Accelerating Discovery and Implementation of Effective Porous Liquids for CO2 Removal
Lead Research Organisation:
Imperial College London
Department Name: Chemical Engineering
Abstract
In this project, we aim to demonstrate the potential of automated high-throughput platforms, combined with insights from process modelling, in screening and identifying effective designer sorbents for gas separations. We will specifically focus on the removal of CO2 from CO2/N2 and CO2/CH4 gas mixtures using porous liquids as designer sorbents.
Research efforts in recent decades have led to an explosion of new sorbent materials or so-called "designer sorbents". The tunability of these sorbents mean that in theory, one could produce a performant sorbent for any given molecular separation. Yet, the tunability of these sorbents also represents a challenge: identifying the best sorbent for a given separation means screening hundreds-of-thousands of possible sorbents. Molecular modelling and process modelling can help narrow down the number of promising candidates. However, this number may still be large from an experimental standpoint, especially considering that each sorbent will require synthesis optimisation and upscaling. This is where automated high-throughput (HT) synthesis and characterisation platforms can help.
Research efforts in recent decades have led to an explosion of new sorbent materials or so-called "designer sorbents". The tunability of these sorbents mean that in theory, one could produce a performant sorbent for any given molecular separation. Yet, the tunability of these sorbents also represents a challenge: identifying the best sorbent for a given separation means screening hundreds-of-thousands of possible sorbents. Molecular modelling and process modelling can help narrow down the number of promising candidates. However, this number may still be large from an experimental standpoint, especially considering that each sorbent will require synthesis optimisation and upscaling. This is where automated high-throughput (HT) synthesis and characterisation platforms can help.
People |
ORCID iD |
Camille Petit (Primary Supervisor) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/X524773/1 | 30/09/2022 | 29/09/2027 | |||
2745194 | Studentship | EP/X524773/1 | 30/09/2022 | 30/03/2026 |