Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration-Reference-Cited by-同舟云学术

Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration

Published:2022-03 Issue: Volume:646 Page:120268
ISSN:0376-7388
Container-title:Journal of Membrane Science
language:en
Short-container-title:Journal of Membrane Science

Author:

Ignacz Gergo^ORCID,Szekely Gyorgy^ORCID

Funder

King Abdullah University of Science and Technology

Publisher

Elsevier BV

Subject

Filtration and Separation,Physical and Theoretical Chemistry,General Materials Science,Biochemistry

Reference73 articles.

1. Molecular separation with organic solvent nanofiltration: a critical review;Marchetti;Chem. Rev.,2014

2. Sustainability assessment of organic solvent nanofiltration: from fabrication to application;Szekely;Green Chem.,2014

3. Advances in organic solvent nanofiltration rely on physical chemistry and polymer chemistry;Galizia;Front. Chem.,2018

4. Membrane research beyond materials science;Beuscher;J. Membr. Sci.,2021

5. Reporting the unreported: the reliability and comparability of the literature on organic solvent nanofiltration;Le Phuong;Green Chem.,2020

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