Persistent free radicals are stable chemical species embedded within biochar structures that dictate environmental reactivity, governing pollutant degradation while posing potential cellular oxidative stress risks. Despite their critical importance in environmental applications, the specific mechanisms through which simple elemental compositions dictate radical behavior have remained unclear. Existing mathematical models often rely on simple linear correlations that fail to capture the complex chemical transformations occurring across diverse biomassBiomass is a complex biological organic or non-organic solid product derived from living or recently living organism and available naturally. Various types of wastes such as animal manure, waste paper, sludge and many industrial wastes are also treated as biomass because like natural biomass these More feedstocks during heat treatment. This gap in understanding limits the ability to design biochars with targeted reactive properties or to properly evaluate potential environmental impacts prior to soil and water applications.

To address these predictive limitations, researchers assembled a comprehensive dataset of paired radical measurements and applied advanced machine learning techniques to map complex elemental relationships. Six distinct computational algorithms, including eXtreme Gradient Boosting, support vector regression, shallow neural networks, random forest, gradient boosting, and ensemble learning, were trained using basic elemental features including carbon, hydrogen, oxygen, hydrogen-to-carbon ratio, and oxygen-to-carbon ratio. The predictive outputs were subsequently decoded using Shapley additive explanations and partial dependence analysis to uncover non-linear feature interactions. Independent laboratory experiments using biochars synthesized from pine sawdust, rice straw, peanut hulls, cellulose, and lignin were conducted to experimentally validate the computational patterns.

The models successfully characterized radical properties, with shallow neural networks achieving top performance for both radical concentration and radical type identification. Feature importance evaluations revealed that hydrogen-to-carbon ratio and total oxygen content controlled radical concentration, while total oxygen and oxygen-to-carbon ratio governed radical classification. Decreases in hydrogen-to-carbon ratios and oxygen contents aligned with elevated radical concentrations, whereas increases in oxygen metrics promoted a structural shift toward oxygen-centered radicals. Independent experimental measurements confirmed these findings, with rank correlations between elemental descriptors and radical features matching the computational predictions.

Source: Gao, L., Xu, C., Li, M., Xu, Z., & Tao, W. (2026). Elemental composition-based prediction of persistent free radicals concentration and g-Factor in lignocellulose-derived biochar combining interpretable machine learning and experimental analysis. Biochar X, 2, e022.