Technical Paper Archive


This site contains all papers presented at Rubber Division, ACS Technical Meetings from 2000-2018. Meetings prior to 2000 are incomplete and papers are added as they are digitized.


For older papers not listed here, please send your request, including Meeting Year, Title, Author, and Paper # to: cbrady@rubber.org


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High-throughput Computational Screening of CO2-philic Functional Groups for Selective CO2/N2 Gas Separation

A18 vogiatzis paper

Event: 198th Technical Meeting - Fall 2020
Location: Virtual
Date: October 19, 2020
Author: Konstantinos Vogiatzis
Paper Number: A18

Price: $20.00

  • In post-combustion carbon capture, CO2 is separated from N2, a process that is performed conventionally with aqueous amines to effectively bind CO2. However, these solvents interact strongly with CO2 and must be scrubbed with large amounts of heat to regenerate the solvent by removing CO2, but the regeneration of the solvent is an energetically expensive process. Passive non-porous polymeric membranes offer an alternative, cost-effective technology for CO2 capture. Noncovalent interactions allow the CO2 molecules to diffuse through the membrane while N2 molecules do not interact with the polymeric matrix and do not permeate the membrane, which results in a lower energy cost separation. The interaction of CO2 with the polymer is one of the most important components, where CO2–philicity can be enhanced by introducing in the material certain functional groups (usually, Lewis bases). We have performed computational studies on individual families of functional groups introduced in the repeating units of polymeric materials. Recently, we developed a novel molecular fingerprinting method based on persistent homology that can encode the geometrical and electronic structure of molecules for chemical applications. We have demonstrated its applicability on interactions between functional groups of materials and CO2/N2 gas molecules. Our quantum chemical calculations were performed on a small number of molecules (~200) for the generation of meaningful data. We have used these data in order to train a machine learning model for high-throughput virtual screening by predicting the properties of larger molecular databases (more than 130,000 entries) where quantum chemical data are not available.

    Papers from 198th Technical Meeting - Fall 2020

    Items from 198th Technical Meeting - Fall 2020