Biomass carbon material has good potential as an anode for potassium-ion batteries (KIBs). However, it suffers from a low initial coulombic efficiency (ICE), which hinders its mass adoption. The formation of large-sized graphitic structures in biomass carbon can improve ICE. However, the commonly reported methods are still inadequate for realising this due to the random distribution of small-sized amorphous carbon domains and the existence of defective structures. Herein, a facile method is reported to prepare biomass carbon with long-distance and large-sized graphitic structures by co-annealing the precursor (tea-waste) powder and recyclable template pellet (petroleum coke pre-loaded with Fe₂O₃ particles) at 1300 °C for catalytic graphitization. This process can allow the embedding of the external graphitic crystals into the defects of the biomass carbon. The hybrid framework is useful for reducing the irreversible K-ion trapping by increasing the amount of reversible insertion sites, providing more reversible K-ion transportation channels, shortening the diffusion distance in the carbon interlayers, and enhancing reversible K-ion adsorption at the defects/edges. The as-prepared biomass carbon delivered a high ICE of 85.6% and a high reversible capacity of 367 mA h g⁻¹. Also, based on the result, the as-prepared biomass carbon could exhibit good compatibility as an electrode for a sodium-ion battery with an ICE of 92.3% and a reversible capacity of 408 mA h g⁻¹. Therefore, utilizing a soft carbon pellet loaded with a catalyst as a recyclable template can be highly beneficial in enhancing the ICE for KIBs and other alkali-ion batteries.

Fast economic development, burgeoning population growth, and rapid urbanization have led to complex pollution sources contributing to water quality deterioration simultaneously in many developing countries including China. This paper explored the use of spatial regression to evaluate the impacts of watershed characteristics on ambient total nitrogen (TN) concentration in a heavily polluted watershed and make predictions across the region. Regression results have confirmed the substantial impact on TN concentration by a variety of point and non-point pollution sources. In addition, spatial regression has yielded better performance than ordinary regression in predicting TN concentrations. Due to its best performance in cross-validation, the river distance based spatial regression model was used to predict TN concentrations across the watershed. The prediction results have revealed a distinct pattern in the spatial distribution of TN concentrations and identified three critical sub-regions in priority for reducing TN loads. Our study results have indicated that spatial regression could potentially serve as an effective tool to facilitate water pollution control in watersheds under diverse physical and socio-economical conditions.