In this review, various methods of preparation of activated carbon from agricultural and commercial waste material are reviewed. In addition, we also discuss various activation treatments using a comparative approach. The data are organised in tabulated form for ease of comparative study. A review of numerous characterisation techniques is also provided. The effect of time and temperature, activation conditions, carbonisation conditions and impregnation ratios are explained and several physical and chemical activation treatments of raw materials and their impact on the micro- and mesoporous volumes and surface area are discussed. Lastly, a review of adsorption mechanisms of activated carbon (AC) is also provided.

Conversion of nitroaniline (NA), a highly toxic pollutant that has been released into aquatic systems due to unmanaged industrial development in recent years, into the less harmful or a useful counterpart is the need of the hour. Various methods for its conversion and removal have been explored. Owing to its nominal features of advanced effectiveness, the chemical reduction of 4-NA using various different nanocatalytic systems is one such approach that has attracted tremendous interest over the past few years. The academic literature has been confined to case studies involving silver (Ag) and gold (Au) nanoparticles, as these are the two most widely used materials for the synthesis of nanocatalytic assemblies. Focus has also been given to sodium borohydride (NaBH₄), which is used as a reductant during the chemical reduction of NA. This systematic review summarizes the fundamentals associated with the catalytic degradation of 4-NA, and presents a comprehensive and critical study of the latest modifications used in the synthesis of these catalytic systems. In addition, the kinetics, mechanisms, thermodynamics, as well as the future directions required for understanding this model reaction, have been provided in this particular study.

Numerous harmful phenolic contaminants are discharged into water that pose a serious threat to environment where two of the most important purification methodologies for the mitigation of phenolic contaminants are adsorption and photocatalysis. Besides cost, each process has drawbacks in terms of productivity, environmental impact, sludge creation, and the development of harmful by-products. To overcome these limitations, the modeling and optimization of water treatment methods is required. Artificial Intelligence (AI) is employed for the interpretation of treatment-based processes due to powerful learning, simplicity, high estimation accuracy, effectiveness, and improvement of process efficiency where artificial neural networks (ANNs) are most frequently employed for predicting and analyzing the efficiency of processes applied for the mitigation of these phenolic contaminants from water. ANNs are superior to conventional linear regression models because the latter are incapable of dealing with non-linear systems. ANNs can also reduce the operational cost of treating phenol-contaminated water. A correlation coefficient of >0.99 can be achieved using ANN with enhanced phenol mitigation percentage accuracy generally ranging from 80 % to 99.99 %. Using ANN optimization, the maximum phenol mitigation efficiencies achieved were 99.99 % for phenol, 99.93 % for bisphenol A, 99.6 % for nonylphenol, 97.1 % for 2-nitrophenol, 96.6 % for 4-chlorophenol and 90 % for 2,6-dichlorophenol. In numerous ANN models, Levenberg-Marquardt backpropagation algorithm for training was employed using MATLAB software. This study overviews their employment and application for optimization and modeling of removal processes and explicitly discusses the important input and output parameters necessary for better performance of the system. The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. Furthermore, several challenges and future prospects have also been discussed.