As power systems evolve to accommodate rising levels of renewable energy, traditional grid-following converters (GFLs) are no longer sufficient to ensure grid stability. Researchers from Imperial College London and Tsinghua University, led by Dr. Fei Teng and Mr. Guoxuan Cui, conduct a comprehensive techno-economic analysis of how to determine the optimal penetration of grid-forming converters (GFMs) required for future power networks. Their findings highlight how coordinated planning and dynamic operational control strategies can enable cost-effective and stable operation of “new-type power systems”.

Physicists at Bielefeld University and the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden) have developed a method to control atomically thin semiconductors using ultrashort light pulses. The study, published in Nature Communications, could pave the way for components that are controlled at unprecedented speeds directly by light — ushering in a new generation of optoelectronic devices. 

Chemists from the National University of Singapore (NUS) have synthesised a new class of carbon nanostructures: fully π-conjugated, pentagon-embedded non-alternant carbon nanobelts (CNBs). This achievement addresses a long-standing challenge in molecular design and opens avenues for next-generation organic semiconductors and quantum materials.

A research team at Japan’s National Institutes for Quantum Science and Technology (QST) has demonstrated that electron beam (EB) irradiation can decompose polytetrafluoroethylene (PTFE) — a highly durable plastic known as Teflon — into gaseous components. This method drastically improves the energy efficiency compared to conventional recycling processes, offering a promising path toward reducing the environmental impact from per- and polyfluoroalkyl substances (PFAS).

Recent key advances in chemistry could tackle emissions from the world’s most polluting industries, according to a new study.

A research team at Shanghai Jiao Tong University has developed a systematic framework that combines semantic-segmentation AI models with experimental validation to analyze catalyst layers (CLs). Using silver nanoparticles as catalysts and Nafion ionomer as a binder, the team fabricated CLs with varying ionomer-to-catalyst (I/C) ratios (0.2, 0.4, and 0.6) to dissect how composition affects performance.