Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

A review on carbon-based biowaste and organic polymer materials for sustainable treatment of sulfonamides from pharmaceutical wastewater

Frequent detection of sulfonamides (SAs) pharmaceuticals in wastewater has necessitated the discovery of suitable technology for their sustainable remediation. Adsorption has been widely investigated due to its effectiveness, simplicity, and availability of various adsorbent materials from natural and artificial sources. This review highlighted the potentials of carbon-based adsorbents derived from agricultural wastes such as lignocellulose, biochar, activated carbon, carbon nanotubes graphene materials as well as organic polymers such as chitosan, molecularly imprinted polymers, metal, and covalent frameworks for SAs removal from wastewater. The promising features of these materials including higher porosity, rich carbon-content, robustness, good stability as well as ease of modification have been emphasized. Thus, the materials have demonstrated excellent performance towards the SAs removal, attributed to their porous nature that provided sufficient active sites for the adsorption of SAs molecules. The modification of physico-chemical features of the materials have been discussed as efficient means for enhancing their adsorption and reusable performance. The article also proposed various interactive mechanisms for the SAs adsorption. Lastly, the prospects and challenges have been highlighted to expand the knowledge gap on the application of the materials for the sustainable removal of the SAs.

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, -0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

Adsorption of Phenoxyacetic Herbicides from Water on Carbonaceous and Non-Carbonaceous Adsorbents

The increasing consumption of phenoxyacetic acid-derived herbicides is becoming a major public health and environmental concern, posing a serious challenge to existing conventional water treatment systems. Among the various physicochemical and biological purification processes, adsorption is considered one of the most efficient and popular techniques due to its high removal efficiency, ease of operation, and cost effectiveness. This review article provides extensive literature information on the adsorption of phenoxyacetic herbicides by various adsorbents. The purpose of this article is to organize the scattered information on the currently used adsorbents for herbicide removal from the water, such as activated carbons, carbon and silica adsorbents, metal oxides, and numerous natural and industrial waste materials known as low-cost adsorbents. The adsorption capacity of these adsorbents was compared for the two most popular phenoxyacetic herbicides, 2,4-dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA). The application of various kinetic models and adsorption isotherms in describing the removal of these herbicides by the adsorbents was also presented and discussed. At the beginning of this review paper, the most important information on phenoxyacetic herbicides has been collected, including their classification, physicochemical properties, and occurrence in the environment.

Application of a novel hybrid MIL-53(Al)@rice husk for the adsorption of glyphosate in water: Characteristics and mechanism of the process

The development of new materials that have a high capacity to remove pollutants in water-based media is becoming increasingly important because of the serious contamination of water and the negative impact on biodiversity and public health. The presence of glyphosate in water, the most widely used herbicide worldwide, has triggered alerts owing to the collateral effects it may cause on human health. The main objective of the present study was to investigate the potential of the hybrid material MIL-53(Al)@RH for the adsorption of glyphosate in aqueous solution. The material was obtained following the methodology of MIL-53(Al) synthesis in the presence of hydrolyzed rice husk assisted by microwave. Batch adsorption experiments were carried out to evaluate the adsorbent dosage, pH₀ solution effect, contact time, adsorbate concentration, and temperature effect. The results demonstrated that a maximum adsorption capacity of 296.95 mg g^-1, at pH₀ 4 with a ratio of 0.04 g MIL-53(Al)@RH/50 mL of solution, was achieved in 30 min. The Avrami and pseudo-second order models appropriately described the adsorption kinetics and the equilibrium by Langmuir and Sips models. The enthalpy changes (ΔH°) determined propose an endothermic reaction governed by chemisorption, corroborating the kinetic and equilibrium settings. Hydrogen bonds, π*-π interactions, and complexation between the metal centers of MIL-53(Al) and the anionic groups of glyphosate were postulated to be involved as adsorption mechanisms. Finally, for practical application, MIL-53(Al)@RH was packed in a column for a fixed-bed test which revealed that the hybrid can remove glyphosate with an adsorption capacity of 76.304 mg L^-1, utilizing 90% of the bed.

Facile synthesis of CoFe2O4@MIL-53(Al) nanocomposite for fast dye removal: Adsorption models, optimization and recyclability

The global occurrence of textile dyes pollution has recently emerged, posing a serious threat to ecological systems. To abate dye contamination, we here developed a novel magnetic porous CoFe₂O₄@MIL-53(Al) nanocomposite by incorporating magnetic CoFe₂O₄ nanoparticles with MIL-53(Al) metal-organic framework. This nanocomposite possessed a surface area of 197.144 m² g^-1 and a pore volume of 0.413 cm³ g^-1. The effect of contact time (5-120 min), concentration (5-50 mg L^-1), dosage (0.1-1.0 g L^-1), and pH (2-10) on Congo red adsorption was clarified. CoFe₂O₄@MIL-53(Al) could remove 95.85% of Cong red dye from water with an accelerated kinetic rate of 0.6544 min^-1 within 10 min. The kinetic and isotherm models showed the predominance of Bangham and Temkin. According to Langmuir, the maximum uptake capacities of CoFe₂O₄@MIL-53(Al), CoFe₂O₄, and MIL-53(Al) adsorbents were 43.768, 17.982, and 15.295 mg g^-1, respectively. CoFe₂O₄@MIL-53(Al) was selected to optimize Cong red treatment using Box-Behnken experimental design. The outcomes showed that CoFe₂O₄@MIL-53(Al) achieved the highest experimental uptake capacity of 35.919 mg g^-1 at concentration (29.966 mg L^-1), time (14.926 min), and dosage (0.486 g L^-1). CoFe₂O₄@MIL-53(Al) could treat dye mixture (methylene blue, methyl orange, Congo red, malachite green, and crystal violet) with an outstanding removal efficiency of 81.24% for 30 min, and could be reused up to five cycles. Therefore, novel recyclable and stable CoFe₂O₄@MIL-53(Al) is recommended to integrate well with real dye treatments systems.

Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugar-extracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 ± 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (R_ANN: 0.99219) and low mean squared error (MSE_ANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R²_ANN: 0.9844, R²_RSM: 0.9721) and low MSE value (MSE_ANN: 3.7002, MSE_RSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R² = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal, and the adsorption process can be modelled and predicted efficiently using ANN.

Experimental and Modeling of Dicamba Adsorption in Aqueous Medium Using MIL-101(Cr) Metal-Organic Framework

Drift deposition of emerging and carcinogenic contaminant dicamba (3,6-dichloro-2-methoxy benzoic acid) has become a major health and environmental concern. Effective removal of dicamba in aqueous medium becomes imperative. This study investigates the adsorption of a promising adsorbent, MIL-101(Cr) metal-organic framework (MOF), for the removal of dicamba in aqueous solution. The adsorbent was hydrothermally synthesized and characterized using N2 adsorption-desorption isotherms, Brunauer, Emmett and Teller (BET), powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and field emission scanning electron microscopy (FESEM). Adsorption models such as kinetics, isotherms and thermodynamics were studied to understand details of the adsorption process. The significance and optimization of the data matrix, as well as the multivariate interaction of the adsorption parameters, were determined using response surface methodology (RSM). RSM and artificial neural network (ANN) were used to predict the adsorption capacity. In each of the experimental adsorption conditions used, the ANN gave a better prediction with minimal error than the RSM model. The MIL-101(Cr) adsorbent was recycled six times to determine the possibility of reuse. The results show that MIL-101(Cr) is a very promising adsorbent, in particular due to the high surface area (1439 m2 g−1), rapid equilibration (~25 min), high adsorption capacity (237.384 mg g−1) and high removal efficiency of 99.432%.