Enhancing Biochar-Based Nonradical Persulfate Activation Using Data-Driven Techniques

Abstract

Converting biomass into biochar (BC) as a functional biocatalyst to accelerate persulfate activation for water remediation has attracted much attention. However, due to the complex structure of BC and the difficulty in identifying the intrinsic active sites, it is essential to understand the link between various properties of BC and the corresponding mechanisms promoting nonradicals. Machine learning (ML) recently demonstrated significant potential for material design and property enhancement to help tackle this problem. Herein, ML techniques were applied to guide the rational design of BC for the targeted acceleration of nonradical pathways. The results showed a high specific surface area, and O% values can significantly enhance nonradical contribution. Furthermore, the two features can be regulated by simultaneously tuning the temperatures and biomass precursors for efficient directed nonradical degradation. Finally, two nonradical-enhanced BCs with different active sites were prepared based on the ML results. This work serves as a proof of concept for applying ML in the synthesis of tailored BC for persulfate activation, thereby revealing the remarkable capability of ML for accelerating bio-based catalyst development.