Abstract
ADVERTISEMENT RETURN TO ISSUEPREVCorrespondence/Rebut...Correspondence/RebuttalNEXTORIGINAL ARTICLEThis notice is a correctionComment on Predicting Aqueous Adsorption of Organic Compounds onto Biochars, Carbon Nanotubes, Granular Activated Carbons, And Resins with Machine LearningGabriel SigmundGabriel SigmundDepartment of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Althanstrasse 14, 1090 Wien, AustriaMore by Gabriel Sigmundhttp://orcid.org/0000-0003-2068-0878, Mehdi GharasooMehdi GharasooEcohydrology Research Group, Department of Earth and Environmental Sciences, University of Waterloo, 200 University Av W, Waterloo, Ontario Canada N2L 3G1More by Mehdi Gharasoo, Thorsten HüfferThorsten HüfferDepartment of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Althanstrasse 14, 1090 Wien, AustriaMore by Thorsten Hüfferhttp://orcid.org/0000-0002-5639-8789, and Thilo Hofmann*Thilo HofmannDepartment of Environmental Geosciences, Centre for Microbiology and Environmental Systems Science, University of Vienna, Althanstrasse 14, 1090 Wien, Austria*Email: [email protected]More by Thilo Hofmannhttp://orcid.org/0000-0001-8929-6933Cite this: Environ. Sci. Technol. 2020, 54, 18, 11636–11637Publication Date (Web):August 25, 2020Publication History Published online25 August 2020Published inissue 15 September 2020https://doi.org/10.1021/acs.est.0c03931Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoicewith CC-BYlicenseArticle Views1950Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (697 KB) Get e-AlertsSUBJECTS:Sorption,Organic compounds,Sorbents,Machine learning,Materials Get e-Alerts
Highlights
Zhang et al.[1] published a paper on machine learning based predictions of organic contaminant sorption onto carbonaceous materials and resins
This study is a valuable contribution to the field that can stimulate the scientific discussion in the adsorption-modeling community regarding (i) mechanistic assumptions prior to model building, (ii) the parametrization of the model based on these assumptions, (iii) the grouping of data to train the algorithm, and (iv) data filtering strategies
(i) Zhang et al used the BET specific surface area and total pore volume to describe the sorbent materials and state that “these two parameters are critical for deciding the adsorption of organic compounds through hydrophobic interactions and pore-filling, two key mechanisms for organic compounds to be adsorbed by various adsorbents.”
Summary
Zhang et al.[1] published a paper on machine learning based predictions of organic contaminant sorption onto carbonaceous materials and resins. The authors provide a novel approach to predict concentration-dependent sorption distribution coefficients (KD) to these materials, without the need to link it to any specific isotherm model.
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