来源：ACS PublicationsThe integration of two-dimensional (2D) semiconductors into next-generation electronics critically depends on the availability of high-κ dielectrics that combine wide band gaps, strong breakdown fields, and pristine interfaces. Conventional oxides such as HfO2 suffer from interfacial incompatibility with 2D channels, while van der Waals dielectrics like h-BN offer excellent stability but limited capacitance. Here, we report the synthesis of quasi-vdW samarium oxybromide (SmOBr) nanosheets via a molten salt-assisted chemical vapor deposition method, establishing a previously ...
Release time:
2026
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来源：ACS PublicationsWe report a novel Task-Specific Ionic Liquid (TSIL) derived from ciprofloxacin in combination with CYPHOS·NTf2 that enables tunable selectivity toward the extraction of rare earth metal elements (REEs), thorium, uranium, titanium, and zinc. The highest metal-TSIL affinity was found at pH 6, showing the highest extraction coefficient values for thorium (446), uranium (404), Sc (307), and the remaining REEs, titanium, and zinc (238–71.5; 99; 98). Under highly acidic solutions, however, the TSIL only showed affinity for thorium but not for the remaining species. The metal-...
Release time:
2026
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来源：ACS PublicationsMaterials science has an important role to play in the successful establishment of quantum technologies as they are expected to develop suitable material platforms for these highly demanding applications. Among different promising solid-state materials, rare-earth ions embedded in some host crystals show outstandingly narrow optical line widths and long-lived spin states at cryogenic temperatures. In this manuscript, we explore a promising rare-earth (RE)-based material for use in quantum applications: monazite-phase LaPO4:Eu3+ crystals, providing the first comprehensive, hi...
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2026
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来源：ACS PublicationsLithium intermetallics with channel structures are of interest for energy storage applications. As a major subset of these intermetallics, ternary tetrelides Li–M–Tt (M = metal; Tt = Si, Ge, Sn) were selected to apply machine learning approaches to predict whether they adopt channel vs nonchannel structures. Through the use of a conventional machine learning method (support vector classifier, SVC) and a more interpretable one (sure independence screening and sparsifying operator, SISSO), models were developed to perform this structural classification. By combining prediction...
Release time:
2026
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