Researchers have recently discovered ultrastrong and ductile behavior of Au nanowires (NWs) through long-ranged coherent-twin-propagation. An elusive but fundamentally important question arises whether the size and surface effects impact the twin propagation behavior with a decreasing diameter. In this work, we demonstrate size-dependent strength behavior of ultrastrong and ductile metallic NWs. For Au, Pd, and AuPd NWs, high ductility of about 50% is observed through coherent twin propagation, which occurs by a concurrent reorientation of the bounding surfaces from {111} to {100}. Importantly, the ductility is not reduced with an increase in strength, while the twin propagation stress dramatically increases with decreasing NW diameter from 250 to 40 nm. Furthermore, we find that the power-law exponent describing the twin propagation stress is fundamentally different from the exponent describing the size-dependence of the yield strength. Specifically, the inverse diameter-dependence of the twin propagation stress is directly attributed to surface reorientation, which can be captured by a surface energy differential model. Our work further highlights the fundamental role that surface reorientations play in enhancing the size-dependent mechanical behavior and properties of metal NWs that imply the feasibility of high efficiency mechanical energy storage devices suggested before.
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Philos Trans A Math Phys Eng Sci
December 2024
Istituto di Fotonica e Nanotecnologie del CNR, Piazza Leonardo da Vinci 32, Milano 20133, Italy.
This work provides a mathematical derivation of a quasi-stationary (QS) model for multimode parametric down-conversion (PDC), which was presented in Gatti . (Gatti ., .
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December 2024
, Waseda University Graduate School of Information Production and Systems, Hibikino 2-7, Wakamatsu-ku, Kitakyushu 808-0135, JAPAN, Kitakyushu, 808-0135, JAPAN.
Recent studies on graph representation learning in brain tumor learning tasks have garnered significant interest by encoding and learning inherent relationships among the geometric features of tumors. There are serious class imbalance problems that occur on brain tumor MRI datasets. Impressive deep learning models like CNN- and Transformer-based can easily address this problem through their complex model architectures with large parameters.
View Article and Find Full Text PDFNat Comput Sci
December 2024
Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.
Solving partial differential equations (PDEs) using numerical methods is a ubiquitous task in engineering and medicine. However, the computational costs can be prohibitively high when many-query evaluations of PDE solutions on multiple geometries are needed. Here we aim to address this challenge by introducing Diffeomorphic Mapping Operator Learning (DIMON), a generic artificial intelligence framework that learns geometry-dependent solution operators of different types of PDE on a variety of geometries.
View Article and Find Full Text PDFIUCrdata
November 2024
School of Chemistry and Physics, University of KwaZulu-Natal, Private Bag X54001, Durban, 4000, South Africa.
The mol-ecular structure of the title salt, CHNO ·Br, reveals near co-planarity between the the imidazole and 4-nitro-benzene moieties with a dihedral angle of 8.99 (14)° between their planes. A prominent feature in the mol-ecular packing is the bromide anion acting as a double acceptor for O-H⋯Br and C-H⋯Br hydrogen-bonds, leading to a linear chain propagating along [110].
View Article and Find Full Text PDFMed Image Anal
February 2025
University of Oxford, Oxford, United Kingdom.
Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG).
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