Stainless steels are basic corrosion-resistant materials, but they still suffer from environmental erosions by ubiquitous chemical reactions, resulting in typical corrosion rates at dozens of mm∙yr^-1. Here a strong-field laser passivation strategy is proposed, with which up to 100,000-fold reduction in the corrosion rates of AISI 304 is achieved respectively in saline, acidic, and alkaline solutions. The generality is exemplified by exhibiting the similar anticorrosion enhancements for AISI 316, 420, 201, 430 and 2205 steels.

This protocol provides a detailed guide for single-molecule tracking (sptPALM) in living microbial cells, specifically focusing on E. coli and Saccharomyces cerevisiae. It outlines the steps for sample preparation, data acquisition, and data processing, enabling researchers to visualize and quantify the dynamics of individual molecules within living cells.

A research team from Wuhan University has developed an innovative machine learning framework that accelerates the discovery of materials with tailored thermal properties. By combining interpretable deep learning with multiscale computational techniques, the team achieved highly accurate predictions of lattice thermal conductivity (LTC) while also shedding light on the underlying physical mechanisms. Their approach maintains the predictive power of traditional "black-box" models but adds the physical interpretability. As result, several high-performance materials for thermal management were identified. This advancement not only speeds up the development of efficient thermoelectric devices and thermal control systems but also provides deeper insights into the fundamental process of heat transfer at the atomic scale.

Recently, a review study led by Professor Xuejun Liu from the College of Resources and Environmental Sciences at China Agricultural University and Tianxiang Hao et al. systematically analyzed the current status of China’s farmland carbon budget, providing a scientific solution to this dilemma. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025602).

Recently, Guoqin Huang from Jiangxi Agricultural University and Kai Huang from Guangxi Hydraulic Research Institute systematically reviewed the multiple benefits of sugarcane intercropping in optimizing soil fertility. Their findings offer new insights for ecological restoration of dry sloping farmland in southern China. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025612).

The research team led by Liang'an Chen from Nanjing Normal University and Liangliang Song from Nanjing Forestry University has made a new breakthrough in the field of conjugated diene chemistry. Based on the neutral reaction mechanism of oxidative addition-reductive elimination and utilizing a ligand angle control strategy, they achieved palladium-catalyzed regio-, chemo-, and stereoselective coupling of propargyl alcohol esters with a variety of nucleophiles (including fluorides, phenols, alcohols, carboxylic acids, and amides), constructing a series of high-value-added multi-substituted conjugated dienes. The practicality of this method has been demonstrated through the application and transformation of these functionalized 1,3-dienes in cycloaddition and coupling reactions, as well as the subsequent modification of natural products and bioactive molecules. The article was published as an open access research article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

What if we told you that the secret to healthier soil, cleaner ecosystems, and smarter farming isn’t buried in a high-tech lab—but hidden in the data behind crop residues, wood chips, and food waste?

Meet the future of sustainable agriculture: a powerful new machine learning tool that can predict exactly how much biochar—a carbon-rich, soil-boosting material—can be made from any type of biomass, and how much nitrogen, phosphorus, and potassium it will contain. No crystal ball needed. Just smart science, powered by data.