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 Abstract

The proliferation of antibiotics in the environment poses a severe threat to public health and ecosystems. While biochar-based catalytic systems offer a promising remediation strategy, their design is complicated by nonlinear interactions between material properties and reaction conditions. To overcome this, we present an interpretable machine learning framework for the accurate prediction of reaction rate constants ( k ) for antibiotic degradation. A comprehensive dataset was compiled from the literature, encompassing 16 features across three domains including biochar catalyst properties, elemental composition, and reaction conditions. Among the six machine learning (ML) algorithms evaluated, the transformer-based deep learning model TabPFN (Tabular Prior-data Fitted Network) demonstrated superior performance (Test R ² = 0.91, RMSE = 0.021), outperforming tree-based, kernel-based, and neural network models. Model interpretability analyses revealed that catalyst properties contributed the largest share of predictive capability (59.3%), followed by reaction conditions (25.9%) and elemental composition (14.8%). Persistent free radicals (formed at 450–550 °C) and high total pore volume (> 0.23 cm ³ g ^{− 1} ) were identified as key drivers of reaction kinetics, along with optimal oxidant (0.5–5.5 mg L ^{− 1} ) and pollutant concentrations (< 22 mg L ^{− 1} ). These insights were embedded into a user-friendly web-based GUI, enabling rapid k prediction for new biochar catalysts with prediction errors below 20% on external validation samples. This work provides both a robust predictive tool and a generalizable, data-driven methodology for understanding and optimizing complex environmental catalytic processes. Graphical Abstract

Frozen regions sequester dissolved organic carbon (DOC) in ice, yet its transformation, especially coupled with other elements, remains unexplored. We found that freezing accelerated DOC transformation when coupled with nitrite, and the transformation pathway varies with carbon source. At pH 3.0, dissolved black carbon (DBC) underwent oxidative degradation with decreasing molecular weight and aromaticity, whereas leached DOC (LDOC) exhibited humification, increasing the humification index from 0.38 to 0.65. The difference arises from nitrite protonation to HNO₂ and NO⁺ under the strongly acidic conditions induced by the freeze concentration effect. HNO₂ amplified the oxidative capacity of H₂O₂ for DBC decomposition, where H₂O₂ concentration in the DBC system was 6.9-fold higher than LDOC. However, NO⁺ converts electron-rich phenols within LDOC (2.65-fold higher than DBC) into phenyl radicals, triggering polymerization to humus. These findings reveal different nitrite-induced DOC turnover during freezing, providing valuable insights into carbon source-sink dynamics during carbon-nitrogen coupling in ice. The alternative text for this image may have been generated using AI.