Machine learning for persistent free radicals in biochar: dual prediction of contents and types using regression and classification models

Abstract

Persistent free radicals (PFRs) are emerging substances with diverse impacts in biochar applications, necessitating accurate prediction of their content and types prior to their optimal use and minimal adverse effects. This prediction task is challenging due to the nonlinearity and intricate variable relationships of biochar. Herein, we employed data-driven techniques to compile a dataset from peer-reviewed publications, aiming to systematically predict the PFRs by developing supervised machine learning models. Notably, extreme gradient boosting (XGBoost) model exhibited the best predictive performance for both regression and classification tasks in predicting the PFRs, achieving a test R2 value of 0.95 for PFR content prediction, along with an Area Under the Receiver Operating Curve (AUROC) of 0.92 for PFR type prediction, respectively. Based on XGBoost model, a graphical user interface (GUI) was developed to access PFRs predictions. Analysis of feature importance revealed that the biochar properties, such as metal/non-metal doping, pyrolysis temperature, carbon content, and specific surface area were identified as the four most significant factors influencing PFRs contents. Regarding the types of PFRs in biochar, specific surface area, pyrolysis temperature, carbon content, and feedstock were top-ranked influencing factors. These findings provide valuable guidance for accurately predicting both the contents and types of PFRs in biochar, and also hold significant potential for highly efficient utilization of biochar across various applications.Graphical