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.

When some species of bacteria find themselves in an environmentally stressful situation they can hunker down and form spores—hardened protein-coated spheres protecting a DNA-filled core, stable for years or even centuries. Tufts researchers have found new ways to expand the potential for using bacterial spores as catalysts for chemical reactions, biofuel production, or breaking down pollutants

New research, published in Science, marks the first time radium molecules have been successfully prepared and studied precisely with lasers in tabletop experiments. 

A surface electrene, BaSiN₂:O, developed by researchers at Science Tokyo enables efficient ammonia synthesis under mild conditions while overcoming the long-standing air instability of electrene materials. Synthesized by doping barium silicon nitride with oxygen, the material forms a stable layer of freely floating electrons on its surface. When combined with ruthenium, it delivers exceptionally high ammonia synthesis activity, paving the way for sustainable ammonia production and practical applications of electrene materials.