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Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wa...

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Published in:Environmental science & technology 2020-04, Vol.54 (7), p.4583-4591
Main Authors: Sigmund, Gabriel, Gharasoo, Mehdi, Hüffer, Thorsten, Hofmann, Thilo
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Language:English
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description Most contaminants of emerging concern are polar and/or ionizable organic compounds, whose removal from engineered and environmental systems is difficult. Carbonaceous sorbents include activated carbon, biochar, fullerenes, and carbon nanotubes, with applications such as drinking water filtration, wastewater treatment, and contaminant remediation. Tools for predicting sorption of many emerging contaminants to these sorbents are lacking because existing models were developed for neutral compounds. A method to select the appropriate sorbent for a given contaminant based on the ability to predict sorption is required by researchers and practitioners alike. Here, we present a widely applicable deep learning neural network approach that excellently predicted the conventionally used Freundlich isotherm fitting parameters log K F and n (R 2 > 0.98 for log K F, and R 2 > 0.91 for n). The neural network models are based on parameters generally available for carbonaceous sorbents and/or parameters freely available from online databases. A freely accessible graphical user interface is provided.
doi_str_mv 10.1021/acs.est.9b06287
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source American Chemical Society:Jisc Collections:American Chemical Society Read & Publish Agreement 2022-2024 (Reading list)
subjects Activated carbon
Carbon nanotubes
Carbonaceous materials
Charcoal
Contaminants
Deep learning
Drinking water
Fullerenes
Graphical user interface
Machine learning
Mathematical models
Nanotechnology
Nanotubes
Neural networks
Organic compounds
Parameters
Pollutants
Sorbents
Sorption
Wastewater pollution
Wastewater treatment
Water filtration
Water pollution
Water purification
Water treatment
title Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials
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