Engineering Insights into Tailored Metal-Organic Frameworks for CO 2 Capture in Industrial Processes.

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 (CO 2 ) into the atmosphere is evident. Therefore, innovation to address and reduce CO 2 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 CO 2 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 CO 2 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 [CO 2 and nitrogen (N 2 )] and mixed gas mixture (CO 2 and N 2 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 CO 2 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 CO 2 capture industrial processes is facilitated.