Nitrate pollution has become one of the most widespread water quality challenges in intensively farmed regions around the world, threatening drinking water safety, aquatic ecosystems, and downstream lakes. A new study published in Nitrogen Cycling reveals how human activities in rural urban transition zones are reshaping the nitrogen cycle, allowing nitrate to move through rivers and groundwater and ultimately reach large freshwater lakes.

In the field of polyoxometalate chemistry, organophosphonate covalently modified polyoxometalates have recently emerged as a promising frontier. These hybrid materials not only broaden the structural diversity of conventional polyoxometalate derivatives and address the inherent stability limitations of polyoxometalates, but also allow for the design of improved properties tailored to diverse applications. This review provides a comprehensive summary of recent advances in organophosphonate covalently modified polyoxometalates research, with a particular focus on their structural features, functional properties, and prospective research directions.

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to underwater cables—and is critical for safe renewable energy development. Traditional numerical simulations and experiments require enormous computational resources, yet often fail to capture multiscale turbulence and long-term system behavior. This review highlights how machine learning (ML) is emerging as a powerful solution for analyzing, predicting, and even controlling FSI systems. Key progress spans feature detection, reduced-order modeling, physics-informed neural networks, and reinforcement-based flow control. By leveraging data-driven models to extract hidden patterns and reconstruct flow fields, ML shows promise in improving efficiency, predictive accuracy, and automated control across ocean engineering applications, positioning itself as a transformative tool for next-generation design.

Polyoxometalates are promising inorganic drugs with antiviral activity; however, they pose a risk to humans because of their potential accumulation in the body. Polyoxometalates encapsulated with berberine from a traditional Chinese herb may exhibit lower cytotoxicity. In this study, the antiviral effects of four berberine-based organic–polyoxometalate hybrids (BR-POMs) on BHK-21 and PK-15 cells were evaluated in vitro using encephalomyocarditis virus (EMCV) or pseudorabies virus (PRV) models. The collected cells were used for quantitative polymerase chain reaction analysis. The supernatants were collected to quantify the viral loads using a TCID50 assay in vitro. EC50 and CC50 were determined through dose–response experiments, and the EC50/CC50 ratio was used as a selectivity index to measure the antiviral activity. The results demonstrate that all BR-POMs exhibited certain antiviral activity. The BR-POMs did not exert toxicity against the EMCV- or PRV-infected cells at the tested concentration (CC50 > 40 μM). Notably, BR-EuSiW (EC50 15.07 μM, CC50 651.2 µM, SI 43.21) exerted antiviral effects by acting on the virus at its biosynthesis stage, thereby inhibiting virus proliferation in a dose-dependent manner. This study demonstrates that organic–polyoxometalate hybrids represent a new strategy for developing antivirals against EMCV.

Researchers from the University of Chinese Academy of Sciences and collaborating institutions have developed a novel hierarchical framework that leverages artificial intelligence (AI) to address a long-standing challenge in solid oxide cell (SOC) engineering. The new Physics-Informed Artificial Intelligence (PIAI) method improves both physical consistency and computational efficiency, opening new opportunities for applications such as real-time performance optimization, degradation diagnosis, and the development of robust Digital Twins.

The ability to generate and detect ultrashort light pulses in the ultraviolet UV-C range (100-280 nm) is crucial for many applications. This work demonstrates a UV-C source-sensor platform that combines nonlinear optical crystals for the generation of femtosecond UV-C laser pulses with photodetectors based on atomically-thin semiconductors. The platform has potential for different technologies, from broad-band imaging to spectroscopy on femtosecond timescales. As a proof of concept, the source-sensor is used to demonstrate free-space communication.