POSTECH, the University of Wisconsin–Madison, and the University of Tokyo control oxide electronic structures via local atomic arrangements at moiré interfaces.

Seeing chemistry unfold inside living cells is one of the biggest challenges of modern bioimaging. Raman microscopy offers a powerful way to meet this challenge by reading the unique vibrational “signatures” of molecules. However, cells are extraordinarily complex environments filled with thousands of biomolecules. To make specific molecules stand out, researchers often attach small chemical “probes,” such as alkyne tags, that produce signals in a so-called cell-silent spectral window where native cellular components do not scatter light. This allows Raman microscopes to selectively detect the tagged molecules against an otherwise crowded molecular background. Despite this advantage, the widespread adoption of Raman microscopy in biology has been limited by one fundamental problem: Raman signals are extremely weak. In a new study published in Angewandte Chemie International Edition, TIFR researchers report a molecular design strategy that dramatically amplifies Raman signals, enabling the development of Raman sensors that can detect biomolecules using standard spontaneous Raman microscopes. These new Raman sensors enable sensitive, ratiometric imaging of biological molecules and ions, such as hydrogen peroxide molecules, copper ions, and pH, inside living cells. The work is supported by the Department of Atomic Energy, Government of India, and the Japan Science and Technology Agency, with the research team comprising Prof. Ankona Datta and Sujit Das from the Tata Institute of Fundamental Research, Mumbai, and Heqi Xi, Itsuki Yamamoto, and Prof. Katsumasa Fujita from the University of Osaka, Japan.

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.

Antibiotics that save lives can also become hidden environmental threats once they leave hospitals and households. A new study reports an innovative solution that transforms sewage waste into a low cost, eco friendly sensor capable of detecting trace levels of the widely used antibiotic trimethoprim in water, urine, and pharmaceutical samples.

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.