Mehdi Gharasoo of the Ecohydrology Research Group co-authored an article, titled “Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials”, in collaboration with the researchers at the Vienna University, Centre for Microbiology and Environmental Systems Science, which was published in Environmental Science and Technology (ES&T). The article has been selected as one of the top cited articles in ES&T by the authors from Europe region from 2018-2020.
Today, many contaminants of emerging concern are polar and/or ionizable organic compounds, including pesticides, pharmaceuticals, and personal care products.For example, in 2010, approximately 50% of the industrial chemicals falling under European chemicals regulation were ionizable organic compounds; of these, 27% were acids, 14% were bases, and 8% were zwitterions. Carbonaceous sorbent materials, such as activated carbon, soot, biochar, and carbon nanotubes (CNTs), have been used for removal of these compounds from aquatic environments and in real-life applications such as, drinking water filtration systems, wastewater treatment plants, and soil and sediment remediation. The ability to predict contaminant sorption as a function of the properties of the sorbent thus would be of great advantage, as it would facilitate selection of both the appropriate sorbent and its quantity for a given application. To address this need, the authors made use of the available literature to develop two neural network-based models. Both models performed excellently in predicting the sorption of organic anions, cations, and zwitterions as well as polar compounds to a wide range of carbonaceous materials.
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