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| http://dx.doi.org/10.1016/j.jclinane.2020.109966 | DOI Listing |
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Correlation of Phase Structure, Defect Relaxation, and Microwave Dielectric Properties in Low-Loss MgTiO Ceramic Systems.
ACS Appl Mater Interfaces
January 2025
State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China.
Low-loss microwave dielectrics are of significant importance for the miniaturization and integration of microwave devices. In this paper, the ceramics of nominal composition MgTiO ( = 3-6) are synthesized, and the correlations among their phase compositions, defect behaviors, and microwave dielectric properties are systematically investigated. The analyses indicate that the MgTiO ceramics are a biphasic system consisting of hexagonal ilmenite-structured MgTiO and cubic spinel-structured MgTiO.
View Article and Find Full Text PDF"Suspended" Single Rhenium Atoms on Nickel Oxide for Efficient Electrochemical Oxidation of Glucose.
J Am Chem Soc
January 2025
CAS Key Laboratory of Science and Technology on Applied Catalysis, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
Well-defined single-atom catalysts (SACs) serve as ideal model systems for directly comparing experimental results with theoretical calculations, offering profound insights into heterogeneous catalytic processes. However, precisely designing and controllably synthesizing SACs remain challenging due to the unpredictable structure evolution of active sites and generation of embedded active sites, which may bring about steric hindrance during chemical reactions. Herein, we present the precious nonpyrolysis synthesis of Re SACs with a well-defined phenanthroline coordination supported by NiO (Re-phen/NiO).
View Article and Find Full Text PDFInfluence of Axial Rotation Between the Femoral Neck and Ankle Joint on Kinematics in Normal Knees: A Cross-Sectional Study.
J Am Acad Orthop Surg Glob Res Rev
January 2025
From the Department of Orthopedic Surgery, Faculty of Medicine, The University of Tokyo, Bunkyo, Tokyo (Dr. Kono, Dr. Taketomi, Dr. Kage, Dr. Inui, and Dr. Tanaka); the Department of Information Systems, Faculty of Engineering, Saitama Institute of Technology, Fukaya, Saitama (Dr. Yamazaki); the Department of Orthopedic Biomaterial Science, Osaka University Graduate School of Medicine, Suita, Osaka (Dr. Tamaki, and Dr. Tomita); the Department of Orthopedic Surgery, Saitama Medical University, Saitama Medical Center, Kawagoe, Saitama (Dr. Inui); and the Department of Health Science, Graduate School of Health Science, Morinomiya University of Medical Sciences, Suminoe, Osaka, Japan (Dr. Tomita).
Background: The effect of axial rotation between the femoral neck and ankle joint (total rotation [TR]) on normal knees is unknown. Therefore, this study aimed to investigate the TR effect on normal knee kinematics.
Methods: Volunteers were divided into groups large (L), intermediate (I), and small (S), using hierarchical cluster analysis based on TR in the standing position.
Liquid Active Surface Growth: Explaining the Symmetry Breaking in Liquid Nanoparticles.
ACS Nano
January 2025
Department of Chemistry, School of Science and Key Laboratory for Quantum Materials of Zhejiang Province, Research Center for Industries of the Future, Westlake University, Hangzhou 310030, China.
In our previous studies of metal nanoparticle growth, we have come to realize that the dynamic interplay between ligand passivation and metal deposition, as opposed to static facet control, is responsible for focused growth at a few active sites. In this work, we show that the same underlying principle could be applied to a very different system and explain the abnormal growth modes of liquid nanoparticles. In such a liquid active surface growth (LASG), the interplay between droplet expansion and simultaneous silica shell encapsulation gives rise to an active site of growth, which eventually becomes the long necks of nanobottles.
View Article and Find Full Text PDFThis study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons.
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