Mechanical response of metal nanowires has recently attracted a lot of interest due to their ultra-high strengths and unique deformation behaviours. Atomistic simulations have predicted that face-centered cubic metal nanowires deform in different modes depending on the orientation between wire axis and loading direction. Here we report, by combination of in situ transmission electron microscopy and molecular dynamic simulation, the conditions under which particular deformation mechanisms take place during the uniaxial loading of [110]-oriented Au nanowires. Furthermore, by performing cyclic uniaxial loading, we show reversible plastic deformation by twinning and consecutive detwinning in tension and compression, respectively. Molecular dynamics simulations rationalize the observed behaviours in terms of the orientation-dependent resolved shear stress on the leading and trailing partial dislocations, their potential nucleation sites and energy barriers. This reversible twinning-detwinning process accommodates large strains that can be beneficially utilized in applications requiring high ductility in addition to ultra-high strength.
The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.
As a natural biocomposite, Strombus gigas, commonly known as the giant pink queen conch shell, exhibits outstanding mechanical properties, especially a high fracture toughness. It is known that the basic building block of conch shell contains a high density of growth twins with average thickness of several nanometres, but their effects on the mechanical properties of the shell remain mysterious. Here we reveal a toughening mechanism governed by nanoscale twins in the conch shell. A combination of in situ fracture experiments inside a transmission electron microscope, large-scale atomistic simulations and finite element modelling show that the twin boundaries can effectively block crack propagation by inducing phase transformation and delocalization of deformation around the crack tip. This mechanism leads to an increase in fracture energy of the basic building block by one order of magnitude, and contributes significantly to that of the overall structure via structural hierarchy.
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