3-(triethoxysilylpropyl) imidazolium chloride as a functionalization agent, triblock copolymer F127 as a templating or pore-directing agent, trimethyl benzene as a swelling agent, KCl as an additive, and water as a solvent. The synthesis method offers a sustainable and environmentally friendly approach to the production of a so-called "green" adsorbent with a bimodal micro-/mesoporous structure and a high surface area comparable with the previous reports regarding FDU-12 synthesis. MI-Cl-FDU-12 was applied as an anion exchanger for the adsorption of toxic Cr(VI) oxyanions from aqueous media and various kinetic and isotherm models were fitted to experimental data to propose the adsorption behavior of Cr(VI) on the adsorbent. Langmuir model revealed the best fit to the experimental data at four different temperatures, indicating a homogeneous surface site affinity. The theoretical maximum adsorption capacities of the adsorbent were found to be 363.5, 385.5, 409.0, and 416.9 mg g-1 at 298, 303, 308, and 313 K, respectively; at optimal conditions (pH=2, adsorbent dose=3.0 mg, and contact time of 30 min), surpassing that of most previously reported Cr(VI) adsorbents in the literature. A regeneration study revealed that this adsorbent possesses outstanding performance even after six consecutive recycling.

Pressure and temperature predictions of Al2O3/water nanofluid flow in a porous pipe for different nanoparticles volume fractions: combination of CFD and ACOFIS.

Artificial intelligence (AI) techniques have illustrated significant roles in finding general patterns of CFD (Computational fluid dynamics) results. This study is conducted to develop combination of the ant colony optimization (ACO) algorithm with the fuzzy inference system (ACOFIS) for learning the CFD results of a physical case study. This binary join of the ACOFIS and CFD was used for pressure and temperature predictions of Al2O3/water nanofluid flow in a heated porous pipe. The intelligence of ACOFIS is investigated for different input numbers and pheromone effects, as the ant colony tuning parameter. The results showed that the intelligence of the ACOFIS could be found for three inputs (x and y nodes coordinates and nanoparticles fraction) and the pheromone effect of 0.1. At the system intelligence, the ACOFIS could predict the pressure and temperature of the nanofluid on any values of the nanoparticles fraction between 0.5 and 2%. Comparing the ANFIS and the ACOFIS, it was shown that both methods could reach the same accuracy in predictions of the nanofluid pressure and temperature. The root mean square error (RMSE) of the ACOFIS (~ 1.3) was a little more than that of the ANFIS (~ 0.03), while the total process time of the ANFIS (~ 213 s) was a bit more than that of the ACOFIS (~ 198 s). The AI algorithms process time (less than 4 min) shows their ability in the reduction of CFD modeling calculations and expenses.

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results.

Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

Predicting Air Superficial Velocity of Two-Phase Reactors Using ANFIS and CFD.

In predicting the turbulence property of gas (bubble) flow in the domain of continuous fluid and liquid, the integration of machine learning and computational fluid dynamics (CFD) methods reduces the overall computational time. This combination enables us to see the effective input parameters in the engineering process and the impact of operating conditions on final outputs, such as gas hold-up, heat and mass transfer, and the flow regime (uniform bubble distribution or nonuniform bubble properties). This paper uses the combination of machine learning and single-size calculation of the Eulerian method to estimate the gas flow distribution in the continuous liquid fluid. To present the machine-learning method besides the Eulerian method, an adaptive neuro-fuzzy inference system (ANFIS) is used to train the CFD finding and then estimate the flow based on the machine-learning method. The gas velocity and turbulent eddy dissipation rate are trained throughout the bubble column reactor (BCR) for each CFD node, and the artificial BCR is predicted by the ANFIS method. This smart reactor can represent the artificial CFD of the BCR, resulting in the reduction of expensive numerical simulations. The results showed that the number of inputs could significantly change this method's accuracy, representing the intelligence of method in the learning data set. Additionally, the membership function specifications can impact the accuracy, particularly, when the process is trained with different inputs. The turbulent eddy dissipation rate can also be predicted by the ANFIS method with a similar model pattern for air superficial gas velocity.

Molecular engineering of cocrystallization process in holt melt extrusion based on kinetics of elementary molecular processes.

Continuous co-crystallization in a twin-screw granulator is a promising technology. In order to fundamentally optimize the process flow, it is necessary to investigate the kinetics of molecular interactions within the mixture and the effect of these interactions on co-crystal formation. In this study, the processes governing the co-crystallization of ibuprofen and nicotinamide were considered. Density functional theory calculations employing the Hirshfeld partitioning scheme were used to identify donor-acceptor sites on each molecule. A total of twenty-one different molecular interactions was identified (nine of ibuprofen and nicotinamide (resembling co-crystals), three of ibuprofen and itself (resembling the ibuprofen dimer), and nine of nicotinamide and itself (resembling the nicotinamide dimer)). Each interaction was defined as an artificial reversible reaction and the kinetics were calculated using the transition state theory of chemical reactions, where linear and quadratic synchronous transition methods were utilized to identify transition-state structures; the minimum energy path was determined using the nudged elastic band method. A kinetic Monte Carlo framework was used to study the collective/coupled effect of reactions on the progress of the co-crystallization process. it was found that operating at low temperatures (especially lower or very close to the melting temperature of ibuprofen) for longer residency times creates a safe route for maximizing the presence of ibuprofen and nicotinamide co-crystals. If the proposed route is applied, the purity and properties of the produced co-crystal would be significant, especially its desirable availability within the body.

Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors.

To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas-liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe.

Heat transfer augmentation of the nanofluids is still an attractive concept for researchers due to rising demands for designing efficient heat transfer fluids. However, the pressure loss arisen from the suspension of nanoparticles in liquid is known as a drawback for developing such novel fluids. Therefore, prediction of the nanofluid pressure, especially in internal flows, has been focused on studies. Computational fluid dynamics (CFD) is a commonly used approach for such a prediction of fluid flow. The CFD tools are perfect and precise in prediction of the fluid flow parameters. But they might be time-consuming and expensive, especially for complex models such as 3-dimension modeling and turbulent flow. In addition, the CFD could just predict the pressure, and it is disabled for finding the relationship of such variables. This study is intended to show the performance of the artificial intelligence (AI) algorithm as an auxiliary method for cooperation with the CFD. The turbulent flow of Cu/water nanofluid warming up in a pipe is considered as a sample of a physical phenomenon. The AI algorithm learns the CFD results. Then, the relation between the CFD results is discovered by the AI algorithm. For this purpose, the adaptive network-based fuzzy inference system (ANFIS) is adopted as AI tool. The intelligence condition of the ANFIS is checked by benchmarking the CFD results. The paper outcomes indicated that the ANFIS intelligence is met by employing gauss2mf in the model as the membership function and x, y, and z coordinates, the nanoparticle volume fraction, and the temperature as the inputs. The pressure predicted by the ANFIS at this condition is the same as that predicted by the CFD. The artificial intelligence of ANFIS could find the relation of the nanofluid pressure to the nanoparticle fraction and the temperature. The CFD simulation took much more time (90-110 min) than the total time of the learning and the prediction of the ANFIS (369 s). The CFD modeling was done on a workstation computer, while the ANFIS method was run on a normal desktop.

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm.

In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow.

Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system.

Computational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

High performance ozone based advanced oxidation processes catalyzed with novel argon plasma treated iron oxyhydroxide hydrate for phenazopyridine degradation.

The present study has focused on the degradation of phenazopyridine (PhP) as an emerging contaminant through catalytic ozonation by novel plasma treated natural limonite (FeOOH·xH2O, NL) under argon atmosphere (PTL/Ar). The physical and chemical characteristics of samples were evaluated with different analyses. The obtained results demonstrated higher surface area for PTL/Ar and negligible change in crystal structure, compared to NL. It was found that the synergistic effect between ozone and PTL/Ar nanocatalyst was led to highest PhP degradation efficiency. The kinetic study confirmed the pseudo-first-order reaction for the PhP degradation processes included adsorption, peroxone and ozonation, catalytic ozonation with NL and PTL/Ar. Long term application (6 cycles) confirmed the high stability of the PTL/Ar. Moreover, different organic and inorganic salts as well as the dissolved ozone concentration demonstrated the predominant role of hydroxyl radicals and superoxide radicals in PhP degradation by catalytic Ozonation using PTL/Ar. The main produced intermediates during PhP oxidation by PTL/Ar catalytic ozonation were identified using LC-(+ESI)-MS technique. Finally, the negligible iron leaching, higher mineralization rate, lower electrical energy consumption and excellent catalytic activity of PTL/Ar samples demonstrate the superior application of non-thermal plasma for treatment of NL.

Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide.

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10-05 and 1.07 × 10-03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308-338 K and 12-40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.

Controlled release evaluation of paracetamol loaded amine functionalized mesoporous silica KCC1 compared to microcrystalline cellulose based tablets.

In the pharmaceutical manufacturing, drug release behavior development is remained as one of the main challenges to improve the drug effectiveness. Recently, more focus has been done on using mesoporous silica materials as drug carriers for prolonged and superior control of drug release in human body. In this study, release behavior of paracetamol is developed using drug-loaded KCC-1-NH2 mesoporous silica, based on direct compaction method for preparation of tablets. The purpose of this study is to investigate the utilizing of pure KCC-1 mesoporous silica (KCC-1) and amino functionalized KCC-1 (KCC-1-NH2) as drug carriers in oral solid dosage formulations compared to common excipient, microcrystalline cellulose (MCC), to improve the control of drug release rate by manipulating surface chemistry of the carrier. Different formulations of KCC-1 and KCC-NH2 are designed to investigate the effect of functionalized mesoporous silica as carrier on drug controlled-release rate. The results displayed the remarkable effect of KCC-1-NH2 on drug controlled-release in comparison with the formulation containing pure KCC-1 and formulation including MCC as reference materials. The pure KCC-1 and KCC-1-NH2 are characterized using different evaluation methods such as FTIR, SEM, TEM and N2 adsorption analysis.

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system.

We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.

Design and optimization of a hybrid process based on hollow-fiber membrane/coagulation for wastewater treatment.

Treatment of textile wastewater using ultrafiltration membranes was carried out in this study. Since membrane fouling is a major operational problem that decreases the membrane separation efficiency, wastewater was treated with polyaluminum chloride (PACl) and alum (aluminum sulfate) as coagulant to decrease the fouling of ultrafiltration membranes. PACl was selected as the best coagulant in the experiments. Also, chitosan was used as coagulant aid upon developing the hybrid process. The obtained optimum dosage of PACl coagulant was 100 mg/L, and maximum turbidity and COD removal of 35% and 66% were attained, respectively. The pretreated wastewater by coagulation was sent to ultrafiltration process for further removal of turbidity and COD. Three ultrafiltration hollow-fiber membranes made of polypropylene (PP), polyvinylidene fluoride (PVDF), and polyethersulfone (PES) were applied in this study. In general, the filtration results were evaluated for two types of samples treated under coagulation and without treatment; the results were unfavorable for the second type. The effects of transmembrane pressure (TMP) and cross velocity on membranes performance were also investigated for process optimization. The obtained results showed that PVDF membrane had the highest flux and turbidity removal, whereas the PES membrane had the highest COD removal. Also, the results revealed that turbidity and COD removal by all membranes were decreased by increasing TMP and cross velocity.

Synthesis, molecular dynamics simulation and adsorption study of different pollutants on functionalized mesosilica.

Experimental and computational works were carried out on a new type of mesoporous silica. In the experimental section, functionalized hollow mesosilica spheres were prepared via a facile technique and then evaluated using some analytical techniques (FESEM, TEM, L-XRD, FTIR, BET-BJH, and TGA). The obtained results revealed that the synthesized material had hollow structure with a diamino-grafted porous shell. The molecular separation of crystal Violet (CV) and neutral Red (NR) dyes from water were investigated by adsorption process using the synthesized powder. Influence of adsorbent loading was evaluated as adsorption ability and dyes removal efficiency. Also, the obtained modeling results revealed appropriate fitting of data with non-linear Langmuir model. The theoretical studies were employed to study the adsorption and removal mechanism of cationic (CV and NR) and anionic (orange II (OII)) dyes using molecular dynamics calculations. Moreover, the simulation outcomes provided valuable information about quantum chemical properties including the HOMO-LUMO maps, chemical reactivity, global softness (σ) and hardness (η) for silica-linker-water-dyes components.