Machine learning and experimentally exploring the controversial role of nitrogen in CO₂ uptake by waste-derived nitrogen-containing porous carbons

Waste-derived nitrogen-containing porous carbons were widely accepted as promising carbon capture materials. However, roles of nitrogen in CO₂ uptake were highly controversial, posing a challenge in designing high CO₂ uptake porous carbons. Herein, nitrogen-containing species was firstly introduced into machine learning (ML) models to uncover the complex relationship of nitrogen, micropore and CO₂ uptake by combining ML models, DFT computations and experiments. The results revealed that micropore volume (V_micro) was the most important property influencing CO₂ uptake, but was not the only determinant factor. Nitrogen-containing species (pyrrolic/pyridonic-N (N₅) and pyridinic-N (N₆)) rather than total nitrogen content, also played an essential role. On the one hand, they can enhanced CO₂ adsorption by Lewis acid-base and hydrogen bonding. On the other hand, they promoted development of micropores by participating in activation reactions. The model further indicated that excessive N₅ (>1.5 wt%) or N₆ (>1.7 wt%) led to restriction on developments of micropores, which was attributed to enlargement of pore size, collapses or blockage of micropores. The double edged-sword effect of N₅ and N₆ on changes of microporous structures was responsible for the long-standing controversy over nitrogen. The result was further verified by synthesizing eight porous carbons with different textural and chemical properties. This study provided not only a new perspective for resolving the controversy of nitrogen in CO₂ uptake, but also a graphical user interface prediction software meaningful for designing porous carbons.