Rapid and efficient ultrasonic assisted adsorption of PNP onto LDH-GO-CNTs: ANFIS, GRNN and RSM modeling, optimization, isotherm, kinetic, and thermodynamic study

Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass

The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (-20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.

Efficient adsorption of Cr (VI) onto hematite nanoparticles: ANN, ANFIS modelling, isotherm, kinetic, thermodynamic studies and mechanistic insights

Hematite nanoparticles (AF-Fe2O3NPs) were prepared through a simple method utilizing Acacia falcata leaf extract in this investigation. The nanoparticles were extensively characterized to understand their specific properties. FESEM images revealed agglomerated surface morphology, while EDS confirmed the existence of elemental components, including Fe, O, and C. The mesoporous nature of AF-Fe2O3NPs with a pore diameter of 3.77 nm was determined through BET studies. XRD analysis indicated the crystallinity, with lattice parameters characteristic of hematite nanoparticles (a = 0.504 nm and c = 1.381 nm). Superparamagnetic property of the AF-Fe2O3NPs was affirmed from the saturation magnetization (2.98 emu/g) without any hysteresis. Subsequently, AF-Fe2O3NPs were used as adsorbent for the removal of Cr (VI) from aqueous solution. The experimental data were subjected to machine learning (ML) models, specifically ANN and ANFIS, to predict Cr (VI) removal. Both ML models exhibited excellent predictive capabilities, with high R2 values (>0.99) and low error indices such as MSE, RMSE, and MAE. Furthermore, comprehensive kinetic, isotherm, and thermodynamic studies were conducted to gain insights into the behavior and sorption mechanisms of Cr (VI). The Hill model, a statistical physics model, demonstrated an outstanding fit compared to conventional isotherms. It revealed a saturation adsorption potential of 12.91 mg/g at pH 2, 1.5 g/L dose, and a temperature of 30 °C, corroborating physisorption as the dominant mechanism. XPS results confirmed Cr (VI) reduction to Cr (III) through the appearance of specific peaks at 577.18 and 587.08 eV. Thermodynamic investigations established the endothermicity and spontaneity of the adsorption. In summary, the hematite nanoparticles synthesized in this study exhibit promising potential to remove Cr (VI) from aqueous streams, making them a viable option for water treatment applications.

Chitosan-minerals-based composites for adsorption of caesium, cobalt and europium

Currently, there is a growing interest in the use of natural materials in various fields of science, technology and environmental protection due to their availability, low-cost, non-toxicity and biodegradability. Chitosan, natural clay of local origin, montmorillonite, zeolite, cross-linking agents (epichlorohydrin, sodium tripolyphosphate, glutaraldehyde) and plasticisers (glycerol) were used to synthesise composites. The composites were characterised by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction analysis (XRD) and scanning electron microscope (SEM), tested for their antibacterial activity and used in batch experiments to study the adsorption of caesium, cobalt and europium ions. The maximum capacities for adsorption of caesium, cobalt and europium on the composites were 1400 mg/g, 900 mg/g and 18 mg/g, respectively. The experimental data fit better the Langmuir isotherm model and indicate favourable monolayer adsorption of Cs+, Co2+ and Eu3+ at homogeneous sites of the composites. The experimental data were in better agreement with the pseudo-second-order non-linear kinetic model for most elements and adsorbents. Adaptive neuro-fuzzy inference system proved to be a practical tool with good performance and generalisation capability for predicting the adsorption capacity of composites for caesium, cobalt, and europium ions. It was found that the predicted data were very close to the experimental data.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

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.

Preparation of Graphene Oxide-Maghemite-Chitosan Composites for the Adsorption of Europium Ions from Aqueous Solutions

The adsorption of Eu(III) on composites synthesised from graphene oxide (GO), maghemite (MGH), and chitosan (CS) has been studied using different approaches. The physicochemical and morphological characteristics of the composites GO-MGH, GO-CS, GO-MGH-CS I, II, and III were determined by XRD, Mössbauer spectroscopy, FTIR, Raman spectroscopy, and TEM. According to the results of batch experiments, the maximum experimental adsorption capacity was 52, 54, 25, 103, and 102 mg/g for GO-MGH, GO-CS, GO-MGH-CS I, II, and III, respectively. The data obtained are in better agreement with the Langmuir, pseudo-second-order, and pseudo-first-order models only for GO-MGH. Thus, the adsorption of Eu(III) on the composites was a favourable, monolayer, and occurred at homogeneous sites. The nature of adsorption is chemical and, in the case of GO-MGH, physical. Tests of the composites in natural waters showed a high removal efficiency for Eu(III), Pu(IV), and Am(III), ranging from 74 to 100%. The ANFIS model has quite good predictive ability, as shown by the values for R², MSE, SSE, and ARE. The GO-MGH-CS composites with the high adsorption capacity could be promising candidates for the removal of Eu(III) and the pre-concentration of Pu(IV) and Am(III) from natural waters.

Adsorption of caesium and cobalt ions on the muscovite mica clay-graphene oxide-γ-Fe2O3-Fe3O4 composite

The muscovite mica clay-graphene oxide-maghemite-magnetite (γ-Fe₂O₃-Fe₃O₄) composite was first used for the adsorption of caesium(I) and cobalt(II). The presence of clay minerals, graphene oxide, maghemite, and magnetite was detected in the prepared composite by XRD, WD-XRF, Mössbauer spectroscopy, and ATR-FTIR. The SEM and TEM results show that the composite has a layered structure with irregularly shaped pores on the surface. It was found that the adsorption of ions depends on the initial concentration, pH (except for caesium), mass of adsorbent, temperature, and contact time. The maximum adsorption capacity for Cs(I) and Co(II) was 2286 mg/g and 652 mg/g, respectively, and was obtained at concentrations (Cs(I) = 12,630 mg/L; Co(II) = 3200 mg/L), adsorbent mass of 0.01 g, pH (Cs(I) = 7; Co(II) = 5), temperature of 20 ± 1 °C, and contact time of 24 h. The high adsorption capacity of the composite could be due to a diversity of functional groups, a large number of active sites or the multilayer adsorption of caesium and cobalt ions on the surface of the composite. The Freundlich, Langmuir isotherms, and the pseudo-second-order kinetic model better describe the adsorption of these ions on the composite. The adsorption was non-spontaneous endothermic for Cs(I) and spontaneous endothermic for Co(II). The proposed mechanism of adsorption of Cs and Co ions on the composite is complex and involves electrostatic interactions and ion exchange. The ANFIS model proved to be quite effective in predicting the adsorption of Cs(I) and Co(II), as shown by the obtained values of R², MSE, SSE, and ARE.

Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS), were applied for modeling and predicting turbidity removal from water using graphene oxide (GO). Based on partial mutual information (PIM) algorithm, pH, GO dosage, and initial turbidity were selected as the input variables for developing the models. The prediction performance of the AI-based models was compared with each other and with the response surface methodology (RSM) model, previously reported by the authors, as well. The models' estimation accuracy was assessed through statistical measures, including mean-squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Among the evaluated models, ANN had the highest estimation accuracy as it showed the highest R² for the validation data (0.949) and the lowest MSE, RMSE, and MAE values. Furthermore, ANN predicted 76.1% of data points with relative errors (RE) less than 10%. In contrast, the weakest prediction performance belonged to the SVR model with the lowest R² for both calibration (0.712) and validation (0.864) data. Besides, only 57.1% of the SVR's predictions were characterized by RE < 10%. The ANFIS and RSM models exhibited a more or less similar performance in terms of R² for the validation data (0.877 and 0.871, respectively) and other statistical parameters. According to the results, the ANN technique is proposed as the best option for modeling the process. Nevertheless, as the RSM technique provides valuable information about the contribution of the independent operational parameters and their complex interaction effects using the least number of experiments, simulating the process by this technique before modeling by ANN is inevitable.

Tetracycline capture from aqueous solutions by nanocomposite of MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan

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 R² (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.