A robust triphenylamine-based monolithic polymer network for selective sieving of CO and PM from flue gas.

The increasingly urgent issue of climate change is driving the development of carbon dioxide (CO) capture and separation technologies in flue gas after combustion. The monolithic adsorbent stands out in practical adsorption applications for its simplified powder compaction process while maintaining the inherent balance between energy consumption for regeneration and selectivity for adsorption. However, optimizing the adsorption capacity and selectivity of CO separation materials remains a significant challenge. Herein, we synthesized monolithic polymer networks (N-CMPs) with triphenylamine adsorption sites, acid-base environment tolerance, and precise narrow microchannel pore systems for the selective sieving of CO and particulate matter (PM) in flue gas. The inherent continuous covalent bonding of N-CMPs, along with their highly delocalized π-π conjugated porous framework, ensures the stability of the monolithic polymer network's adsorption and separation capabilities under wet and acid-base conditions. Specifically, under the conditions of 1 bar at 273 K, the CO adsorption capacity of N-CMP-1 is 3.35 mmol/g. Attributed to the highly polar environment generated by triphenylamine and the inherent high micropore/mesopore ratio, N-CMPs exhibit an excellent ideal adsorbed solution theory (IAST) selectivity for CO/N under simulated flue gas conditions (CO/N = 15:85). Dynamic breakthrough experiments further visualize the high separation efficiency of N-CMPs in practical adsorption applications. Moreover, under acid-base conditions, N-CMPs achieve a capture efficiency exceeding 99.76 % for PM, enabling the selective separation of CO and PM in flue gas. In fact, the combined capture of hazardous PM and CO from the exhaust gases produced by the combustion of fossil fuels will play a pivotal role in mitigating climate change and environmental issues until low-carbon and alternative energy technologies are widely adopted.