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.

Herein, a novel biosorbent was successfully fabricated through a two-step process employing Pinus massoniana pollen as raw material. The efficacy of this biosorbent in eliminating methylene blue (MB), a typical organic cationic dye, from highly concentrated industrial wastewater was investigated. The results demonstrated that by adjusting the wettability of pollen microcarriers, it is possible to significantly increase their adsorption capacity for cationic dyes, resulting in a remarkable 25-fold improvement. The modified Pinus massoniana pollen microcarriers (MPPMC) exhibited an optimal adsorption capacity (585 mg/g) under specific conditions and a rapid equilibrium (97.6% in 5 min, uptake 487.8 mg/g) even at room temperature, showing excellent performance in removing MB efficiently and quickly. It is worth noting that the modified microcarriers could be regenerated via a simple pH-controlled adsorption-desorption cycle, maintaining their superior efficiency (> 99%) even after undergoing five cycles, indicating their excellent reproducibility. The MB adsorption process on MPPMC obeyed the pseudo-second-order kinetic model and followed the Langmuir model. Through the introduced modifications, the substantial deprotonation of carboxyl groups notably augmented electrostatic and hydrogen bonding interactions between MPPMC and MB. Overall, this study offers a sustainable, eco-friendly biological adsorbent, and the MPPMC exhibit the considerable potential for efficient and rapid removal of organic cationic dyes in wastewater.

The present work reports the production of cellulose nanocrystals, CNC30 and CNC60, developed using vegetable waste, i.e., bottle gourd peel through sulfuric acid hydrolysis with a 30 and 60 min hydrolysis process coupled with ultrasonication. The FTIR confirmed the absence of hemicellulose and lignin, and XRD confirmed the crystallinity of the cellulose nanocrystals. DLS studies indicated the hydrodynamic diameter of CNC30 and CNC60 to be 195.5 nm and 192.2 nm, respectively. The TEM image and SAED pattern established the shape of CNC60 to be spherical, with an average particle size of 38.32 nm. CNC60 possessed lesser negative potential and higher thermal stability than CNC30, possibly due to the demolition of the crystalline regions containing sulfate groups. The functional properties, such as swelling power, water, and oil holding capacities of CNC60, were superior to that of CNC30. The adsorption batch parameters yielded 95.68 % methylene dye removal by CNC60 against the predicted value of 96.16 % by the RSM-PSO hybrid approach. The analyses of adsorption isotherms, kinetics, and thermodynamic parameters revealed the nature of the adsorbed layer and adsorption mechanism. Overall observations recommend that CNC60 could be a good and potent functional agent in paper technology, food technology, water treatment, and biomedical applications.

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.