A universal potential for all-purpose atomic simulations has been pursued for decades, but remains challenging due to limitations in model expressiveness and dataset construction. Now, writing in the journal Science China Chemistry, the research team led by Professor Zhi-Pan Liu at Fudan University has developed a generalized global neural network potential (GG-NN) based on a newly proposed low-cost, high-accuracy High-order Pair-reduced Neural Network (HPNN) model and a comprehensive global potential energy surface dataset of 5.84 million configurations covering 83 elements. GG-NN achieves high-precision predictions for both energies and forces, offering the potential to significantly accelerate the prediction and design of molecular and materials systems.

An Osaka Metropolitan University research team examined the practicality of implementing MR treadmill walking in healthy older adults to determine readiness for a definitive randomized controlled trial.

Thermo-mechanical energy storage (TMES) technologies have attracted significant attention due to their potential for grid-scale, long-duration electricity storage, offering advantages such as minimal geographical constraints, low environmental impact, and long operational lifespans. A key benefit of TMES systems is their ability to perform energy conversion steps that enable interaction with both thermal energy consumers and prosumers, effectively functioning as combined cooling, heating and power (CCHP) systems. This paper reviews recent progress in various TMES technologies, focusing on compressed-air energy storage (CAES), liquid-air energy storage (LAES), pumped-thermal electricity storage (PTES, also known as Carnot battery), and carbon dioxide energy storage (CES), while exploring their potential applications as extended CCHP systems for trigeneration. Techno-economic analysis indicate that TMES-based CCHP systems can achieve roundtrip (power-to-power) efficiencies ranging from 40% to 130%, overall (trigeneration) energy efficiencies from 70% to 190%, and a levelized cost of energy (with cooling and heating outputs converted into equivalent electricity) between 70 and 200 $/MWh. In general, the evolution of TMES-based CCHP systems into smart multi-energy management systems for cities or districts in the future is a highly promising avenue. However, current economic analyses remain incomplete, and further exploration is needed, especially in the area “AI for energy storage,” which is crucial for the widespread adoption of TMES-based CCHP systems.

Hydrogen, recognized as a critical energy source, requires green production methods, such as proton exchange membrane water electrolysis (PEMWE) powered by renewable energy. This is a key step toward sustainable development, with economic analysis playing an essential role. Life cycle costing (LCC) is commonly used to evaluate economic feasibility, but traditional LCC analyses often provide a single cost outcome, which limits their applicability across diverse regional contexts. To address these challenges, a Python-based tool is developed in this paper, integrating a bottom-up approach with net present value (NPV) calculations and Monte Carlo simulations. The tool allows users to manage uncertainty by intervening in the input data, producing a range of outcomes rather than a single deterministic result, thus offering greater flexibility in decision-making. Applying the tool to a 5 MW PEMWE plant in Germany, the total cost of ownership (TCO) is estimated to range between €52 million and €82.5 million, with hydrogen production costs between 5.5 and 11.4 €/kg H₂. There is a 95% probability that actual costs fall within this range. Sensitivity analysis reveals that energy prices are the key contributors to LCC, accounting for 95% of the variance in LCC, while iridium, membrane materials, and power electronics contribute to 75% of the variation in construction-phase costs. These findings underscore the importance of renewable energy integration and circular economy strategies in reducing LCC.

Carbon dioxide energy storage (CES) is an emerging compressed gas energy storage technology which offers high energy storage efficiency, flexibility in location, and low overall costs. This study focuses on a CES system that incorporates a high-temperature graded heat storage structure, utilizing multiple heat exchange working fluids. Unlike traditional CES systems that utilize a single thermal storage at low to medium temperatures, this system significantly optimizes the heat transfer performance of the system, thereby improving its cycle efficiency. Under typical design conditions, the round-trip efficiency of the system is found to be 76.4%, with an output power of 334 kW/(kg·s−1) per unit mass flow rate, through mathematical modeling. Performance analysis shows that increasing the total pressure ratio, reducing the heat transfer temperature difference, improving the heat exchanger efficiency, and lowering the ambient temperature can enhance cycle efficiency. Additionally, this paper proposes a universal and theoretical CES thermodynamic intrinsic cycle construction method and performance prediction evaluation method for CES systems, providing a more standardized and accurate approach for optimizing CES system design.

Biochar has been widely promoted as a sustainable solution for improving soil health, cleaning polluted water, and capturing carbon. However, a comprehensive review warns that its environmental safety may be more complex than previously thought.

Every experience leaves a trace in the brain, a memory that can shape future behavior. Alcohol and other addictive substances are no exception. Over time, repeated alcohol use can create strong memories that link certain places, cues or actions with reward. These memories can persist even after a person stops drinking, making relapse a common and ongoing challenge for the treatment of alcohol use disorder.

New research from the Texas A&M University Naresh K. Vashisht College of Medicine at Texas A&M Health is helping to explain why this happens and may offer new clues for developing more effective treatments.

A comprehensive phylogenetic and morphological study resolves Wendlandia as biphyletic, leading to the proposal of a new monotypic tribe Clavistigmateae and new genus Clavistigma in Rubiaceae, shedding light on plant evolution in Asia’s biodiversity hotspots.