Unveiling the adsorption behaviour of nitrogen-doped porous carbons (NDPCs) towards carbon dioxide capture using machine learning techniques
Year: 2025
Type: article
Abstract: The urgent need to mitigate climate change has driven extensive research into effective carbon capture technologies. Among these, nitrogen-doped porous carbons (NDPCs) have emerged as a promising material for CO 2 capture due to their large surface area, tunable porosity, and nitrogen functionalities. This paper investigates the adsorption behaviour of NDPCs towards CO 2 using advanced machine learning (ML) techniques. These ML models are applied to different physicochemical properties of NDPCs to unveil the adsorption phenomenon of CO 2 towards NDPCs. Gradient boosting decision trees (GBDT) performed best, with training and test R 2 values of 0.99 and 0.88, respectively. Explainable machine learning (XML) has also been applied to find the intrinsic relation between NDPCs' properties and CO 2 uptake. Combined factor partial dependence plots revealed the optimal range of the features for the CO 2 uptake. Individual conditional expectation-partial dependence plots (ICE-PDPs) show the dependence of CO 2 uptake on each instance of input features. To examine the impact of input features on CO 2 uptake, Shapley Additive exPlanations (SHAP) have been employed. For nitrogen functionalities, the main impact came from pyridinic-N (N-6) and graphitic-N/quaternary-N (N-Q) compared to pyrolytic-N (N-5) and oxidized-N (N-X).
Institution: Indian Institute of Technology Roorkee
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,550
OpenAlex ID: W4415538360