首页|Reports on Machine Learning Findings from Nankai University Provide New Insights (Reveal the Main Factors and Adsorption Behavior Influencing the Adsorption of Pollutants On Natural Mineral Adsorbents: Based On Machine Learning Modeling and Dft ...)
Reports on Machine Learning Findings from Nankai University Provide New Insights (Reveal the Main Factors and Adsorption Behavior Influencing the Adsorption of Pollutants On Natural Mineral Adsorbents: Based On Machine Learning Modeling and Dft ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting originating in Tianjin, People's Re public of China, by NewsRx journalists, research stated, "Montmorillonite, as a natural mineral adsorption material that has high research value in water pollut ion treatment. However, the adsorption capacity varies depending on the type of pollutant and the properties of the montmorillonite material, and the factors co ntrolling adsorption are not yet clear." Financial support for this research came from Tianjin science and technology sup port key projects. The news reporters obtained a quote from the research from Nankai University, "H erein, we investigated the adsorption behavior of pollutants on montmorillonite materials using density functional theory (DFT) calculations and machine learnin g modeling. Furthermore, it explores the main factors influencing their adsorpti on. The machine learning results indicate that the gradient boosting decision tr ee (GBDT) model exhibits a better fit to the experimental data compared to the o ther five machine learning models (R2 = 0.79). The higher pH levels and larger r elative molecular mass of pollutants have a positive impact on montmorillonite a dsorption. However, an increase in the proportion of oxygen atoms in the adsorbe nt material and longer hydrothermal preparation time show a trend of initially p ositive and then negative effects on the predicted results. The influence of pH on the adsorption capacity of montmorillonite adsorbents was further analyzed us ing density functional theory (DFT). Density functional theory (DFT) studies rev eal that montmorillonite primarily removes protonated sulfamethoxazole (SMZ) thr ough hydrogen bonding (N-H...O) interactions, accompanied by van der waals (O-O) and ionic bond (C-O...Al) forces under different pH conditions. The partial den sity of states (PDOS) reveals that the LUMO orbital of montmorillonite has a hig her electron accepting ability than the HOMO orbital of SMZ (p orbital peak is g reater than the S orbital) in the actual electron transfer process."
TianjinPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningNankai University
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