A deep understanding of the mechanisms controlling shear banding is of fundamental importance for improving the mechanical properties of metallic glasses. Atomistic simulations highlight the importance of nanoscale stresses and strains for shear banding, but corresponding experimental proofs are scarce due to limited characterization techniques. Here, by using precession nanodiffraction mapping in the transmission electron microscope, the atomic density and strain distribution of an individual shear band is quantitatively mapped at 2 nm resolution. We demonstrate that shear bands exhibit density alternation from the atomic scale to the submicron scale and complex strain fields exist, causing shear band segmentation and deflection. The atomic scale density alternation reveals the autocatalytic generation of shear transformation zones, while the density alternation at submicron scale results from the progressive propagation of shear band front and extends to the surrounding matrix, forming oval highly strained regions with density consistently higher (∼0.2%) than the encapsulated shear band segments. Through combination with molecular dynamic simulations, a complete picture for shear band formation and propagation is established.
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http://dx.doi.org/10.1103/PhysRevLett.128.245501 | DOI Listing |
J Mol Model
January 2025
Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 807, Taiwan.
Context: To address the severe fuel crisis and environmental pollution, the use of lightweight metal materials, such as AZ alloy, represents an optimal solution. This study investigates the mechanical behavior and deformation mechanism of AZ alloys under uniaxial compressive using molecular dynamics (MD) simulations. The influence of various compositions, grain sizes (GSs), and temperatures on the compressive stress, the ultimate compressive strength (UCS), compressive yield stress (CYS), Young's modulus (E), shear strain, phase transformation, dislocation distribution, and total deformation length is thoroughly examined.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong.
This paper investigates the effects of particle morphology (PM) and particle size distribution (PSD) on the micro-macro mechanical behaviours of granular soils through a novel X-ray micro-computed tomography (μCT)-based discrete element method (DEM) technique. This technique contains the grain-scale property extraction by the X-ray μCT, DEM parameter calibration by the one-to-one mapping technique, and the massive derivative DEM simulations. In total, 25 DEM samples were generated with a consideration of six PSDs and four PMs.
View Article and Find Full Text PDFNanomaterials (Basel)
January 2025
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USA.
The present work investigates the interfacial and atomic layer-dependent mechanical properties, SOC-entailing phonon band structure, and comprehensive electron-topological-elastic integration of ZrTe and NiTe. The anisotropy of Young's modulus, Poisson's ratio, and shear modulus are analyzed using density functional theory with the TB-mBJ approximation. NiTe has higher mechanical property values and greater anisotropy than ZrTe.
View Article and Find Full Text PDFNanoscale Adv
January 2025
Department of Condensed Matter Physics, Faculty of Mathematics and Physics, Charles University Ke Karlovu 5, 12116, Prague 2 Czech Republic
Heterostructuring of two-dimensional materials offers a robust platform to precisely tune optoelectronic properties through interlayer interactions. Here we achieved a strong interlayer coupling in a double-layered heterostructure of sulfur isotope-modified adjacent MoS monolayers two-step chemical vapor deposition growth. The strong interlayer coupling in the MoS(S)/MoS(S) was affirmed by low-frequency shear and breathing modes in the Raman spectra.
View Article and Find Full Text PDFNPJ Comput Mater
January 2025
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT USA.
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.
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