A semiconductor–metal synergistic interface design via in situ engineering of a Bi/BiOCl heterostructure on Zn anodes was presented. This dual–functional heterointerface enables unprecedented electrochemical performance, including: (i) stable cycling for 2500 h at 10 mA cm^–2 in symmetric cells; (ii) 1000 cycles at 10 A g^–1 for the Zn@Bi/BiOCl//dibenzo[b,i]thianthrene–5,7,12,14–tetraone (DTT) full battery, and 15,000 cycles at room temperature and 7500 cycles at –20 °C for the Zn@Bi/BiOCl//activated carbon (AC) hybrid ion capacitor (HIC), outperforming most reported AZIBs. This breakthrough originates from a dual–functional synergy: Bi nanoparticles serve as zincophilic nucleation guides to expedite homogeneous Zn²⁺ deposition, while the BiOCl semiconductor establishes a built–in electric field with Zn to redistribute interfacial ion/charge flux and elevate the hydrogen evolution barrier. This coordinated regulation simultaneously inhibits Zn dendrite formation, HER, and Zn corrosion, imparting promising applications for Zn anodes in AZIBs. Our work not only resolves the long–standing interfacial instability of Zn anodes but also pioneers a semiconductor–metal heterojunction strategy, offering a universal platform for designing dendrite–free metal batteries operable under extreme thermal and rate conditions.

In recent years, digital agricultural technology extension services (DATES), leveraging Internet platforms such as WeChat official accounts and mobile applications, have gained popularity, providing a new pathway for agricultural technology dissemination. This service overcomes the temporal and spatial limitations of traditional agricultural technology extension, enabling farmers to conveniently access planting knowledge. Then, can DATES effectively encourage farmers to adopt OMF and contribute to the green transformation of agriculture? Professor Minjuan Zhao from the College of Economics and Management, Northwest A&F University, and her team addressed this question through a survey of farmers in major apple - producing areas in China. The related research has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024590).

The POINT platform (http://point.gene.ac/) integrates multi-omics biological networks, advanced network topology analysis, deep learning prediction algorithms, and a comprehensive biomedical knowledge graph. It provides a powerful tool to overcome current bottlenecks in network pharmacology and advance the field.

A research team led by Professor Yumei Zhang from the Academy of Global Food Economics and Policy (AGFEP) at China Agricultural University, in collaboration with Dr. Issa Ouedraogo from the Alliance of Biodiversity International and International Center for Tropical Agriculture (CIAT), has conducted a study that reveals the structural characteristics and emission reduction potential of agrifood system carbon emissions in China and Africa. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025609).

Researchers from the Hong Kong University of Science and Technology (HKUST) and Tongji University have developed FerroAI, a deep learning model that can produce phase diagrams for ferroelectric materials in just 20 seconds. Using FerroAI, the team discovered a novel ferroelectric material with an exceptionally high dielectric constant of 11,051, representing a significant advancement in AI-driven ferroelectric materials research. Their findings, published in the prestigious journal npj Computational Materials, offer an innovative approach to accelerating the design and discovery of functional materials.

Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.