Technological trends in nanosilica synthesis and utilization in advanced treatment of water and wastewater.

Water and wastewater treatment applications stand to benefit immensely from the design and development of new materials based on silica nanoparticles and their derivatives. Nanosilica possesses unique properties, including low toxicity, chemical inertness, and excellent biocompatibility, and can be developed from a variety of sustainable precursor materials. Herein, we provide an account of the recent advances in the synthesis and utilization of nanosilica for wastewater treatment. This review covers key physicochemical aspects of several nanosilica materials and a variety of nanotechnology-enabled wastewater treatment techniques such as adsorption, separation membranes, and antimicrobial applications. It also discusses the prospective design and tuning options for nanosilica production, such as size control, morphological tuning, and surface functionalization. Informative discussions on nanosilica production from agricultural wastes have been offered, with a focus on the synthesis methodologies and pretreatment requirements for biomass precursors. The characterization of the different physicochemical features of nanosilica materials using critical surface analysis methods is discussed. Bio-hybrid nanosilica materials have also been highlighted to emphasize the critical relevance of environmental sustainability in wastewater treatment. To guarantee the thoroughness of the review, insights into nanosilica regeneration and reuse are provided. Overall, it is envisaged that this work's insights and views will inspire unique and efficient nanosilica material design and development with robust properties for water and wastewater treatment applications.

Intelligent modeling of dye removal by aluminized activated carbon.

Methylene blue (MB) is an important compound in textile and wood processing industries as well as in medical research for combating malaria parasites. Despite these versatilities, direct contact with human beings results in adverse health challenges, and contamination of water bodies affects aquatic biotas. Hence, it is important to treat MB-contaminated wastewaters before disposal into water bodies. Adsorption, which depends on some parameters, proves to be an easy, cheap, and efficient technique to remove pollutants in wastewater. However, investigating these parameters experimentally is a laborious, expensive, and time-consuming process whose efficiency is limited by the conditions imposed on the experiments. Herein, we developed polynomial multiple linear regression (MLR) and the three other machine learning models to study the interplay of five adsorption parameters (descriptors) and their effects on the removal of methylene blue from water using aluminized activated carbon (Al-AC). The optimized machine learning models, that is random forest (R = 0.9905), support vector regression (R = 0.9946), and multilayer perceptron (R = 0.9993), outperformed the best MLR model (R = 0.9845) by small margins. High statistical R and low error values are not enough to satisfactorily classify a model. Hence, the generalizability of the models was further determined under different experimental conditions, and the order of predictive accuracy of the models was established as ANN > SVR > RF > 2-degree MLR. Aluminum loading, adsorbent dosage, and initial adsorbate concentration are the most important factors affecting MB removal. The removal efficiency, which could reach 99.9% at optimum conditions, does not depend on the temperature thus eliminating the need to install temperature control apparatus for practical setup.