Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites DOI Creative Commons

Iman Salahshoori,

Amirhosein Yazdanbakhsh, Alireza Baghban

et al.

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

Published: April 15, 2024

Abstract This study explores the potential of photocatalytic degradation using novel NML-BiFeO 3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness various Gaussian process regression (GPR) models in predicting MG is investigated. Four GPR (Matern, Exponential, Squared and Rational Quadratic) were employed to analyze a dataset 1200 observations encompassing experimental conditions. have considered ten input variables, including catalyst properties, solution characteristics, operational parameters. Exponential kernel-based model achieved best performance, with near-perfect R 2 value 1.0, indicating exceptional accuracy degradation. Sensitivity analysis revealed time as most critical factor influencing degradation, followed by pore volume, loading, light intensity, type, pH, anion surface area, humic acid concentration. highlights complex interplay between these factors process. reliability was confirmed outlier detection William’s plot, demonstrating minimal number outliers (66–71 data points depending on model). indicates robustness utilized development. suggests that composites hold promise wastewater treatment models, particularly Matern-GPR, offer powerful tool Identifying fundamental properties can expedite application , leading optimized processes. Overall, this provides valuable insights into machine learning efficient removal

Language: Английский

Advancements in wastewater Treatment: A computational analysis of adsorption characteristics of cationic dyes pollutants on amide Functionalized-MOF nanostructure MIL-53 (Al) surfaces DOI

Iman Salahshoori,

Majid Namayandeh Jorabchi,

Somayeh Ghasemi

et al.

Separation and Purification Technology, Journal Year: 2023, Volume and Issue: 319, P. 124081 - 124081

Published: May 16, 2023

Language: Английский

Citations

55
Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges DOI Creative Commons

Iman Salahshoori,

Mahdi Golriz,

Marcos A.L. Nobre

et al.

Journal of Molecular Liquids, Journal Year: 2023, Volume and Issue: 395, P. 123888 - 123888

Published: Dec. 27, 2023

Efficient drug delivery systems (DDSs) play a pivotal role in ensuring pharmaceuticals' targeted and effective administration. However, the intricate interplay between formulations poses challenges their design optimization. Simulations have emerged as indispensable tools for comprehending these interactions enhancing DDS performance to address this complexity. This comprehensive review explores latest advancements simulation techniques provides detailed analysis. The encompasses various methodologies, including molecular dynamics (MD), Monte Carlo (MC), finite element analysis (FEA), computational fluid (CFD), density functional theory (DFT), machine learning (ML), dissipative particle (DPD). These are critically examined context of research. article presents illustrative case studies involving liposomal, polymer-based, nano-particulate, implantable DDSs, demonstrating influential simulations optimizing systems. Furthermore, addresses advantages limitations It also identifies future directions research development, such integrating multiple techniques, refining validating models greater accuracy, overcoming limitations, exploring applications personalized medicine innovative DDSs. employing like MD, MC, FEA, CFD, DFT, ML, DPD offer crucial insights into behaviour, aiding Despite advantages, rapid cost-effective screening, require validation addressing limitations. Future should focus on models, enhance outcomes. paper underscores contribution emphasizing providing valuable facilitating development optimization ultimately patient As we continue explore impact advancing discovery improving DDSs is expected be profound.

Language: Английский

Citations

52
Assessing cationic dye adsorption mechanisms on MIL-53 (Al) nanostructured MOF materials using quantum chemical and molecular simulations: Toward environmentally sustainable wastewater treatment DOI

Iman Salahshoori,

Majid Namayandeh Jorabchi,

Somayeh Ghasemi

et al.

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 55, P. 104081 - 104081

Published: July 29, 2023

Language: Английский

Citations

50
An in silico study of sustainable drug pollutants removal using carboxylic acid functionalized-MOF nanostructures (MIL-53 (Al)-(COOH)2): Towards a greener future DOI

Iman Salahshoori,

Majid Namayandeh Jorabchi,

Somayeh Ghasemi

et al.

Desalination, Journal Year: 2023, Volume and Issue: 559, P. 116654 - 116654

Published: May 1, 2023

Language: Английский

Citations

49
Molecular simulation-based insights into dye pollutant adsorption: A perspective review DOI Creative Commons

Iman Salahshoori,

Qilin Wang, Marcos A.L. Nobre

et al.

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

Citations

21
Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach DOI Creative Commons

Iman Salahshoori,

Marcos A.L. Nobre, Amirhosein Yazdanbakhsh

et al.

Journal 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

18
Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet pollutants capture DOI

Amir Bateni,

Iman Salahshoori,

Majid Namayandeh Jorabchi

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: 354, P. 128986 - 128986

Published: Feb. 1, 2025

Language: Английский

Citations

11
Synthesis of a novel polymer and design of carboxylate-terminated hyperbranched PEI-incorporated PVDF membranes for efficient oil-in-water emulsion separation DOI
Nadeem Baig, Billel Salhi, Shahid Ali

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: 354, P. 128628 - 128628

Published: Feb. 1, 2025

Language: Английский

Citations

9
MIL-53 (Al) nanostructure for non-steroidal anti-inflammatory drug adsorption in wastewater treatment: Molecular simulation and experimental insights DOI

Iman Salahshoori,

Majid Namayandeh Jorabchi,

Somayeh Ghasemi

et al.

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 175, P. 473 - 494

Published: May 26, 2023

Language: Английский

Citations

40
Insights into the morphology and gas separation characteristics of methylene diisocyanate (MDI)-functionalized nanoTiO2 polyurethane: quantum mechanics and molecular simulations studies DOI Creative Commons

Iman Salahshoori,

Majid Namayandeh Jorabchi, Morteza Asghari

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 23, P. 1862 - 1886

Published: Jan. 18, 2023

It is of important scientific significance to develop membranes with high gas transfer properties. The primary aim our study was inspect the separation behavior and morphological characteristics mixed matrix (MMMs) based on TiO2-polyurethane (PU) methylene diisocyanate (MDI)-TiO2-PU using quantum mechanics (QM), Monte Carlo (MC), molecular dynamics (MD) simulations. Frontier Molecular Orbital (FMO) QM approaches such as Mulliken charges, density states (DOS), electrostatic potential (ESP), Conductor-like screening model (COSMO), Fukui's function orbitals were employed ascertain chemical reactivity, regioselectivity, phase behaviour, surface properties neat MMMs. Furthermore, physicochemical MMMs structures, including radius gyration (Rg), X-ray scattering, radial distribution (RDF), solubility parameter (δ), cohesive energy (CED), free fractional volume (FFV), mechanical investigated MD simulation. Based obtained results, it can be dedicated that structures could exhibit improved Additionally, simulation used CO2, CH4, N2 transport in containing higher concentrations nanoparticles. Consequently, results indicated constructed MMM's performances close Robeson's upper bound.

Language: Английский

Citations

38