A sustainable remediation of Congo red dye using magnetic carbon nanodots and B. pseudomycoides MH229766 composite: mechanistic insight and column modelling studies

Enhanced adsorption of Bismark Brown R dye by chitosan conjugated magnetic pectin loaded filter mud: A comprehensive study on modeling and mechanisms

Herein, a polymer-based bioadsorbent was prepared by cross-linking chitosan to filter mud and magnetic pectin (Ch-mPC@FM) for the removal of Bismark Brown R dye (BB-R) from wastewater. Morphological characterization analysis indicated that Ch-mPC@FM had a higher surface area and better pore structure than its components. The Artificial Neuron Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were employed to evaluate the simulation and prediction of the adsorption process based on input variables like temperature, pH, dosage, initial BB-R dye concentration, and contact time. ANFIS and ANN demonstrated significant modeling and predictive accuracy, with R2 > 0.93 and R2 > 0.96, and root mean square error < 0.023 and <0.020, respectively. The Langmuir isotherm and the pseudo-second-order kinetic models provided the best fits to the equilibrium and kinetic data. The thermodynamic assessment showed spontaneous and endothermic adsorption with average entropy and enthalpy changes of 119.32 kJ mol-1 K and 403.47 kJ mol-1, respectively. The study of BB-R dye adsorption on Ch-mPC@FM revealed multiple mechanisms, including electrostatic, complexation, pore filling, cation-π interaction, hydrogen bonding, and π-π interactions. The approximate production cost of US$ 5.809 Kg-1 and excellent adsorption capability render Ch-mPC@FM an inexpensive, pragmatic, and ecologically safe bioadsorbent for BB-R dye removal from wastewater.

Integrating biosorption and machine learning for efficient remazol red removal by algae-bacteria co-culture and comparative analysis of predicted models

This research investigates into the efficacy of algae and algae-bacteria symbiosis (ABS) in efficiently decolorizing Remazol Red 5B, a prevalent dye pollutant. The investigation encompasses an exploration of the biosorption isotherm and kinetics governing the dye removal process. Additionally, various machine learning models are employed to predict the efficiency of dye removal within a co-culture system. The results demonstrate that both Desmodesmus abundans and a composite of Desmodesmus abundans and Rhodococcus pyridinivorans exhibit significant dye removal percentages of 75 ± 1% and 78 ± 1%, respectively, after 40 min. The biosorption isotherm analysis reveals a significant interaction between the adsorbate and the biosorbent, and it indicates that the Temkin model best matches the experimental data. Moreover, the Langmuir model indicates a relatively high biosorption capacity, further highlighting the potential of the algae-bacteria composite as an efficient adsorbent. Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks are evaluated for predicting dye removal efficiency. The Random Forest model emerges as the most accurate, exhibiting an R2 value of 0.98, while Support Vector Regression and Artificial Neural Networks also demonstrate robust predictive capabilities. This study contributes to the advancement of sustainable dye removal strategies and encourages future exploration of hybrid approaches to further enhance predictive accuracy and efficiency in wastewater treatment processes.

Enhancing cationic dye removal via biocomposite formation between chitosan and food grade algae: Optimization of algae loading and adsorption parameters

Herein, a natural material including chitosan (CTS) and algae (food-grade algae, FGA) was exploited to attain a bio-adsorbent (CTS/FGA) for enhanced methyl violet 2B dye removal. A study of the FGA loading into CTS matrix showed that the best mixing ratio between CTS and FGA to be used for the MV 2B removal was 50 %:50 % (CTS/FGA; 50:50 w/w). The present study employed the Box-Behnken design (RSM-BBD) to investigate the impact of three processing factors, namely CTS/FGA-(50:50) dose (0.02-0.1 g/100 mL), pH of solution (4-10), and contact time (5-15 min) on the decolorization rate of MV 2B dye. The results obtained from the equilibrium and kinetic experiments indicate that the adsorption of MV 2B dye on CTS/FGA-(50:50) follows the Langmuir and pseudo-second-order models, respectively. The CTS/FGA exhibits an adsorption capacity of 179.8 mg/g. The characterization of CTS/FGA-(50:50) involves the proposed mechanism of MV 2B adsorption, which primarily encompasses various interactions such as electrostatic forces, n-π stacking, and H-bonding. The present study demonstrates that CTS/FGA-(50:50) synthesized material exhibits a distinctive structure and excellent adsorption properties, thereby providing a viable option for the elimination of toxic cationic dyes from polluted water.

Sugeno fuzzy inference system modeling and DFT calculations for the treatment of pesticide-laden water by newly developed arginine functionalized magnetic Mn-based metal organic framework

The uncontrolled utilization of pesticides poses a significant risk to the environment and human health, making its management essential. In this regard, a new arginine functionalized magnetic Mn-based metal-organic framework (Arg@m-Mn-MOF) was fabricated and assessed for the removal of cypermethrin (CYP) and chlorpyrifos (CHL) from aqueous system. The Arg@m-Mn-MOF was characterized by scanning electron microscopy, energy dispersive X-ray, Fourier transform infrared spectroscopy, X-ray diffraction, and Brunauer-Emmett-Teller analysis. Various parameters were optimized in a series of batch experiments and the following conditions were found optimal: pH: 4 and 5, contact time: 20 min, adsorbent dosage: 0.6 and 0.8 g L-1 with initial concentration: 10 mg L-1 and temperature: 298 K for CYP and CHL, respectively. The composite attained a maximum removal capacity of 44.84 and 71.42 mg g-1 for CYP and CHL, respectively. The elucidated data was strongly fitted to the pseudo-second-order model of kinetics (R2 > 0.98) and Langmuir isotherm (R2 > 0.98). Based upon 350 experimental datasets obtained from batch studies and interpolated data, the adsorption capacity of the adsorbent was elucidated with R2 > 0.97 (CHL) and > 0.91 (CYP). The adsorption energy (- 11.67 kcal mol-1) calculated by Gaussian software suggests a good interaction between arginine and CHL through H-bonding. The present study's findings suggested the prepared Arg@m-Mn-MOF as a promising adsorbent for the efficient removal of pesticides from agriculture runoff.