Machine learning for persistent free radicals in biochar: dual prediction of contents and types using regression and classification models
Junaid Latif,
Na Chen,
Azka Saleem
et al.
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… Show more
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