Scientists have unveiled a sunlight-powered catalyst that transforms methane—the main component of natural gas—directly into ethanol, a high-value liquid fuel and chemical feedstock. The new material, described today in Frontiers in Energy, achieves an apparent quantum efficiency of 9.4 %, a record for photocatalytic methane-to-ethanol conversion, while operating under near-ambient conditions.

To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

Pilot whale samples from 1986-2023 show that legacy PFAS are declining in the open ocean. Newer PFAS remain a major unknown and may be accumulating in near-source environments.

Professor Hirotoshi Mori (Department of Applied Chemistry, Faculty of Science and Engineering, Chuo University), together with Nichika Ozawa (first-year Ph.D. student at Ochanomizu University) and Assistant Professor Nahoko Kuroki of Ochanomizu University, has proposed a new design principle for QM/MM (quantum mechanics/molecular mechanics) simulations. The approach enables objective and automatic determination of the quantum-mechanical region based on electronic-state changes, addressing a long-standing challenge in multiscale molecular simulations. This work was published online in Advanced Science on December 23, 2025 (JST), an international journal that features conceptually innovative and interdisciplinary breakthroughs across materials science, life science, chemistry, and physics. This article will be published in issue 17 (March 23rd) of Advanced Science, and the cover design suggestion has been selected to be featured on a frontispiece in it. A frontispiece is a full-page image (similar to a cover) placed at the beginning of this article that highlights outstanding results.