The issue of environmental pollution has got worse due to the quick expansion of manufacturing, industry, and human society. Among different pollutants, the emission impact of volatile organic compounds (VOCs) has been steadily expanding, posing a significant challenge to human health. Gas sensors are devices that convert gas concentration from chemical to electrical signals and play a central role in real-time gas detection system.

Contact-free and object-agnostic 3D rotation at the micro/nanoscale is playing an increasingly important role in advanced imaging, biology, and microrobotics. Towards this goal, Scientist in Germany developed an opto-thermoviscous micromanipulation strategy that enables robust out-of-plane spinning and sheathless focusing in highly viscous media. The technique opens new avenues for all-optical manipulation-enabled multiview 3D microscopy and advanced biological imaging.

A laser-based technology being developed at Adelaide University could soon help authorities detect deadly counterfeit alcohol, expose wine fraud and even identify dangerous chemicals inside sealed bottles – all without opening them.

Transition metal-based electrocatalysts, especially NiFe (oxy)hydroxides, represent state-of-the-art systems for the alkaline oxygen evolution reaction (OER). While structural evolution of electrocatalysts under working conditions has garnered considerable attention, as the evolved structure often dictates catalytic activity, the mechanisms driving these transformations and catalytic performance remain poorly understood. In this work, using NiFe phosphide (NiFeP) as a model system, we systematically elucidate how phosphorus guides irreversible structural transformations and boosts OER performance. Through a combination of electron microscopy, spectroscopy, and electrochemical analysis, we demonstrate that phosphorus facilitates the transformation of NiFeP into NiFe (oxy)hydroxides while retaining residual oxyanion moieties. Importantly, we reveal that phosphorus not only accelerates the active phase formation but also suppresses Fe dissolution and directly modulates the electronic structure, a dual function that has not been well-documented in the literature. The reconstructed catalyst exhibits outstanding OER activity and remarkable stability under alkaline conditions, achieving a low overpotential of 225 mV at 10 mA cm⁻² and stably maintaining a current density of 500 mA cm⁻² for over 100 h, ranking among the most effective OER catalysts reported in the literature. Density functional theory calculations indicated that the resulting PO₄³⁻ and Fe act synergistically to regulate the redox chemistry of Ni, preventing Ni over-oxidation and enhancing electronic conductivity. Furthermore, PO₄³⁻ stabilizes key OER intermediates and lower the thermodynamic energy barrier, thereby improving catalytic performance.

Thermal energy systems (TES) are an essential part of industries that have evolved over time through the engagement of managers and researchers. The development of digital twin (DT) technology has enabled accurate prediction of their performance. The inherent limitations of complex thermal systems, such as noisy input data and occasional lack of measurement data or boundary conditions, have recently created opportunities to apply physics-based problem-solving alongside DT technology. This paper aims to systematically review the novel physics-informed neural network-digital twin (PINN-DT) methodology as a potential solution to these challenges, and to present a taxonomy for problem-solving. The outcome of this study provides valuable guidance in selecting PINN-DT technology in thermal energy system (TES) modeling. A review of the proposed loss functions demonstrates that their design is critical for achieving precise outcomes in this technology, effectively serving as the foundational core of PINN-DT. As a result, it is recommended that the construction of the loss function be fundamentally guided by two principal considerations: forecasting accuracy and compliance with physical principles, which serve as foundational pillars in the surrogate model design framework. A significant gap exists in applying this technology to industries that use discrete sampling for quality control. Implementing the PINN-DT framework could address this issue by determining optimal sampling intervals, thereby offering vital decision-making support. Moreover, the absence of exergy analysis in formulating the physical loss component of the loss function represents a significant research gap. Future studies should therefore incorporate the exergy concept into the design of the loss function.

Graphene revolutionized materials science after its discovery in 2004. Its list of potential applications is long, but its use in high-temperature superconductors has been stymied by weak electron interactions. Researchers from Tohoku University have recently created a stable version of boron graphene on the surface of a three-dimensional crystal, potentially overcoming this limitation and opening a pathway toward more energy-efficient electronic devices.

Rivers do more than transport water across landscapes. They also act as natural filters, transforming excess nitrogen before it reaches downstream lakes, reservoirs, and coastal waters. A new study of a forest-dominated river in central China reveals that this filtering capacity is controlled by a changing combination of microbial activity, water and sediment conditions, altitude, and surrounding land use.