Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery

Coordination Chemistry Reviews, Год журнала: 2024, Номер 514, С. 215888 - 215888

Опубликована: Май 8, 2024

Язык: Английский

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

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

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Advancements in molecular simulation for understanding pharmaceutical pollutant Adsorption: A State-of-the-Art review

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

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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

Separation and Purification Technology, Год журнала: 2025, Номер 354, С. 128986 - 128986

Опубликована: Фев. 1, 2025

Язык: Английский

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Tackling antibiotic contaminations in wastewater with novel Modified-MOF nanostructures: A study of molecular simulations and DFT calculations

Environmental Research, Год журнала: 2024, Номер 252, С. 118856 - 118856

Опубликована: Апрель 8, 2024

Язык: Английский

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Molecular Simulation Studies of Pharmaceutical Pollutant Removal (Rosuvastatin and Simvastatin) Using Novel Modified-MOF Nanostructures (UIO-66, UIO-66/Chitosan, and UIO-66/Oxidized Chitosan)

ACS Applied Materials & Interfaces, Год журнала: 2024, Номер 16(20), С. 26685 - 26712

Опубликована: Май 9, 2024

The ubiquitous presence of pharmaceutical pollutants in the environment significantly threatens human health and aquatic ecosystems. Conventional wastewater treatment processes often fall short effectively removing these emerging contaminants. Therefore, development high-performance adsorbents is crucial for environmental remediation. This research utilizes molecular simulation to explore potential novel modified metal-organic frameworks (MOFs) pollutant removal, paving way design efficient strategies. Utilizing UIO-66, a robust MOF, as base material, we developed UIO-66 functionalized with chitosan (CHI) oxidized (OCHI). These MOFs' physical chemical properties were first investigated through various characterization techniques. Subsequently, dynamics (MDS) Monte Carlo (MCS) employed elucidate adsorption mechanisms rosuvastatin (ROSU) simvastatin (SIMV), two prevalent pollutants, onto nanostructures. MCS calculations demonstrated significant enhancement energy by incorporating CHI OCHI into UIO-66. increased ROSU from -14,522 -16,459 kcal/mol SIMV -17,652 -21,207 kcal/mol. Moreover, MDS reveals rejection rates neat be at 40%, rising 60 70% OCHI. Accumulation increase 4 Å 6 9 UIO-CHI UIO-OCHI. Concentration analysis shows surges 50 90%, accumulation increasing 11 Functionalizing enhanced capacity selectivity SIMV. Abundant hydroxyl amino groups facilitated strong interactions, improving performance over that unmodified Surface functionalization plays vital role customizing MOFs removal. insights guide next-gen adsorbent development, offering high efficiency treatment.

Язык: Английский

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Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Chemosphere, Год журнала: 2024, Номер 362, С. 142792 - 142792

Опубликована: Июль 5, 2024

Pesticide pollution has been posing a significant risk to human and ecosystems, photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as powerful method modeling complex water treatment processes. For first time, this study developed novel ML models that improved estimation photocatalytic various pesticides using ZnO-based photocatalysts. The input parameters encompassed source light, mass proportion dopants Zn, initial pesticide concentration (C0), pH solution, catalyst dosage irradiation time. Additionally, physicochemical properties such molecular weight pesticides, well solubility both were considered. Notably, numerical data extracted from literature via relevant tables (directly) or graphs (indirectly) web-based tool WebPlotDigitizer. Four including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), coupled simulated annealing-least squares support vector machine (CSA-LSSVM) developed. In comparison, RBF showed best accuracy among all models, with highest determination coefficient (R2) 0.978 average absolute relative deviation (AARD) 4.80%. model was effective in estimating except 2-chlorophenol, triclopyr lambda-cyhalothrin, where CSA-LSSVM demonstrated superior performance. Dichlorvos completely degraded by ZnO photocatalyst under visible light. sensitivity analysis relevancy factor exhibited light time most important influencing positively negatively, respectively. new provide predicting wastewater treatment, which will reduce photochemical experiments promote sustainable development.

Язык: Английский

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A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Physica Scripta, Год журнала: 2024, Номер 99(7), С. 076015 - 076015

Опубликована: Июнь 10, 2024

Abstract Hydrogen, as the lightest and most abundant element in universe, has emerged a pivotal player quest for sustainable energy solutions. Its remarkable properties, such high density zero emissions upon combustion, make it promising candidate addressing pressing challenges of climate change transitioning towards clean renewable future. In an effort to improve efficiency reduce experimental costs, we adopted machine learning techniques this study. Our focus turned predictive analyses hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO 3 (GLFO), LaRuO (GLRO), BiFeO (GBFO), examining their correlation with varying levels pH, catalyst amount, H 2 O concentration. To achieve this, diverse range models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, AdaBoost—each bringing its strengths modeling arena. An important step involved combining effective models—Random Forests, XGBoost—into ensemble model. This collaborative approach aimed leverage collective overall predictability. The model powerful tool understanding photocatalytic evolution. Standard metrics were employed assess performance our prediction model, encompassing R squared, Root Mean Squared Error (RMSE), (MSE), Absolute (MAE). yielded results showcase exceptional accuracy, squared 96.9%, 99.3%, 98% GLFO, GBFO, GLRO, respectively. Moreover, demonstrates minimal error rates across all metrics, underscoring robust capabilities highlighting efficacy accurately forecasting intricate relationships between GLRO influencing factors.

Язык: Английский

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Sustainable synergistic adsorption of tetracycline in water by biochar and microplastics: Exploration of the mechanism of DFT

Journal of Water Process Engineering, Год журнала: 2024, Номер 66, С. 105998 - 105998

Опубликована: Авг. 15, 2024

Язык: Английский

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Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes

Energy Conversion and Management, Год журнала: 2025, Номер 327, С. 119544 - 119544

Опубликована: Янв. 24, 2025

Язык: Английский

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