Enhanced Removal of Rhodamine B Dye by Adsorption and Photodegradation Processes Using Bentonite-Semiconductor-Based Nanocomposites: Synthesis, Characterization, Kinetic Study, and Cost Analysis

Chemistry Africa, Journal Year: 2025, Volume and Issue: unknown

Published: March 9, 2025

Language: Английский

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Synthesis and characterisation of novel beta-cyclodextrin based Fe ₃ O ₄ nanocomposite: adsorption behaviour and photocatalytic dye degradation studies

International Journal of Environmental & Analytical Chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: July 5, 2024

Novel Beta-Cyclodextrin-based Fe3O4 nanocomposite has been synthesised through copolymerisation by incorporating nanoparticles into the polymer matrix. Ultraviolet-Visible (UV-Visible) spectroscopy, Fourier transform infrared (FTIR) Thermogravimetric (TGA) analysis, Point of Zero Charge, X-ray diffraction (XRD), and Scanning electron microscopy (SEM) analysis have used to characterise Nanoparticles β-CDHs@Fe3O4 nanocomposite. The utilised for adsorption methylene blue (MB) dye from aqueous solutions. maximum percentage removal MB at optimum pH 7 was found be 98.51%. capacity (qe) 268.09 mg/g dosages 1.2 g/L, 7, temperature 25°C, concentration 100 mg/L an agitation speed 200 rpm. Adsorption data fitted with three isotherm models namely Langmuir, Freundlich, Temkin, it best fit Langmuir model. Kinetic studies like pseudo-first order, pseudo-second intra-particle diffusion model were also performed study mechanism. Further, thermodynamic demonstrate that reaction exothermic spontaneous. Photocatalytic degradation under visible light studied, 79.01% observed. Also, desorption performed, reusable up 5 times 90% dye-desorption in fifth cycle.

Language: Английский

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Sustainable polymeric adsorbents for adsorption-based water remediation and pathogen deactivation: a review

RSC Advances, Journal Year: 2024, Volume and Issue: 14(45), P. 33143 - 33190

Published: Jan. 1, 2024

Water is a fundamental resource, yet various contaminants increasingly threaten its quality, necessitating effective remediation strategies.

Language: Английский

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Plant mediated MgO@biochar nanocomposite: A sustainable and promising photo catalyst for degradation of Eosin-Y dye

Inorganic Chemistry Communications, Journal Year: 2025, Volume and Issue: unknown, P. 114142 - 114142

Published: Feb. 1, 2025

Language: Английский

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Selective Adsorption of Lead in Mixed Metals Wastewater System by Lignin-Carbon-Supported Titanate Nanoflower BC@TNS Adsorbent: Performance and Mechanism

Coatings, Journal Year: 2025, Volume and Issue: 15(3), P. 317 - 317

Published: March 9, 2025

This study introduced a novel type of biochar–titanate nanosheet (BC@TNS) composite for the selective adsorption Pb(II) from wastewater containing various heavy metal ions. The biochar derived lignin–carbon pyrolysis forms scaffold, while titanate nanosheets coat it via an alkaline hydrothermal reaction. synthesis was confirmed through analytic characterizations, revealing distinctive morphology TNS nanoflowers consisting numerous incorporated into BC support. BC@TNS achieved maximum capacities 37.89 mg/g Pb(II), 13.38 Cd(II), and 8.47 Zn(II), demonstrating its remarkable selectivity Pb(II). Kinetic studies using Weber–Morris, PFO, PSO models indicated that primarily driven by chemisorption, whereas Cd(II) Zn(II) were predominantly governed physisorption. Isotherm analysis Langmuir, Freundlich, Dubinin–Radushkevich, Temkin revealed involved both monolayer multilayer processes, monolayer. Detailed insights scanning electron microscopy (SEM-EDS) X-ray photoelectron spectroscopy (XPS) analyses further elucidated these mechanisms. superior validated in multicomponent simulated HMs 10 co-existing ions, maintaining high efficiency 75.68%, highlighting potential Pb recovery. Moreover, adsorbent demonstrated excellent regeneration capacity recyclability. shows great efficient removal ions wastewater, offering sustainable solution environmental protection.

Language: Английский

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Optimizing the properties of CdS nanoparticles through niobium incorporation: A study on their potential applications in industrial effluent remediation

Results in Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 102211 - 102211

Published: March 1, 2025

Language: Английский

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Sustainable activated carbon derived from coconut shell and betel nuts for energy storage and environmental remediation

Diamond and Related Materials, Journal Year: 2025, Volume and Issue: unknown, P. 112243 - 112243

Published: March 1, 2025

Language: Английский

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Enhanced dyeing performance, UV resistance and antioxidant properties of plant dyes based on a binary synergistic effect

Cellulose, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Language: Английский

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Composition modulation of composited metal oxides sorbents based on modified nanofibers for H2S removal towards high temperature coal gas

Applied Surface Science, Journal Year: 2025, Volume and Issue: unknown, P. 163212 - 163212

Published: April 1, 2025

Language: Английский

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Heavy metals removal from wastewater using nanoporous adsorbent: Separation analysis via machine learning model

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 59, P. 104501 - 104501

Published: May 8, 2024

This paper presents a comprehensive analysis of three predictive models, namely Multi-Layer Perceptron (MLP), LASSO, and Extreme Gradient Boosting (XGB) for estimating concentration (C) substance in given dataset. Adsorption separation was considered water treatment, the models were employed tracking variations solute process. With x(m) y(m) as input variables which are location, measured mol/m³ output, dataset comprises more than 19,000 data points. The Fireworks Algorithm (FWA) to perform hyper-parameter optimization models. Different metrics utilized gauge proficiency each model, such R2 (coefficient determination) both training testing datasets, RMSE, MAE. Results indicate that MLP model obtained highest score terms R2, with values 0.99751 0.99756 data, suggesting excellent accuracy. also demonstrated lowest error rates, an RMSE 1.3937E+00 MAE 8.81875E-01. results revealed machine learning well capable predicting adsorption process when combined mass transfer modeling great accuracy can be obtained.

Language: Английский

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