Energy utilization of agricultural waste: Machine learning prediction and pyrolysis transformation.

The rapid screening of agricultural waste materials for capacitor preparation holds significant importance in comprehending the relationship between material properties and enhancing experimental efficiency. In this study, we developed two machine learning models to predict electrode material characteristics using 2997 data points extracted from 235 articles. The identification and influence of key features on prediction indices provide a theoretical foundation for subsequent practical preparation.

Insight into the soil aggregate-mediated restoration mechanism of degraded black soil via biochar addition: Emphasizing the driving role of core microbial communities and nutrient cycling.

Soil microbial communities are responsive to biochar application. However, few studies have investigated the synergistic effects of biochar application in the restoration of degraded black soil, especially soil aggregate-mediated microbial community changes that improve soil quality. From the perspective of soil aggregates, this study explored the potential microbial driving mechanism of biochar (derived from soybean straw) addition in black soil restoration in Northeast China.