Process Safety and Environmental Protection, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 1, 2024
Язык: Английский
Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(5), С. 113754 - 113754
Опубликована: Авг. 6, 2024
Язык: Английский
Процитировано
60
Journal of Water Process Engineering, Год журнала: 2024, Номер 58, С. 104766 - 104766
Опубликована: Янв. 16, 2024
Язык: Английский
Процитировано
35
Separation and Purification Technology, Год журнала: 2024, Номер 343, С. 126987 - 126987
Опубликована: Март 6, 2024
Язык: Английский
Процитировано
32
Advances in Colloid and Interface Science, Год журнала: 2024, Номер 333, С. 103281 - 103281
Опубликована: Авг. 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.
Язык: Английский
Процитировано
26
Journal of Hazardous Materials, Год журнала: 2024, Номер 468, С. 133725 - 133725
Опубликована: Фев. 6, 2024
Язык: Английский
Процитировано
23
Journal of Molecular Liquids, Год журнала: 2024, Номер 410, С. 125592 - 125592
Опубликована: Июль 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.
Язык: Английский
Процитировано
20
Nano Today, Год журнала: 2024, Номер 56, С. 102227 - 102227
Опубликована: Март 18, 2024
Язык: Английский
Процитировано
19
Separation and Purification Technology, Год журнала: 2024, Номер 347, С. 127563 - 127563
Опубликована: Апрель 18, 2024
Язык: Английский
Процитировано
19
Journal of Molecular Liquids, Год журнала: 2024, Номер 410, С. 125513 - 125513
Опубликована: Июль 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.
Язык: Английский
Процитировано
19
The Science of The Total Environment, Год журнала: 2024, Номер 925, С. 171774 - 171774
Опубликована: Март 18, 2024
Язык: Английский
Процитировано
18