Dr Elena Velado-Alonso at the University of Göttingen has been awarded a Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship to launch “METAGROLAND: Understanding metacommunity dynamics through plant–pollinator interactions in agroecosystems to improve the efficiency of agri-environmental schemes.” Agri-environmental schemes (AES) are government-funded schemes set up to help farmers manage their land in an environmentally friendly way. The new 24-month project will investigate how AES can better sustain pollinator communities and the fundamental interactions that underpin plant reproduction and crop yields. Supported by the European Union’s Horizon Europe programme, METAGROLAND will develop tools to guide the design of more effective, large-scale AES and help build more resilient agricultural ecosystems.

A research team at The Hong Kong University of Science and Technology (HKUST) has developed an innovative urban food waste management framework by analyzing food waste data from 29 large cities worldwide, including Hong Kong, Beijing, and New York. The study shows that in cities with higher food waste moisture loads, such as Hong Kong, grinding food waste and diverting it into the sewage system is more effective than relying solely on landfilling. This approach can reduce overall greenhouse gas emissions by about 47% and lower total waste-management costs by about 11%. The research provides a new, quantitative basis for shaping food waste management strategies in cities around the globe.

Electrocatalysis sits at the heart of clean hydrogen production, fuel cells, and carbon dioxide conversion, yet progress toward scalable, high-performance catalysts has remained frustratingly slow. A growing body of research now suggests that artificial intelligence (AI) may be key to breaking this bottleneck—but only if it is used wisely. By reviewing three decades of AI applications in electrocatalysis, researchers reveal how the field has shifted from isolated data analysis toward end-to-end, data-driven discovery. The work highlights a critical turning point: AI is no longer just accelerating experiments, but beginning to reshape how electrocatalysts are designed, evaluated, and understood at a fundamental level.

Long-wave infrared (LWIR) microbolometers are essential for thermal imaging in harsh environments, yet their performance typically degrades at elevated temperatures. This study introduces a nanocomposite thermistor that combines two vanadium oxide phases to overcome this limitation. By engineering a heterointerface between conductive VO₂(B) and insulating V₂O₅, the material sustains high temperature sensitivity and fast infrared response even at 125 °C. The composite leverages interfacial charge transfer and photo-electron effects to maintain strong resistance changes under LWIR illumination. As a result, microbolometers based on this nanocomposite exhibit stable responsivity, low noise, and rapid response at temperatures where conventional materials fail, opening new possibilities for reliable thermal sensing in extreme operating conditions.

A research team led by Pengfei Guan from Advanced Interdisciplinary Science Research (AiR) Center, Ningbo Institute of Materials Technology and Engineering (CAS), has published a topic review focusing on harnessing machine learning for metallic glasses (MGs). Through an integrated assessment of pre-Kepler-era studies, researchers have identified that the alignment gap, between simulation and experiment, currently hinders the further application of machine learning. Tailored for closing theory-experiment loop, a revolutionary AI4S (AI for Science) framework based on domain-knowledge embedding is prototyped. Actionable strategies and quantifiable metrics are also prepared to navigate the data pipelines and reshape the research paradigm from “accelerated trial-and-error” into “truly unbiased and intelligent discovery”.