NIMS developed TEGNet (Thermoelectric Generator Neural Network), a neural network for designing thermoelectric generators by utilizing artificial intelligence (AI). TEGNet can predict performance of a power generator, a process which used to take enormous computational time with traditional simulation techniques, with only about 1/10,000 of the time conventionally needed, while maintaining over 99% accuracy. This technology significantly accelerates optimization from material development to device design, and is expected to be applied to waste heat recovery and stand-alone power supply for IoT sensors, for example. This research result was published in Nature at 11:00 U.S. Eastern Standard Time, April 15, 2026 (0:00 Japan Standard Time, April 16, 2026).

SAN ANTONIO — May 18, 2026 — Looking back at 14 years of Hubble telescope data for Jupiter’s moon Europa has given Southwest Research Institute (SwRI) scientists a better understanding of its tenuous atmosphere. The findings have cast doubt on previous evidence suggesting that the icy moon intermittently discharges faint water plumes from a presumed subsurface ocean.

Aqueous zinc-ion batteries (ZIBs) have attracted significant interest as safe, low-cost, and environmentally friendly energy storage systems. However, their performance and stability are limited by complex interfacial phenomena such as zinc dendrite growth, parasitic side reactions, and the evolution of the solid electrolyte interphase. These processes are inherently dynamic and span multiple spatial and temporal scales, posing challenges to traditional ex situ characterization techniques. To address this, advanced in situ and operando techniques have been developed, broadly categorized into imaging, spectroscopic, synchrotron scattering/diffraction, and coupled mass spectrometry approaches. These methods enable real-time visualization and chemical analysis of the electrode/electrolyte interface, providing insights into nucleation and dissolution dynamics, interfacial chemical transformations, and the mechanisms driving dendrite formation and parasitic reactions. Through the integration of these complementary techniques, structural evolution can be correlated with electrochemical behavior, elucidating the underlying physicochemical mechanisms. This review systematically summarizes recent advances in in situ and operando characterization methods and highlights their contributions to understanding interfacial stability in aqueous ZIBs. Future directions emphasizing multi-modal strategies and data integration to guide the rational design of high-performance ZIBs are discussed. These insights are expected to accelerate the development of next-generation aqueous energy storage systems.

The resonant trident process (Oleinik's resonances) in the field of a strong electromagnetic wave and ultrarelativistic initial and final particles has been theoretically studied. Resonant kinematics and resonant differential and total rates are obtained. Published in Quantum Research. The results obtained can be used to describe experiments at high-intensity lasers centers, as well as in astrophysics near neutron stars and supernovae.

Water is not a magical information storage medium — but its short-lived molecular order can have very real physical consequences. A TU Wien study, together with the University of Vienna and the University of Oslo, shows that the way water arranges itself around ions helps determine whether charged particles stick to surfaces, a key process in electrochemistry, batteries, catalysts and biological membranes. The results, published in Science Advances, provide a quantitative model linking ion adsorption, entropy and the microscopic structure of water.

Rising carbon dioxide emissions pose a major global challenge. Electrochemical CO₂ reduction using copper-based electrocatalysts offers a promising and sustainable route to convert CO₂ into valuable multi-carbon fuels and chemicals. However, achieving high stability and selectivity remains difficult. Researchers have now examined advanced catalyst design strategies that integrate atomic-level engineering, machine learning and in situ analysis to enhance performance and enable scalable carbon recycling systems significantly.

Reliable water supply in large canal systems is often compromised by unpredictable lateral offtake discharges.

Metal–amide chemistry provides a rational approach to controlling heavy-pnictogen reduction, paving the way for safer and more scalable semiconductor quantum dots.