Chitosan, a biopolymer obtained from chitin, is known for its remarkable adsorption abilities for dyes, drugs, and fats, and its diverse array of antibacterial characteristics. This study explores the extraction and characterization of chitosan from the mycelium of Amanita phalloides. The moisture content, ash content, water binding capacity, fat binding capacity, and degree of deacetylation of the extracted chitosan were determined. The chitosan exhibited a high yield of 70%, crystallinity of 49.07%, a degree of deacetylation of 86%, and potent antimicrobial properties against both Gram-negative and Gram-positive bacteria. The study also examined the adsorption capabilities of chitosan to remove methylene blue (MB) dye by analysing specific factors like pH, reaction time, and MB concentration using the response surface model. The highest degree of MB dye removal was 91.6% at a pH of 6, a reaction time of around 60 min and an initial dye concentration of 16 ppm. This experimental design can be applied for chitosan adsorption of other organic compounds such as dyes, proteins, drugs, and fats.

This article presents a one-step ultrasonication technique for generating biomass carbon dots (BCDs) from neem bark (Azadirachta indica) powder. The BCDs were characterized using modern techniques such as UV-Vis, FTIR, Raman, XRD, HRTEM, FESEM, EDAX, and Zeta potential analyses. Unlike traditional nanocomposite bed systems, this study utilized BCDs as a liquid-phase adsorbent for the regenerative adsorption of the environmentally harmful dye, methylene blue (MB), through an in-situ precipitation reaction. This involved the formation of BCDs-MB adduct via an electrostatic mechanism. The adsorption capacity and percentage of removal were remarkable at 605 mg g-1 and 64.7% respectively, exceeding various solid-based adsorption methods in the literature. The Langmuir isotherm and pseudo-second-order kinetics model provided an excellent fit for this system. The calculated thermodynamic parameter, Gibbs free energy change (ΔG) was negative, indicating a spontaneous, exothermic, and physisorption-based mechanism. The regenerative capacity of our system was further demonstrated by successfully extracting and recovering the MB dye (64%) using ethyl alcohol as the solvent. This method provides an efficient means of recovering valuable cationic organic dye compounds from contaminated environments.

This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H2O2), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R2 values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H2O2/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent.