Water Research, Journal Year: 2024, Volume and Issue: unknown, P. 123037 - 123037
Published: Dec. 1, 2024
Language: Английский
Water Research, Journal Year: 2024, Volume and Issue: unknown, P. 123037 - 123037
Published: Dec. 1, 2024
Language: Английский
Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 472, P. 134420 - 134420
Published: April 27, 2024
Language: Английский
Citations
42Advances 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
Citations
21Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125592 - 125592
Published: July 20, 2024
Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.
Language: Английский
Citations
18Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125513 - 125513
Published: July 14, 2024
The contamination of natural water resources by pharmaceutical pollutants has become a significant environmental concern. Traditional experimental approaches for understanding the adsorption behavior these contaminants on different surfaces are often time-consuming and resource-intensive. In response, this review article explores powerful combination in silico techniques, including molecular dynamics (MD), Monte Carlo simulations (MC), quantum mechanics (QM), as comprehensive toolset to obtain broad perspectives into pollutants. By bridging multiple scales, from molecular-level interactions macroscopic impact, computational methods offer holistic processes involved. We provide an overview their ecological effects, emphasizing need efficient sustainable solutions. Subsequently, we delve theoretical foundations MD, MC, QM, highlighting respective strengths simulating pollutant adsorption. Moreover, synergistic potential combining methodologies is also discussed more characterization processes. Recent case studies illustrate successful application techniques predicting behaviors various conditions. Finally, implications discussed, along with how modelling can guide solutions mitigating impact.
Language: Английский
Citations
16Environmental Pollution, Journal Year: 2024, Volume and Issue: 350, P. 124037 - 124037
Published: April 25, 2024
Language: Английский
Citations
10Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 12, 2024
As regulatory standards for per- and polyfluoroalkyl substances (PFAS) become increasingly stringent, innovative water treatment technologies are urgently demanded effective PFAS removal. Reported sorbents often exhibit limited affinity frequently hindered by competitive background substances. Recently, fluorinated (abbreviated as fluorosorbents) have emerged a potent solution leveraging fluorine-fluorine (F···F) interactions to enhance selectivity efficiency in This review delves into the designs applications of fluorosorbents, emphasizing how F···F improve binding affinity. Specifically, existence results removal efficiencies orders magnitude higher than other counterpart sorbents, particularly under conditions. Furthermore, we provide detailed analysis fundamental principles underlying elucidate their synergistic effects with sorption forces, which contribute enhanced efficacy selectivity. Subsequently, examine various fluorosorbents synthesis fluorination techniques, underscore importance accurately characterizing through advanced analytical methods, emphasize significance this interaction developing selective sorbents. Finally, discuss challenges opportunities associated employing techniques guide design advocate further research development sustainable cost-effective interactions.
Language: Английский
Citations
10Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(18), P. 8065 - 8075
Published: April 10, 2024
We report a previously unrecognized but efficient reductive degradation pathway in peroxydisulfate (PDS)-driven soil remediation. With supplements of naturally occurring low-molecular-weight organic acids (LMWOAs) anaerobic biochar-activated PDS systems, rates 12 γ-hexachlorocyclohexanes (HCH)-spiked soils boosted from 40% without LMWOAs to maximum 99% with 1 mM malic acid. Structural analysis revealed that an increase α-hydroxyl groups and diminution pKa1 values facilitated the formation carboxyl anion radicals (COO•–) via electrophilic attack by SO4•–/•OH. Furthermore, kinetics were strongly correlated matter (SOM) contents than iron minerals. Combining newly developed situ fluorescence detector quenching experiments, we showed for high, medium, low SOM contents, dominant reactive species switched singlet oxygen/semiquinone SO4•–/•OH then COO•– (contribution increased 30.8 66.7%), yielding superior HCH degradation. Validation experiments using model compounds highlighted critical roles redox-active moieties, such as phenolic – OH quinones, radical conversion. Our study provides insights into environmental behaviors related activation persulfate broader horizon inspiration more advanced reduction technologies.
Language: Английский
Citations
9Journal of Hazardous Materials, Journal Year: 2025, Volume and Issue: 488, P. 137216 - 137216
Published: Jan. 14, 2025
Language: Английский
Citations
1Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 493, P. 152557 - 152557
Published: May 25, 2024
Language: Английский
Citations
6Deleted Journal, Journal Year: 2024, Volume and Issue: 1(4), P. 100018 - 100018
Published: Sept. 11, 2024
Language: Английский
Citations
6