首页|Beijing Technology and Business University Reports Findings in Machine Learning [Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS]
Beijing Technology and Business University Reports Findings in Machine Learning [Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Heavy met- als raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment.” The news correspondents obtained a quote from the research from Beijing Technology and Business Uni- versity, “However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well- trained multi-grained cascade forest models exhibited high accuracy (>90%) and predictive performance (R 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations.” According to the news reporters, the research concluded: “This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would pro- vide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.” This research has been peer-reviewed.
BeijingPeople’s Republic of ChinaAsiaAnionsCyborgsEmerging TechnologiesEngineeringMachine LearningOxidesOxygen Compounds
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