Disentangling Operating Conditions from Intrinsic Adsorbent Properties in CO₂ Capture: An Explainable Machine Learning Study Across Four Adsorbent Categories
This study employs explainable machine learning on a curated database of 398 CO₂ adsorption measurements to demonstrate that unequal testing pressure distributions across material families confound literature-based performance rankings, necessitating the decoupling of operating conditions from intrinsic properties to accurately identify key descriptors like BET surface area and pore volume.