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

Gabriel Sigmund; Mehdi Gharasoo; Thorsten Hüffer; Thilo Hofmann

doi:10.1021/acs.est.9b06287

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

Gabriel Sigmund, Mehdi Gharasoo, Thorsten Hüffer, Thilo Hofmann

Department für Umweltgeowissenschaften

Veröffentlichungen: Beitrag in Fachzeitschrift › Artikel › Peer Reviewed

Originalsprache	Englisch
Seiten (von - bis)	4583-4591
Seitenumfang	9
Fachzeitschrift	Environmental Science & Technology
Jahrgang	54
Ausgabenummer	7
DOIs	https://doi.org/10.1021/acs.est.9b06287
Publikationsstatus	Veröffentlicht - 7 Apr. 2020

ÖFOS 2012

105906 Umweltgeowissenschaften

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10.1021/acs.est.9b06287Lizenz: CC BY 4.0

https://phaidra.univie.ac.at/o:2093773Lizenz: CC BY 4.0

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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. / Sigmund, Gabriel; Gharasoo, Mehdi; Hüffer, Thorsten et al.
in: Environmental Science & Technology, Band 54, Nr. 7, 07.04.2020, S. 4583-4591.

Veröffentlichungen: Beitrag in Fachzeitschrift › Artikel › Peer Reviewed

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AU - Gharasoo, Mehdi

AU - Hüffer, Thorsten

AU - Hofmann, Thilo

PY - 2020/4/7

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KW - ADSORPTION

KW - AQUEOUS-SOLUTION

KW - THERMODYNAMICS

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KW - BIOCHAR

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JO - Environmental Science & Technology

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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

ÖFOS 2012

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