Publication 2020: Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials
Section 1: Publication
Authorship
Sigmund, G., Gharasoo, M., Hüffer, T., & Hofmann, T.
Title
Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials
Year
2020
Publication Outlet
Environmental Science & Technology, 54 (7), 4583-4591
DOI
Citation
Sigmund, G., Gharasoo, M., Hüffer, T., & Hofmann, T. (2020) Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials, Environmental Science & Technology, 54 (7), 4583-4591.
https://doi.org/10.1021/acs.est.9b06287.
Additional Information
Core Modelling & Forecasting Team
Section 4: Computed Information
AZLine
SIGMUND-G-GHARASOO-M-HüFFER-T-HOFMANN-T-2020-DEEP-LEARNING-NEURAL-NETWORK-APPROACH-FOR-PREDICTING-THE-SORPTION-OF-IONIZABLE-AND-POLAR-ORGANIC-POLLUTANTS-TO-A-WIDE-RANGE-OF-CARBONACEOUS-MATERIALS-ENVIRONMENTAL-SCIENCE-TECHNOLOGY-54-7-4583-4591-HTTPSDOIORG101021ACSEST9B06287
AZHash
9de290c1a71a553d665311be82dc3de0