For decades, nonproliferation scientists and engineers at the U.S. Department of Energy's Argonne National Laboratory have helped convert the fuel in dozens of research reactors around the world so that they operate safely and more securely.
Argonne National Laboratory researchers proposed an AI-powered monitoring system that can spot blockages in the heat exchangers of molten-salt-cooled reactors in real time, helping prevent costly shutdowns and improve reliability for next-generation nuclear energy.
This study models how India’s expanding electric vehicle fleet could change demand for and requirements of lithium, nickel, cobalt, manganese, and graphite through 2060 under several deployment and battery-chemistry scenarios. Under the baseline scenario, EV battery demand reaches about 965 GWh by 2060, while recycling could reduce cumulative lithium demand by up to 54% and offset much of the demand for cobalt and manganese.
A ChemRxiv preprint reviewing 58 new materials-science foundation models found that 52 used a single data modality, while only six combined multiple modalities, and none combined multimodality with explicit physical grounding. The authors propose a framework that combines harmonized materials data, physics-aware multi-modal architectures, efficient specialization, and rigorous validation, although such a unified model has yet to be demonstrated.
A team of researchers led by the U.S. Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) developed a powerful new approach that enabled them to directly observe how electrons interact with defects in advanced semiconductor devices with an unprecedented level of detail.
Professor Do Hyun Ryu's research team in the Department of Chemistry at Sungkyunkwan University (SKKU) has developed two new asymmetric catalytic methods for the precise synthesis of complex molecules using chiral organic catalysts. The team demonstrated that a single catalyst can promote two different carbon-carbon bond-forming reactions with high selectivity.
A Defense Technology review examines how tunable plasma layers could absorb, reflect, refract, and scatter radar signals to reduce the radar cross-section of aircraft and other military platforms. The authors assess plasma-generation methods, hybrid stealth systems, and the substantial challenges in power, stability, communication, and integration that still separate laboratory research from operational use.
A review of durable superhydrophobic materials reframes surface design around three dominant failure modes: structural degradation, loss of low-surface-energy chemistry, and coating-substrate interfacial failure. The authors connect fabrication choices with failure-matched testing and examine fluorine-free chemistries, protected architectures, self-healing systems, and interface engineering as routes toward longer service life in demanding environments.
Researchers developed an AI-assisted computational pipeline that combines symmetry-guided screening, machine learning, and density functional theory to search for altermagnetic metal-organic frameworks. The approach also identified molecular features associated with altermagnetism and spin splitting, providing design rules for selecting future candidates for computational and experimental study.
Because many natural and industrial processes rely on the transport of bubbles and droplets through fluids, the behavior of ordinary air bubbles and water droplets has been well documented.
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