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Modeling of leaching process as a function of operational variables can specify an accurate perspective in the forecasting of valuable metals’ recovery from waste lithium-ion batteries (LIBs). Gene-expression programming algorithm (GEP), an innovative progressed computing strategy, was utilized to estimate the recovery efficiency of manganese from waste LIBs. Numerous tests were performed through different leaching parameters, i.e., the concentration of reagents (Cr: H2SO4 and H2O2), the solid/liquid ratio (ρS/L), temperature (Tr), and experiment time (τr) as inputs and the manganese recovery as the corresponding GEP model output. The capability of the GEP model to anticipate and formulate leaching reaction was compared with response surface modeling (RSM). Results showed that the GEP model with mean R2 and MSE (mean square error) values of 0.982 and 0.01 is more accurate in manganese leaching prediction. The approach proposes a mathematical expression to describe the complicated relationship between the manganese recovery and the reaction parameters accurately. Furthermore, sensitivity analysis revealed that the solid/liquid ratio (ρS/L) and the leaching time were the foremost affecting variables on the manganese recovery.

In this study, a hybrid of support vector regression and a modified tunicate swarm algorithm (SVR-MTSA) strategy is developed to optimize the process parameters for recovery of Pb from the residual of the liberator cells. The lead recovery efficiency in the selected process was strongly nonlinear and depended on several process parameters including temperature, processing time, the content of coke, Na2CO3, and Fe in the precursor. The results confirmed a good agreement between the efficiencies obtained experimentally and those predicted by the model. It is also shown that using the optimal process parameters suggested by the model, achieving a Pb recovery of more than 99% was possible. Sensitivity analysis using the proposed SVR-MTSA model revealed that temperature, coke content, processing time, Na2CO3 amount, and Fe content of the raw material had the most significant effect on the efficiency of the lead recovery, respectively.

In this study, characterization and anticancer activity of synthesized silver and silver chloride nanoparticles (Ag–AgCl NPs) using aqueous fruit extract of Levisticum Officinale were investigated. The first sign of the synthesis of Ag–AgCl NPs was the color change of the solution from yellow to brown due to the addition of plant extract to the silver nitrate (AgNO3) solution, which was confirmed by UV–Vis spectroscopy. The phase and chemical characteristics, size, spherical shape, nanocrystalline structure and stability of Ag–AgCl NPs were evaluated by fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray analysis (EDX), transmission electron microscope (TEM), scanning electron microscopy (SEM), dynamic light scattering (DLS), X-Ray diffraction (XRD) and zeta potential analyzes. The results confirmed the formation of spherical nanoparticles with the face centered cubic (FCC) crystalline structure. The cytotoxic effect of Ag–AgCl on MDA-MB-468 cancer cell line was studied by 3-[4,5 dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) colorimetric method at 48 h. Also, MTT assay results prove promising that the Ag–AgCl NPs could be an agent of breast cancer treatment.

Cu–W nanocomposites had many engineering applications due to the unique characteristics including hardness, transverse rupture strength, electrical conductivity, thermal conductivity, and relative density. This study was an attempt to used gene expression programming as a powerful soft computing technique to model the parameters for the synthesis of Cu–W nanocomposite prepared by spark plasma sintering. First, 97 different reliable experiments were carried out considering the type of Cu and W concentration, temperature, die pressure, and heat rate as input variables. The hardness, transverse rupture modules, electrical conductivity, thermal conductivity, and relative density of nanocomposite defined as output variable separately. An absolute fraction of variance (R2), mean absolute percentage error (MAPE), root relative squared error (RRSE), and mean squared error (MSE) were considered to validate the most appropriate GEP models. Sixfold cross validation was used through testing and training steps of GEP modeling. The results were divided randomly into 68 training sets and 29 testing sets. Finally, the best GEP models were selected for each output parameter. Sensitivity analyses are done to determine the rank of the practical parameters on each investigated properties and revealed that on hardness, transverse rupture modules, electrical conductivity, thermal conductivity, and relative density of nanocomposite, respectively. The results confirmed the ability of GEP for all parameters of Cu–W nanocomposites prepared by spark plasma sintering.

Recently, the preparation of silver nanoparticles (AgNPs) by green technique has improved due to their fundamental applications in medicine. In this study, methadone syrup (ME) was used for the preparation of AgNPs as a reducing and stabilizing agent with the aim of in vitro cytotoxicity effect against the human breast cancer cells. The characteristics of prepared particles are investigated by transmission electron microscopy (TEM), environmental scanning electron microscopy (ESEM), energy-dispersive X-ray spectroscopy (EDS), dynamic light scattering (DLS), UV–visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction studies (XRD). The AgNPs (about 18 nm) were synthesized in a spherical shape and uniform distribution. The mechanism of ME through the synthesis has been proposed based on FT-IR analysis and density functional theory. To investigate the cytotoxicity of prepared AgNPs by ME, MTT assay was used in the range of 0-100 μg/mL. As a function of its dosage, the green synthetic AgNPs showed anti-proliferation activity against MDA-MB-468 cells respect to ME. The results demonstrated the feasibility of producing AgNPs in a simple, rapid, and green manner using ME, which has an important function in inhibiting the growth of breast cancer cells.

Leaching is a complex solid–liquid reaction which has an important influence on the recovery efficiency of the spent lithium-ion batteries (LIBs). Therefore, it is of significant importance to utilize an appropriate technique to predict the effect of operating parameters on the optimized recovery rate. In the present study, a combined method of the artificial neural network (ANN) and particle swarm optimization algorithm (PSO) was used as a model to predict the leaching efficiency of cobalt from spent LIBs. To find the dependency of the leached percentage of cobalt on the operational parameters as model inputs, 42 repeatable numerous experiments are performed using H2SO4 in the presence of H2O2. It was found that the proposed model can be a useful technique in the demonstration of the nonlinear relationship between the leaching efficiency and the process parameters. The performance of PSO–ANN models was validated by statistical thresholds and compared with those of common ANN technique. Moreover, it was found that the pulp density of the leaching solution and the concentration of sulfuric acid were the most important reaction parameters of the spent LIBs recovery, respectively.

This study proposes a novel approach for the prediction of crystallite size of prepared hydroxyapatite (HA) by sol–gel technique using density functional theory (DFT) and neural networks (NN). In this regard, various practical variables, viz., aging time, calcination temperature, calcination time, and drying temperature with three phosphor precursors were used as input, and the crystallite size of prepared HA was used as output for NN model. Firstly, exception of phosphor precursor type, all practical variables were directly used as input to NN model. To input the precursor type of phosphor, the difference between energy levels of interacting orbitals of phosphor precursor and calcium nitrate that were calculated by DFT was used. Such approach provides the possibility of conversion of discrete space between phosphor precursors to continuous space, which enables the NN model to predict the crystallite size of HA even for other types of precursors outside the range of investigated by experimental collected data, e.g., Na2HPO4 as case study. To validate the results of NN model, X-ray diffraction (XRD) and field emission scanning electron microscope (FESEM) were used for characterization of prepared HA by typically out range phosphor precursor, Na2HPO4. The trained NN model showed an overall mean square error (MSE) of 0.2871 with a linear regression factor of 0.9993, and confirmed the prediction ability of the proposed method for prediction of HA crystallite size effectively.

A facile approach is presented to obtain visible-light-active TiO2/CdS nanocomposites with visible-light photocatalytic activity. Ordered nanospheres of TiO2 (with anatase as the main crystalline phase) were synthesized using evaporation-induced self-assembly (EIS) with P123 block polymer without any hazardous additives. Then, a rapid microwave (MW) protocol was used to obtain visible-light-absorbing nanocomposites between TiO2 and CdS in just 10 s. Based on Rietveld refinements of X-ray diffractrograms and elemental analysis it was found that nanoparticles of CdS are mainly decorated between the nanospheres of TiO2, which can simultaneously improve light absorption and suppress the unwanted photo-corrosion effects. The prepared nanocomposites were put to test for the removal of malachite green as a model pollutant from aqueous media under irradiation. The results revealed, that excessive loading results in unwanted surface covering and less than ideal activity, but for the sample with optimized loading, about 86 and 77% of the pollutant was degraded after 100 min of illumination at room temperature under the xenon short-arc and LED illumination, respectively.

Removing of arsenic and antimony from electrolyte of copper electrorefining plant by cooling treatment is the subject of current study. In this regards, the temperature of various electrolyte samples reduce to 5, 10, 15 and 20 °C and hold at different times without any turbulency. Experimental results reveal that decreasing the temperature of the electrolyte, facilitate the deposition of As and Sb in the form of AsO5Sb, AsO4Sb and As2O3 as the white precipitate at a critical time. Also, in the case of the electrolyte retention times exceed than the critical time, the copper content of electrolyte precipitate as blue phase. Typically, it is possible to remove 27 wt.% of Sb and 6 wt.% of As by the cooling of the electrolyte to 5 ᵒC after 8 h. It seems that due to the biocompatibility, the lack of need to the complex technology and its simplicity, the proposed method is a suitable alternative to the common approaches for the removal of antimony and arsenic from industrial electrolyte

Bone char (BC) is one of the most common adsorbent with extensive applications in the removal of pollutions. The adsorption capability of BC is proportional to the crystalline index, i.e, the atomic ratio of Ca/P. This study is an attempt to model the crystalline index of BC that by thermal decomposition of natural bone using artificial neural network (ANN) and genetic expression programming (GEP). In this regard, 100 various experimental data used to construct the ANN and GEP models, separately. Through the data collection step, heating rate, the type of precursor, calcination temperature, and residence time selected as the inputs for the preset output as Ca/P ratio. The results reveal that the minimum amount of Ca/P ratio are at the heating rate 10 °C/min, HNO3 1.6 M as activation agent, calcination temperature 1000 °C, and residence time 2 h. R squared indices is used to compare the performance of extracted models. Finally, the best ANN uses to investigate the effect of each practical variable by sensitivity analysis and revealed that the residence time is the most effective parameter on the crystalline index while acid activation is of secondary importance.

Titanium carbonated hydroxyapatite (Ti/CHA) nanobiocomposites have extensive biological applications due to the excellent biocompatibility and similar characteristics to the bone. Ti/CHA nanobiocomposite has good biological properties but it suffer from diverse characteristics especially in the hardness, Young's modulus, apparent porosity and relative density. This investigation is an attempt to propose the predictive models using gene expression programming (GEP) for the estimation of these characteristics. In this regards, GEP is used to model and compare the effect of practical variables including compact pressure, Ti/CHA ratio and sintering temperature on their investigated properties. To achieve this goal, 90 different reliable experiments were considered to create the GEP models. Selected data set were divided randomly into 63 training sets and 27 testing sets. Finally, 5 of the best models reported for each different output. Sensitivity analyses are done to determine and rank the practical parameters on each investigated properties and revealed that wt.% Ti, wt.% CHA, Compaction pressure (MPa) and Temperature (ᵒC), respectively are the most effective parameters on hardness, Young's modulus, shear modulus, apparent porosity and relative density. By comparing the results, a very good agreement was observed between the experimentals and the results obtained from GEP model.

In this study, the design of experiments is used to study and model the time of passivation in copper electrorefining as a function of the charge of melting furnace through the preparation of copper casting anodes. As a result of optimization for the proposed optimized anodes, the charge percent values of concentrate (Co), refinery scrap (RS), and non-refinery scrap (NRS) were proposed equals to 69.1, 0.574 and 30.32 (wt.%), respectively. Experimental data confirmed the enhanced passivation time of the proposed anode was 6520 s. Also, it was observed that the molar ratio of As/(Bi+Sb) and Ag/(Se+Te) are the key factors in passivation time. Finally, the relation of passivation time (seconds) with the charge of melting furnace is proposed as: t (s)= - 3728.98 × Co + 4640.00 × RS + 3141.00 × NRS + 17763.27 × Co × RS + 25547.65 × Co × NRS - 1758.00 × RS × NRS. Moreover, adding of As ingot in casting anodes as a dose dependent of non-refinery scrap portion in the input charge of the melting unit can effectively prolong the time of passivation.

This study is an attempt to produce surface nanocrystalline composite of Fe-Co-TiO2 at various current densities in the range of 20 to 50 mA/cm2 via pulse electrodeposition method. The prepared composites were characterized by field emission scanning microscope (FESEM), electron dispersive spectrum (EDS), Vickers microhardness, vibrating sample magnetometer (VSM), and x- ray diffraction techniques (XRD). The results showed that the formation of cauliflower morphology was preferred at lower current densities. Moreover, the higher current densities enhanced the Fe content and at the same time diminished the Co and TiO2 contents of prepared surface composites. XRD patterns and Rietveld analysis confirmed the formation of combinations of BCC (as dominant) and FCC phases. Higher current density enhanced the saturation magnetization and decreased lower coercivity due to the higher Fe content and the reduction of TiO2 nanoparticles in coatings. In addition, the lowest coercivity and highest saturation magnetization were gained at 50 mA/cm2, while, the maximum microhardness obtained at 30 mA/cm2.

The carbon nanotubes (CNTs) were produced by mechanical milling of Hexane in the presence of two different catalysts (amorphous and crystalline Fe) at Ar atmosphere. Raman spectroscopy, Fourier-transform infrared spectroscopy, transmission electron microscopy, high-resolution scanning electron microscopy, Brunauer–Emmett–Teller (BET) and thermal analysis were employed to characterize the CNTs. The results showed that the amorphous catalyst induced more structural defects in CNTs respect to the crystalline catalyst. The ratio of single wall to multiwall CNTs of prepared samples were about 0.31 and 7.3 for crystalline and amorphous catalyst, respectively. Moreover, the BET results confirmed the higher adsorption capacity of the sample prepared by amorphous catalyst due to their higher structural defects.

This study presents a new prediction model for estimating the size of AgNPs prepared by green synthesis via gene expression programming (GEP). Firstly, 30 different experiments were used to construct the GEP models. Plant extract, reaction temperature, concentration of AgNO3 and stirring time parameters were considered as input and the size of AgNPs parameter selected as output variables. By consideration of correlation coefficient (R2), mean absolute error (MAE), root relative square error (RRSE) as criteria, the performance of proposed models by GEP were compared each other. Finally, the best model (i.e., GEP-1) with R2=0.9961, MAE=0.2545 and RRSE=0.0668 proposed as a new model with simplified mathematical expressions to estimate the size of AgNPs. The results of sensitivity analysis showed that the amount of plant extract, the concentration of AgNO3, stirring time and reaction temperature are the most effective parameters on the size of AgNPs, respectively. Proposed model via gene expression is satisfactory and can be extended for a wide range of applications. Moreover, the proposed model provides the possibility of preparation of the minimum materials consumption for preparation of the lowest size of AgNPs by consideration of practical or economical constraints.

The kinetic study of valuable metals recovery from waste lithium-ion batteries (LIBs) using the artificial neural network (ANN) was investigated. A multilayer feed-forward artificial neural network with back-propagation learning algorithm was used for kinetic analysis of Co, Mn, Ni, and Li leaching from waste LIBs using H2SO4 in the presence of H2O2. Required data for ANNs learning were generated using various random combinations of kinetic parameters in their extended ranges (i.e., activation energy and Arrhenius equation constant) as well as the most common solid–liquid reaction model. The predictive accuracy of the model was comparable with the correlation coefficient of the model fitting method (R2). Results showed that the model developed in this study can be a useful tool in accurately predicting the kinetics of leaching reactions. The activation energy of Co, Mn, Ni, and Li recovery from waste LIBs using the proposed method calculated to be 40.83, 43.35, 39.06, and 27.30 kJmol−1, respectively, and leaching reaction for metals found to follow the surface chemical reaction model.

In this study, the stress relaxation of amorphous Fe0.7Nb0.1Ti0.1Zr0.1 alloy was investigated by differential thermal analysis (DTA) to enhance the soft magnetic properties. Amorphous Fe0.7Nb0.1Zr0.1T0.1 alloy was produced by mechanical alloying technique using the mixture of pure elements and without adding metalloids. It was found that the stress relaxation process follows the Johnson-Mehl-Avrami model and the mechanism was volume diffusion control with three dimensional growth of the nuclei. The relation of stress relaxation progress (α), with time was proposed as: dα dt = β dα dT = 3.04 exp − 4312 RT 1 − α − ln 1 − α 0.37 . The final achieved microstructure leads to higher saturation magnetization and lower coercivity that are favorable for soft magnetic applications.

In this paper, the crystallization kinetic of amorphous Fe0.7Nb0.1Zr0.1Ti0.1 was studied by differential thermal analysis to produce an appropriate amorphous/nanocrystalline microstructure for improving the soft magnetic properties. Amorphous Fe0.7Nb0.1Zr0.1Ti0.1 alloy without metalloids was prepared by mechanical alloying of pure elements. The results showed that Johnson-Mehl-Avrami model was able to explain the crystallization kinetic of products as: dα dt = β dα dT = 4.86 × 10 9 exp − 186776 RT 1 − α − ln 1 − α 0.24 . Moreover, volume diffusion control with three dimensional growth of the nuclei was the predominant mechanism of crystallization. The simulation results confirmed that by employment of kinetic model, it was possible to control the microstructure as combination of amorphous/crystalline phases and enhance the saturation magnetization from 59 to 142 A·m2/kg and reduce the coercivity from 2.05 to 0.14 kA/m by controlling of crystallization progress at specific predefined α value.

Leaching process is one of the most influential steps during waste lithium-ion batteries (LIBs) recycling. Therefore, the employment of beneficial reaction modeling strategies assists to distinguish and predict the behavior of operational parameters and optimized efficiency. In this study, a gene-expression programming (GEP), i.e., a new evolutionary computing approach, was applied for the prediction of cobalt leaching from waste LIBs using H2SO4 in the presence of H2O2. Several leaching experiments were carried out by consideration of the reagent concentration (Cr), the solid-liquid ratio (S/L), reaction temperature (Tr) and time (τr) as input parameters and leached cobalt percentage as output variable. The GEP-based models were able to predict the leaching of cobalt with a mean standard error (MSE) of less than 0.1 and mean R-square of 0.979. Results affirmed that the proposed model can be a powerful tool in prediction and generation of a mathematical expression for illustration of the relationship between the leaching reaction parameters and the leached percentage. Moreover, the sensitivity analysis showed that the sulfuric acid concentration and S/L ratio were the most influencing parameters on the cobalt leaching from the waste LIBs, respectively.

A new non-isothermal method of kinetic analysis was employed to investigate the thermal decomposition kinetic modeling of the basic carbonate cobalt nanosheets (n-BCoC) synthesized from spent lithium-ion batteries (LIBs). Fraser–Suzuki function was applied to deconvoluting overlapping complex processes from the overall differential thermal curves obtained under the linear heating rate conditions, followed by the kinetic analysis of the discrete processes using a new kinetic analysis method. Results showed that the decomposition of n-BCoC in air occurred through two consecutive reactions in the 136–270 °C temperature intervals. Decomposition started by hydroxide component (Co(OH)2) decomposition until to 65% and simultaneously carbonate phase decarbonation began. The process was continued by CO2 evolution and finally carbonate cobalt nanosheets have been produced. The reaction mechanism of the whole process can be kinetically characterized by two successive reactions: a phase boundary contracting reaction followed by an Avrami–Erofeev equation. Mechanistic information obtained by the kinetic study was in good agreement with FT-IR (Fourier transform infrared spectroscopy) and SEM (scanning electron microscopy) results.

Excellent biocompatibility and its similar biological characteristics to bone apatite, extensively expand the hydroxyapatite (HA) usage in bioimplant applications. The crystallite size of HA is one of the most administrated parameter for determination of reaction rate at the interface of artificial/natural bones. This study tried to propose a new predictive model by employment of the gene expression programming (GEP), i.e., a powerful soft computing technique, to estimate the crystallite size of HA that were prepared by sol–gel route. Firstly, 37 different reliable experiments were carried out considering the type of phosphor precursor, pH, drying temperature, aging time, temperature and time of calcination as practical parameters as input variables, and HA crystallite size as output variable. Absolute fraction of variance (R2), mean absolute percentage error (MAPE), root relative squared error (RRSE), and mean squared error (MSE) were considered to validate the most appropriate GEP model/s. The experiment results were divided randomly into 29 training sets and 8 testing sets. Finally, the best model was selected in R2 = 0.9929, MAPE = 2.8, RRSE = 0.0956, and MSE = 1.7. The results of simulation confirmed the unique features of GEP for the determination of HA crystallite size prepared by sol–gel route.

Biodegradable polymers have become a promising option to prevent the accumulation and replacement of non-destructive petroleum-based plastics. Among all biopolymers, starch films have been considered due to their unique characteristics, especially abundance, cheapness and biocompatibility for packaging industries. Recently, absorbing ultraviolet (UV) rays plays a key role to increase the durability of food and pharmaceutical products. But common organic UV absorbers pose risks to human health and the environment. In this research, date palm syrup (DPS) that is an edible ingredient, has been used to add UV absorption properties to the starch films. UV–Vis spectroscopy showed that these films have good UV absorption and stability in the UV range due to the presence of C O, C C, and O–H functional groups. With the addition of DPS, hydrophilic of these films have been increased. Mechanical properties tests have shown that DPS increases the tensile strength to be about 7.3%, reduces the ductility to be about 26.5%, and also increases the thermal stability of prepared films.

This study is an attempt to improve the surface micro-hardness and corrosion resistance of pure Ti by fabricating in-situ Ni/Ni3Ti-NiTi-Fe2Ti-βCr2Ti composite using TIG process and Inconel 718 filler alloy. Surface coating prepared by depositing of the filler rods on the surface of pure Ti substrate at various heat inputs ranging from 2.750 to 3.125 kJ/cm. Field emission scanning electron microscopy (FESEM) and X-ray diffraction spectroscopy (XRD) were carried out for detailed characterization of the elemental composition, surface morphology and phase analysis. X-ray diffraction via sin2ψ analysis and ASTM E384-HV were used to evaluate the residual stresses at the interface of prepared coating and micro-hardness of prepared samples, respectively. The corrosion behavior of the coatings was also investigated in NaCl 3.5 wt% electrolyte using potentiostat analysis. Results manifested that the Ni3Ti dendritic phase, NiTi needle-like phase, Fe2Ti and β-Cr2Ti rode-like phases mainly formed at the heat inputs ranging from 2.750 and 3 kJ/cm, whereas the Ni2Nb and Ni8Nb phases mainly generated at the higher heat input equal to 3.125 kJ/cm and the creation of micro-crack and micro-segregation was occurred. By increasing in the heat input up to 3 kJ/cm, creating of unreacted Ti particles and increasing in the size of dendrite and rode-like particles have been observed in the fusion zone. All of the prepared samples have much higher hardness than that of the pure Ti (in the range of 2.3–5.3 times higher than Ti). In this condition, based on the polarization and electrochemical impedance spectroscopy (EIS) tests, the prepared sample at the heat input equal to 2.875 kJ/cm has the highest corrosion resistance. The results of residual stresses determination of the optimum sample exhibited the tensile residual stress equal to 215 ± 40 MPa at the centerline of fusion zone.