Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials-Reference-Cited by-同舟云学术

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

Published:2020-03-03 Issue:7 Volume:54 Page:4583-4591
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Sigmund Gabriel¹²³^ORCID,Gharasoo Mehdi⁴,Hüffer Thorsten¹^ORCID,Hofmann Thilo¹^ORCID

Affiliation:

1. Department of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Althanstrasse 14, 1090 Wien, Austria

2. Agroscope, Environmental Analytics, Reckenholzstrasse 191, CH-8046 Zurich, Switzerland

3. Ithaka Institute, Ancienne Eglise 9, 1974 Arbaz, Switzerland

4. Department of Earth and Environmental Sciences, Ecohydrology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada

Funder

Bundesministerium f?r Nachhaltigkeit und Tourismus

Canada First Research Excellence Fund

Global Water Features program

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.est.9b06287

Reference69 articles.

1. Linear Free Energy Relationships Used To Evaluate Equilibrium Partitioning of Organic Compounds