Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation

Author:

Pilz Lena1ORCID,Natzeck Carsten1ORCID,Wohlgemuth Jonas1ORCID,Scheuermann Nina1,Spiegel Simon1ORCID,Oßwald Simon23ORCID,Knebel Alexander4ORCID,Bräse Stefan23ORCID,Wöll Christof1ORCID,Tsotsalas Manuel15ORCID,Prasetya Nicholaus1ORCID

Affiliation:

1. Institut für Funktionelle Grenzflächen (IFG), Karlsruhe Institute of Technology (KIT), Campus Nord, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany

2. Institut für Biologische und Chemische Systeme - Funktionelle Molekulare Systeme (IBCS-FMS), Karlsruhe Institute of Technology (KIT), Campus Nord, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344, Germany

3. Institut für Organische Chemie (IOC), Karlsruhe Institute of Technology (KIT), Campus Süd, Fritz-Haber-Weg 6, D-76131, Karlsruhe, Germany

4. Friedrich Schiller University Jena, Fraunhoferstr. 6, 07743, Jena, Germany

5. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA

Abstract

In this study, machine learning has been used to assist the fabrication of high-quality SURMOFs that are then further used as a template to fabricate polymer-based SURGEL membranes for gas separation.

Funder

Helmholtz-Gemeinschaft

Alexander von Humboldt-Stiftung

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

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