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In silico discovery of metal-organic frameworks for precombustion CO₂ capture using a genetic algorithm.

ABSTRACT: Discovery of new adsorbent materials with a high CO₂ working capacity could help reduce CO₂ emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO₂ capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO₂ working capacity and a high CO₂/H₂ selectivity. One of the synthesized MOFs shows a higher CO₂ working capacity than any MOF reported in the literature under the operating conditions investigated here.

SUBMITTER: Chung YG

PROVIDER: S-EPMC5065252 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm.

Chung Yongchul G YG Gómez-Gualdrón Diego A DA Li Peng P Leperi Karson T KT Deria Pravas P Zhang Hongda H Vermeulen Nicolaas A NA Stoddart J Fraser JF You Fengqi F Hupp Joseph T JT Farha Omar K OK Snurr Randall Q RQ

Science advances 20161014 10

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 cap ...[more]

PMID: 27757420

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Project description:Despite the known impacts on climate change of carbon dioxide emissions, the continued use of fossil fuels for energy generation leading to the emission of carbon dioxide (CO2) into the atmosphere is evident. Therefore, innovation to address and reduce CO2 emissions from industrial operations remains an urgent and crucial priority. A viable strategy in the area is postcombustion capture mainly through absorption by aqueous alkanolamines, which focuses on the separation of CO2 from flue gas, despite its limitations. Within this context, porous materials, particularly metal-organic frameworks (MOFs), have arisen as favorable alternatives owing to their significant adsorption capacity, selectivity, and reduced regeneration energy demands. This research evaluates the engineering insights into tailored MOFs for enhanced CO2 capture, focusing on three series of MOFs (ZIF, UiO-66, and BTC) to investigate the effects of organic ligands, functional groups, and metal ions. The evaluation encompassed a range of aspects including adsorption isotherms of pure gases [CO2 and nitrogen (N2)] and mixed gas mixture (CO2 and N2 with 15:85% ratio), along with utilization of the ideal adsorbed solution theory (IAST) to simulate multicomponent gas adsorption isotherms. Moreover, the reliability of IAST for mixed gas adsorption prediction has been investigated in detail. The research offers valuable insights into the correlation between the characteristics of MOFs and their effectiveness in gas separation and how these characteristics contribute to the differences between IAST predictions and experimental results. The findings enhance the understanding of how to enhance MOF characteristics in order to reduce CO2 emissions and also highlight the need for advanced models that consider thermodynamic nonidealities to accurately predict the behavior of mixed gas adsorption in MOFs. As a result, the incorporation of MOFs with enhanced predictability and reliability into CO2 capture industrial processes is facilitated.

Dataset Information

In silico discovery of metal-organic frameworks for precombustion CO₂ capture using a genetic algorithm.

Publications

In silico discovery of metal-organic frameworks for precombustion CO<sub>2</sub> capture using a genetic algorithm.

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