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The synthesis of atomically precise copper nanoclusters (Cu-NCs) with high chemical stability is a prerequisite for practical applications, yet still remains a long-standing challenge. Herein, we have prepared a pyrazolate-protected Cu-NC (Cu8), which exhibited exceptional chemical stability either in solid-state or in solution. The crystals of Cu8 are still suitable for single crystal X-ray diffraction analysis even after being treated with boiling water, 8 wt % H O , high concentrated acid (1 M HCl) or saturated base (≈20 M KOH), respectively. More importantly, the structure of Cu8 in solution also remained intact toward oxygen, organic acid (100 eq. HOAc) or base (400 eq. dibutylamine) confirmed by H NMR and UV/Vis analysis. Taking advantage of high alkali-resistant, Cu8 illustrates excellent catalytic activity for the synthesis of indolizines, and it can be reused for at least 10 cycles without losing catalytic performance.
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http://dx.doi.org/10.1002/anie.202218369 | DOI Listing |
Adv Mater
September 2025
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315201, China.
Van der Waals (vdW) layered materials have gained significant attention owing to their distinctive structure and unique properties. The weak interlayer bonding in vdW layered materials enables guest atom intercalation, allowing precise tuning of their physical and chemical properties. In this work, a ternary compound, NiInSe (x = 0-0.
View Article and Find Full Text PDFPhys Chem Chem Phys
September 2025
State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.
Selenium, as an important semiconductor material, exhibits significant potential for understanding lattice dynamics and thermoelectric applications through its thermal transport properties. Conventional empirical potentials are often unable to accurately describe the phonon transport properties of selenium crystals, which limits in-depth understanding of their thermal conduction mechanisms. To address this issue, this study developed a high-precision machine learning potential (MLP), with training datasets generated molecular dynamics simulations.
View Article and Find Full Text PDFNanoscale
September 2025
Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore.
A crack-free and residue-free transfer technique for large-area, atomically-thin 2D transition metal dichalcogenides (TMDCs) such as MoS and WS is critical for their integration into next-generation electronic devices, either as channel materials replacing silicon or as back-end-of-line (BEOL) components in 3D-integrated nano-systems on CMOS platforms. However, cracks are frequently observed during the debonding of TMDCs from their growth substrates, and polymer or metal residues are often left behind after the removal of adhesive support layers wet etching. These issues stem from excessive angular strain accumulated during debonding and the incomplete removal of support layers due to their low solubility.
View Article and Find Full Text PDFNanoscale Horiz
September 2025
School of Biomedical Engineering, University of Sydney, Darlington 2008, New South Wales, Australia.
Entropy-driven drying-mediated self-assembly of plasmonic nanocrystals (termed "plasmonic atoms") has emerged as a general strategy for fabricating plasmene nanosheets from a wide range of monodisperse nanocrystals. However, extending this approach to binary systems remains challenging due to the complex nanoscale interactions between dissimilar nanocrystal shapes. Here, we introduce a combined enthalpy- and entropy-driven strategy to achieve an orderly mixed two-dimensional (2D) binary nanoassemblies from complementary reacting polymer-ligated nanocrystals.
View Article and Find Full Text PDFMed Phys
September 2025
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.
Background: Dual-energy computed tomography (DECT) enhances material differentiation by leveraging energy-dependent attenuation properties particularly for carbon ion therapy. Accurate estimation of tissue elemental composition via DECT can improve quantification of physical and biological doses.
Objective: This study proposed a novel machine-learning-based DECT (ML-DECT) method to predict the physical density and mass ratios of H, C, N, O, P, and Ca.