Molecular simulation-based insights into dye pollutant adsorption: A perspective review

Advances in Colloid and Interface Science, Journal Year: 2024, Volume and Issue: 333, P. 103281 - 103281

Published: Aug. 24, 2024

Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, machine learning (ML) are powerful tools used study predict adsorption processes of dyes on adsorbents. These provide detailed insights into interactions mechanisms involved, which can be crucial designing systems. MD detailing arrangements, dyes' behaviour interaction energies with They simulate entire process, including surface diffusion, solvent layer penetration, physisorption. QM especially density functional theory (DFT), determine structures reactivity descriptors, aiding in understanding mechanisms. identify stable configurations like hydrogen bonding electrostatic forces. MC simulations equilibrium properties by sampling configurations. ML proven highly effective predicting optimizing processes. models offer significant advantages over traditional methods, higher accuracy ability handle complex datasets. optimize conditions, clarify adsorbent functionalization roles, removal efficiency under conditions. This research explores MD, MC, QM, approaches connect macroscopic phenomena. Probing these techniques provides energetics pollutants surfaces. The findings will aid developing new materials removal. review has implications remediation, offering a comprehensive at scales. Merging microscopic data observations enhances knowledge pollutant adsorption, laying groundwork efficient, technologies. Addressing growing challenges ecosystem protection, contributes cleaner, more future. • Enviro concern drives eco-friendly Computation unveils Study bridges dynamics, Carlo, mechanics. Insights inform novel adsorbents Integration shapes greener solutions.

Language: Английский

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Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 480, P. 135853 - 135853

Published: Sept. 16, 2024

Language: Английский

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Adsorptive removal of malachite green dye from aqueous solution using Cordia africana leaf as biosorbent

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 20, 2025

Language: Английский

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Machine learning-driven prediction of biochar adsorption capacity for effective removal of Congo red dye

Carbon Research, Journal Year: 2025, Volume and Issue: 4(1)

Published: Jan. 22, 2025

Abstract Congo red, a widely utilized dye in the textile industry, presents significant threat to living organisms due its carcinogenic properties and non-biodegradable nature. This study proposes data-driven machine-learning approach optimize biochar characteristics environmental conditions maximize adsorption capacity of for removal red dye. Therefore, six machine learning models were trained tested on dataset containing eleven input parameters (related conditions) capacity. The evaluated using performance metrics such as R-squared ( R 2 ), Mean Squared Error (MSE), Root (RMSE). With highest (0.9785) lowest RMSE (0.1357), Random Forest Regression (RF) outperformed other models. DT XGB also performed well, achieving slightly lower values 0.9741 0.9577, respectively. LR model worst, with (0.4575) (0.6821). Moreover, reliability these was validated 10-fold cross-validation method. RF once again best an value 0.9762. Feature analysis revealed that initial concentration relative dosage C 0 specific surface area BET pore volume PV ) are most factors affecting biochar, while carbon content oxygen nitrogen molar ratio [ (O + N)/C ], diameter D had minimal impact. research demonstrates can accurately predict biochar’s contaminant capacity, enhancing wastewater treatment promoting efficient, cost-effective management. Graphical

Language: Английский

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Engineering of Mesoporous Gd-substituted Ni-Co Ferrites as Adsorbents for Efficient Elimination of Congo Red Dye and Oxytetracycline

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(3)

Published: Feb. 24, 2025

Language: Английский

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Biochar-Based Contaminant Removal: A Tutorial on Analytical Quality Assurance and Best Practices in Batch Sorption

Journal of Chromatography Open, Journal Year: 2025, Volume and Issue: unknown, P. 100219 - 100219

Published: April 1, 2025

Language: Английский

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Fabrication of Reduced Graphene Oxide Decorated with Nonmetal-Doped Nanotitania: An Efficient Visible Light–Driven Photocatalyst and Sterilizing Agent for Microbial Cells

ACS Omega, Journal Year: 2025, Volume and Issue: 10(6), P. 5296 - 5311

Published: Feb. 10, 2025

The present study pertains to the fabrication of a visible light-responsive nanocomposite nitrogen and sulfur-doped TiO2 anchoring reduced graphene oxide (NSNTG) using facile microwave method for enhanced photocatalytic activity. Two other nanocomposites, TiO2/rGO (NTG) nitrogen-doped (NNTG), were also synthesized by same method. X-ray diffraction (XRD) Raman spectroscopy employed confirm presence crystalline anatase phase. Elemental composition formation homogeneous dispersion modified on GO surface explored Field-emission scanning electron microscopy (FESEM) with energy-dispersive (EDX), transmission (TEM). photoelectron (XPS) photoluminescence (PL) studies revealed composition, chemical state, oxygen vacancy defects. A lower bandgap energy stronger absorption in region confirmed UV-visible diffuse reflectance (DRS) analysis. Mott-Schottky analysis flat band potentials alignment NTG, NNTG, NSNTG, indicating n-type semiconductor behaviors potential values -0.50, -0.60, -0.68 V, respectively, providing insights into their charge transfer processes. high photocurrent response NSNTG facilitates efficient migration inhibiting recombination, aligning results from PL EIS measurements. In addition, large area (233.0 m2/g) small pore size distribution (7.8 nm) nanocomposites N2 adsorption-desorption analysis, supporting adsorption organic pollutants. activity was studied degradation dyes under light; exhibited higher removal rates, 97 90% within 35 60 min, methylene blue (MB) rhodamine B (RhB), respectively. optimal conditions pH 8, dye concentration 50 mg/L, photocatalyst dosage mg. Mineralization rates evaluated TOC 85.21 82.30% MB RhB, key reactive species involved identified as photogenerated hydroxyl radicals, holes, superoxide radicals. tested antimicrobial efficacy against Gram-negative Gram-positive bacteria, disc diffusion method, maximum zone inhibition Bacillus subtilis Staphylococcus aureus, comparable gentamycin. This outlines cost-effective, high-yield, reusable material environmental applications.

Language: Английский

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Areca Catechu Biochar and Nano-Biochar as Adsorbents for Congo Red: Synthesis, Characterization, and Performance Evaluation

BULLETIN OF CHEMICAL REACTION ENGINEERING AND CATALYSIS, Journal Year: 2025, Volume and Issue: 20(1), P. 112 - 128

Published: Feb. 10, 2025

The presence of hazardous synthetic dyes such as Congo Red in industrial wastewater poses a significant environmental threat. This study explores the potential biochar (BC) and nano-biochar (nano-BC), derived from Areca catechu husk sustainable adsorbents for dye removal. Nano-BC was synthesised via hydrothermal carbonisation mechanical ball milling, leading to enhanced structural surface properties. X-ray Diffraction (XRD) revealed that Pinang is predominantly amorphous, while BC exhibits increased crystallinity with sharp peaks, nano-BC demonstrates highest nanostructural refinement. Fourier Transform Infra (FTIR) confirmed transformation aliphatic-rich raw biomass into aromatic-dominant structures nano-BC, showing more pronounced graphite-like features. Scanning Electron Microscope (SEM) illustrated morphological evolution, exhibiting refined, uniformly porous structures. BET analysis has significantly higher area 41.38 m²/g smaller pore size 8.4928 nm compared 22.38 15.39 nm, enhancing adsorption capacity. Furthermore, kinetics followed pseudo-second-order model, isothermal monolayer maximum capacity (Qmax = 154.526 mg/g). These findings highlight superior performance emphasising its environmentally friendly water treatment applications. Copyright © 2025 by Authors, Published BCREC Publishing Group. an open access article under CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Language: Английский

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Prediction of methylene blue dye sorption to sulfonated date palm kernel biochar using statistical regression and machine learning methods and DFT studies

Journal of Molecular Liquids, Journal Year: 2025, Volume and Issue: unknown, P. 127547 - 127547

Published: April 1, 2025

Language: Английский

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Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 30, 2024

The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, industrial from runoff discharges, poses critical public health environmental risks leading to severe issues ecosystem damage; education plays a crucial role mitigating these effects by enhancing awareness, promoting sustainable practices, integrating science into curricula empower individuals address advocate for effective solutions water pollution. However, the educational transformation should be accompanied with technical process which can eventually transferred society education. In this study, carbonaceous material derived Haematoxylum campechianum (CM-HC) was utilized removing 3-nitrophenol (3-Nph) aqueous solutions. novelty research utilizes bark coconut shell, abundant agricultural wastes Campeche, Mexico, toxin removal, adsorption through artificial neural networks genetic algorithms optimize conditions maximize absorption efficiency. CM-HC's surface morphology analyzed using scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), pHpzc. Kinetic models pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich were applied fit data. Adsorption isotherms determined at varying pH (3-8), adsorbent dosages (2-10 g/L), temperatures (300.15-330.15 K), employing Langmuir, Freundlich, Temkin, Redlich-Peterson models. PSO kinetics demonstrated good (R

Language: Английский

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