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Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q e  = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The presence of pharmaceuticals as the emerging contaminates needs novel approaches and new materials to be remediated. This study aimed to develop and apply MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan nanocomposite (MWCNTs/CS-PVA/GA NC) for removal of tetracycline (TC) as a model of antibiotics from aqueous solutions. The successful synthesis of NC was supported by techniques of SEM, XRD, TGA, FTIR, and EDX. The prepared NC was then utilized for TC adsorption under the main effective parameters of TC concentration (25-125 mg/L), sonication time (0-8 min), NC dose (1-130 mg), and tempearure (5-45 °C). The process behavior was comparably explored with different methods of central composite design (CCD), artificial neural networks (ANN), and general regression neural network (GRNN). The results showed that under the optimum settings presented by desirability function (DA), in which the respective values for the factors were 125 mg/L, 6.8 min, 130 mg, and 45 °C, the efficiency and adsorption capacity of NC is supposed to be 99.07% and ∼525 mg/g, respectively. From the models studied, although all were able to express the process with satisfactory accuracy, ANN provided the best accuracy and reliability owning to the highest R 2 (0.999) and lowest RMSE, ADD, MAE. The kinetics, isotherms, and thermodynamic studies showed that the process is fast (over 4.5 min), chemisorption, heterogeneous with multilayer nature, spontaneous, feasible, and endothermic. In addition, the as prepared NC could be recycled for five times without significant fail in its performance. All in all, the developed MWCNTs/CS-PVA/GA NC can be considered as a promising candidate in dealing with aqueous solutions' pollution with antibiotic., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper reports a very high capacity and recyclable Mg-Co-Al-layered double hydroxide@ g-C 3 N 4 nanocomposite as the new adsorbent for remediation of radioisotope-containing medical-based solutions. In this work, a convenient solvothermal method was employed to synthesize a new nano-adsorbent, whose features were determined by energy dispersive X-ray (EDS/EDX), XRD, FESEM, TEM, TGA, BET, and FT-IR spectroscopy. The as-prepared nano-adsorbent was applied to capture the radioisotope iodine-131 mainly from the medical-based wastewater under different conditions of main influential parameters, (i.e. adsorbent dose, initial I 2 concentration, sonication time, and temperature). The process was evaluated by three models of RSM, CCD-ANFIS, and CCD-GRNN. Furthermore, comprehensive kinetic, isotherm, thermodynamic, reusability cycles and optimization (by GA and DF) studies were conducted to evaluate the behavior and adsorption mechanism of I 2 on the surface of Mg-Co-Al-LDH@ g-C 3 N 4 nanocomposite. High removal efficiency (95.25%) of 131 I in only 30 min (i.e. during 1/384 its half-life), along with an excellent capacity that has ever been reported (2200.70 mg/g) and recyclability (seven times without breakthrough in the efficiency), turns the nanocomposite to a very promising option in remediation of 131 I-containing solutions. Besides, from the models studied, ANFIS described the process with the highest accuracy and reliability with R 2 > 0.999., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In this research paper, response surface methodology (RSM), generalized regression neural network (GRNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to develop prediction models for Triclosan (TCS) removal by a novel inclusion complex (host-guest complex). Hence, β-cyclodextrin (β-CD) and poly(ethylene glycol) (PEG) host-guest complex loaded on the multi walled carbon nanotube (MWCNT/PEG/β-CD) was prepared and characterized by Raman, NMR, TGA, XRD, SEM, TEM, and point of zero charge (pH pzc ) technique. The effects of MWCNT/PEG/β-CD dose (g), temperature (°C), antibiotic concentration (mg L -1 ), and sonication time (min), each at five levels were investigated as independent factors. Central composite design (CCD) of RSM setup was applied in combination with ANFIS and GRNN training dataset for evaluation purposes. Moreover, the kinetic, isotherm equilibrium, and thermodynamic parameters of adsorption of TCS on MWNT-PEG/β-CD nanocomposite was examined. To assess the accuracy of results, several statistics such as R 2 , RMSE (root mean square error), mean squared error (MSE), MAE (mean absolute error), sum of the absolute error (SAE), %AAD (absolute average deviation), average relative error (ARE), hybrid fractional error function (HYBRID), Marquart's percentage standard deviation (MPSD), and Pearson's Chi-square measure (χ) were checked. The results of ANFIS approach were found to be more trustworthy than GRNN model since better statistical analysis were attained. However, it was known that the GRNN is easier and take a little time for modeling than the ANFIS approach., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The present study devoted to description of efficient removal of radioactive UO 2 2+ ions (U(IV)) via complexation with Arsenazo III (ARS III) accelerated by ultrasound-assisted adsorption onto the Au-NPs supported on carbon nanotubes (Au-NPs-CNTs), which were characterized by conventional techniques such as FESEM, EDS and XRD. Central composite design (CCD) employed to model contribution of parameters viz. pH (2.5-8.5), adsorbent mass (5-25mg), UO 2 2+ concentration (5-25mgL -1 ) and sonication time (1-5min) onto response. The predicted results optimum conditions corresponding achievement of maximum UO 2 2+ removal efficiency are pH 5.5, 20mg of Au-NPs-CNTs, is highly applicability for removal of more than 98% of 25mgL -1 of UO 2 2+ following 5min sonication. Through analysis of corresponding results based on evaluation according to UO 2 2+ concentration were found significantly affect responds. ANOVA analysis revealed a high R 2 (0.9950) & AP (51.79) and low SD (0.6078) & CV% (0.6703) values of regression model equation which completely ensure accuracy of the quadratic model. Langmuir isotherm model was applicable for description of adsorption data with maximum monolayer adsorption capacity of 133.3mgg -1 at 25°C and pH 5.5. Dubinin-Radushkevich (D-R) isotherm model based on mean sorption energy (E) reveal high contribution of physisorption (1.17-3.78kJmol -1 ) on adsorption process. Moreover, Pseudo-second-order kinetic model delivered a better correlation for the experimental data in comparison to the pseudo-first-order kinetic model and intraparticle diffusion mechanism., (Copyright © 2017 Elsevier Inc. All rights reserved.)

By combining the advantage of multi-walled carbon nanotubes (MWCNTs) and interpolymer complexes, we synthesis a new carrier system consist of poly(acrylic acid)/poly(ethylene glycol)/carbon nanotube (PAA/PEG/CNT). Then, Methotrexate (MTX) and Cyclophosphamide (CPP) were loaded on PAA/PEG/CNT, and the physicochemical properties of nanoparticles characterized by Infrared spectroscopy (IR), Scanning Electron Microscopy (SEM), Thermo Gravimetric Analysis (TGA) and Nuclear Magnetic Resonance (NMR). In the second part after efficiency determination of loaded drugs, in vitro drug release study was examined with ultra violet spectroscopy (UV) in pH=7.4 buffer (human body range) and pH=4 buffer (pH of cancer cells), and fever temperature drug release kinetic was studied by different mathematical models., (Copyright © 2017 Elsevier B.V. All rights reserved.)

S-doped and Cu- and Co-doped TiO 2 was synthesized by a sol-gel method and characterized by FE-SEM, XRD, EDX and FTIR. The Co/Cu/S-TiO 2 nanocomposite loaded on the activated carbon as new nanoadsorbent was used for simultaneous removal of methylene blue (MB) and sunset yellow (SY) from aqueous solution by ultrasonic-assisted adsorption method. In this work, central composite design (CCD) and adaptive neuro-fuzzy inference system (ANFIS) as a support tool for examining data and making prediction are used to recognize and predict the removal percentage in MB and SY dye solution of different concentrations. The predictive capabilities of CCD and ANFIS are compared in terms of square correlation coefficient (R 2 ), root mean square error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD) against the empirical data. It is found that the ANFIS model shows the better prediction accuracy than the CCD model. In addition to, the optimization of ultrasound-assisted simultaneous removal of methylene blue (MB) and sunset yellow (SY) on the Co/Cu/S-TiO 2 /AC nanocomposite by response surface methodology (RSM) for the optimization of the process variables, such as MB and SY concentrations, Co/Cu/S-TiO 2 /AC nanocomposite dose and sonication time, was investigated. Various isotherm and kinetic models were used in the experimental data. The results revealed that the langmuir isotherm and pseudo-second-order model had a better correlation than the other models., (Copyright © 2017 Elsevier B.V. All rights reserved.)