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Cascade Reactions for Enhanced CO2 Capture: Concurrent Optimization of Porosity and N-Doping
Journal article   Peer reviewed

Cascade Reactions for Enhanced CO2 Capture: Concurrent Optimization of Porosity and N-Doping

Hao Li, Jia Bin Niu, Long Gang Tao, Mei Chee Tan and Hong Yee Low
Advanced functional materials, Vol.35(7), p.n/a
12/02/2025

Abstract

Chemistry Chemistry, Multidisciplinary Chemistry, Physical Materials Science Materials Science, Multidisciplinary Nanoscience & Nanotechnology Physical Sciences Physics Physics, Applied Physics, Condensed Matter Science & Technology Science & Technology - Other Topics Technology
Carbon capture emerges as a pivotal decarbonization technology for addressing global warming challenges. Porous carbons, despite their cost-effectiveness and ease of regeneration for CO2 capture, typically exhibit limited capacity owing to insufficient adsorption sites. Here, nitrogen-doped porous carbons (NPCs) are introduced that overcome the prevalent trade-offs between specific surface area and N-doped content in NPCs fabrication through cascade reactions. The optimized NPC, which features hierarchical porosity ranging from ultra-micropores to macropores, shows a superior CO2 capture capacity of 4.46 mmol g(-1), ranking in the top 10% of the reported NPCs. This capacity exceeds that of the NPC fabricated with the conventional method by 58% and surpasses the control porous carbon by 106%. Langmuir adsorption modeling and mathematic correlation analysis revealed that this enhanced capacity is attributed to significantly improved ultra-micropores volume and nitrogen-species content. Moreover, this optimized NPC demonstrates exceptional stability, preserving its adsorption performance over 110 adsorption-desorption cycles under simulated flue gas conditions. This research not only highlights the integration of templating and N-doping within NPCs fabrication but also offers an effective strategy to optimize porosity and nitrogen functionality in carbon materials, advancing beyond conventional methodologies.
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https://doi.org/10.1002/adfm.202415441View
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