scholarly journals Direct Visualization of Large‐Scale Intrinsic Atomic Lattice Structure and Its Collective Anisotropy in Air‐Sensitive Monolayer 1T’‐ WTe 2

2021 ◽  
pp. 2101563
Author(s):  
Kangdi Niu ◽  
Mouyi Weng ◽  
Songge Li ◽  
Zenglong Guo ◽  
Gang Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Lattice Structure ◽  
Direct Visualization ◽  
Atomic Lattice

Full-Field Deformation and Thermal Characterization of GNP/Epoxy and GNP/SMA Fiber/Epoxy Composites

2019 ◽  
Author(s):  
Ugur Kilic ◽  
Muhammad M. Sherif ◽  
Sherif M. Daghash ◽  
Osman E. Ozbulut
Keyword(s):  
Shape Memory ◽  
Epoxy Matrix ◽  
Large Scale ◽  
Low Cost ◽  
Image Correlation ◽  
Epoxy Composites ◽  
Uniaxial Tensile ◽  
Graphene Sheets ◽  
Atomic Lattice ◽  
Full Field

Abstract Shape memory alloys (SMAs) are a class of metallic alloys that possess remarkable characteristics such as superelasticity and shape memory effect. Superelastic SMAs have been considered as fiber in polymer composites due to their ability to recover their deformation upon removal of load, good energy dissipation capacity and impact resistance. Graphene nanoplatelets (GNPs) consists of small stacks of graphene sheets that are two-dimensional. Both sides of atomic lattice of GNPs contact matrix of a composite system and can generate more sites for potential chemical and physical bonding with the host material. Most importantly, graphene sheets and their derivatives can be produced at large-scale for industrial demand at low-cost. This study explores the fabrication of multi-scale reinforced epoxy matrix composites in which GNPs and SMA strands are employed as nano- and micro-scale reinforcements, respectively. First, GNPs are dispersed into a ductile and brittle epoxy matrix to produce GNP/epoxy nanocomposites. To study the effect of GNP content on the behavior of the developed nanocomposite, GNPs are added to the epoxy-hardener mixture at different weight percentages (neat, 0.1%, 0.25%, 0.5%, 1%, and 2%). Uniaxial tensile tests of the developed nanocomposites are conducted under monotonic load up to failure. The optimum GNP content for GNP-reinforced epoxy matrix is determined and used in the fabrication of SMA fiber/epoxy composite. The developed multiscale reinforced epoxy composites are tested under tensile loading and their full-field strain and temperature behavior are monitored and evaluated using a digital image correlation system and an infrared thermal camera.

Dynamic Performance and Structural Optimization of Large-Scale Machine Table

2014 ◽  
Vol 615 ◽  
pp. 313-316
Author(s):  
Zai Liang Chen ◽  
Luo Hong Deng ◽  
Cong Jing
Keyword(s):  
Structural Optimization ◽  
Natural Frequency ◽  
Dynamic Optimization ◽  
Large Scale ◽  
Lattice Structure ◽  
Dynamic Performance ◽  
Structure Parameters ◽  
Milling Machine ◽  
Plate Dynamic ◽  
The Ideal

Designed new table for large floor boring and milling machine, used ANSYS to optimize the structure of the table as a whole. According to the contours of removable material the materials which can be removed, obtained the inner ribs layout of table and the sand holes location of rib plate. Dynamic optimization variables on basic ribs cell, studied the effect of steel lattice structure parameters influenced on the natural frequency of the lattices and the related parameter of lattices influenced on whole table, to get the ideal rib lattice structure after optimizing again. Optimized bench can reduce quality, increase rigidity and dynamic performance.

Evidence of Atomic Dislocation Loops Crystal Lattice Flaws Causing EOS/ESD Damage in 〈100〉 Silicon

2014 ◽  
Vol 23 (01n02) ◽  
pp. 1420007
Author(s):  
Fayik Bundhoo
Keyword(s):  
Crystal Lattice ◽  
Integrated Circuit ◽  
Lattice Point ◽  
Silicon Surface ◽  
Lattice Structure ◽  
Crystalline Lattice ◽  
Normal Operation ◽  
Dislocation Loops ◽  
Atomic Lattice ◽  
Electrical Failure

Crystalline lattice point defects in integrated circuit can lay hidden below the silicon surface exist in the atomic lattice point structure or as interstitials. Initially they may not necessary represent an electrical failure but can act as seeds for electrical degradation in a time period. This paper study one category of Silicon defect “Dislocation loops” that initially were dormant then propagated under the silicon surface in the crystal lattice structure causing catastrophic EOS damage. This is particularly true if the crystalline defect exists near or at the boundary of PN junctions. This paper present evidence of dormant crystalline lattice defects in the form of crystalline dislocation loops degrading to EOS. Over a period of time under normal operation life of the part these dislocation loops can trigger EOS/ESD events.

Structural and elastic properties of Cu6Sn5 and Cu3Snfrom first-principles calculations

2009 ◽  
Vol 24 (7) ◽  
pp. 2361-2372 ◽  
Author(s):  
Jiunn Chen ◽  
Yi-Shao Lai ◽  
Ping-Feng Yang ◽  
Chung-Yuan Ren ◽  
Di-Jing Huang
Keyword(s):  
Electronic Structure ◽  
Elastic Modulus ◽  
Elastic Properties ◽  
First Principles ◽  
Elastic Anisotropy ◽  
Lattice Structure ◽  
Elastic Stiffness ◽  
First Principles Calculations ◽  
Atomic Lattice ◽  
Microstructure Effect

We investigated the elastic properties of two tin-copper crystalline phases, the η′-Cu6Sn5 and ε-Cu3Sn, which are often encountered in microelectronic packaging applications. The full elastic stiffness of both phases is determined based on strain-energy relations using first-principles calculations. The computed results show the elastic anisotropy of both phases that cannot be resolved from experiments. Our results, suggesting both phases have the greatest stiffness along the c direction, particularly showed the unique in-plane elastic anisotropy associated with the lattice modulation of the Cu3Sn superstructure. The polycrystalline moduli obtained using the Voigt-Reuss scheme are 125.98 GPa for Cu6Sn5 and 134.16 GPa for Cu3Sn. Our data analysis indicates that the smaller elastic moduli of Cu6Sn5 are attributed to the direct Sn–Sn bond in Cu6Sn5. We reassert the elastic modulus and hardness of both phases using the nanoindentation experiment for our calculation benchmark. Interestingly, the computed polycrystalline elastic modulus of Cu6Sn5 seems to be overestimated, whereas that of Cu3Sn falls nicely in the range of reported data. Based on the observations, the elastic modulus of Cu6Sn5 obtained from nanoindentation tests admit the microstructure effect that is absent for Cu3Sn is concluded. Our analysis of electronic structure shows that the intrinsic hardness and elastic modulus of both phases are dominated by electronic structure and atomic lattice structure, respectively.

Electron Mobility across Grain Boundaries in Graphene Synthesized using Chemical Vapor Deposition Process

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Fei Long ◽  
Chang Kyoung Choi
Keyword(s):  
Chemical Vapor Deposition ◽  
Grain Boundaries ◽  
Potential Barrier ◽  
Vapor Deposition ◽  
Large Scale ◽  
Chemical Vapor ◽  
Deposition Process ◽  
Barrier Effect ◽  
Atomic Lattice ◽  
Mismatch Angle

Chemical vapor deposition (CVD) is currently the only method for large-scale synthesis of graphene. However, the CVD process introduces grain boundaries (GBs) when individual grains coalesce with various mismatch angles. These GBs contain atomic dislocations and defects, which are believed to alter graphene's mechanical, electrical, and thermal properties. Specifically, the GBs can act as “potential barriers” when charges move from one grain to neighboring grains. This barrier effect will not only change the electrical conductivity but also the thermal conductivity of graphene. Besides high-resolution, 3-dimensional topography images, Atomic force microscopy (AFM) can also obtain the electrical properties at the nanoscale. In this report, the potential barrier effect of graphene GBs is studied with AFM. During the experiment, the probe is brought into contact with the graphene while positively (or negatively) biased. This process injects net charges into the graphene. The electrostatic potential across the GBs can be measured by AFM as an indication of the potential barrier effect. GBs with lower potential difference correspond to lower potential barrier, and vice versa. The dependency of the barrier effect on the mismatch angles was also measured. Considering the 6 folds’ symmetry of graphene atomic lattice, the mismatch angle is in the range of 0° ∼ 30°, with 30° the maximum mismatch angle. Our results can be well fitted with a sinusoidal function with π/3 period, which supports our hypothesis that higher mismatch angle contains higher density of dislocations and defects that increase the potential barrier of GBs.

scholarly journals Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures

2021 ◽  
pp. 1-16
Author(s):  
Shengjun Liu ◽  
Tao Liu ◽  
Qiang Zou ◽  
Weiming Wang ◽  
Eugeni L. Doubrovski ◽  
...  
Keyword(s):  
Large Scale ◽  
Lattice Structure ◽  
Weighted Graph ◽  
Lattice Structures ◽  
Computational Framework ◽  
Density Distributions ◽  
Graded Material ◽  
Convolution Surfaces ◽  
Supporting Structures ◽  
Memory Efficient

Abstract Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive memory. This hinders the use of lattice structures in large-scale applications (e.g., to design the interior structure of a solid with spatially graded material properties). To solve this issue, we propose a memory-efficient method for the modeling and slicing of adaptive lattice structures. A lattice structure is represented by a weighted graph where the edge weights store the struts' radii. When slicing the structure, its solid model is locally evaluated through convolution surfaces and in a streaming manner. As such, only limited memory is needed to generate the toolpaths of fabrication. Also, the use of convolution surfaces leads to natural blending at intersections of struts, which can avoid the stress concentration at these regions. We also present a computational framework for optimizing supporting structures and adapting lattice structures with prescribed density distributions. The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.

Interactive Data Mining for Large-Scale Image Databases Based on Formal Concept Analysis

2010 ◽  
Vol 14 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Takanari Tanabata ◽  
◽  
Kazuhito Sawase ◽  
Hajime Nobuhara ◽  
Barnabas Bede ◽  
...  
Keyword(s):  
Data Mining ◽  
Formal Concept Analysis ◽  
Large Scale ◽  
Lattice Structure ◽  
Concept Analysis ◽  
Image Features ◽  
Image Databases ◽  
Formal Concept ◽  
Interactive Data Mining ◽  
Interactive Data

In order to perform an interactive data-mining for huge image databases efficiently, a visualization interface based on Formal Concept Analysis (FCA) is proposed. The proposed interface system provides an intuitive lattice structure enabling users freely and easily to select FCA attributes and to view different aspects of the Hasse diagram of the lattice of a given image database. The investigation environment is implemented using C++ and the OpenCV library on a personal computer (CPU = 2.13 GHz, MM = 2 GB). In visualization experiments using 1,000 Corel Image Gallery images, we test image features such as color, edge, and face detectors as FCA attributes. Experimental analysis confirms the effectiveness of the proposed interface and its potential as an efficient datamining tool.

scholarly journals Spin mapping of intralayer antiferromagnetism and field-induced spin reorientation in monolayer CrTe2

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Jing-Jing Xian ◽  
Cong Wang ◽  
Jin-Hua Nie ◽  
Rui Li ◽  
Mengjiao Han ◽  
...  
Keyword(s):  
Lattice Structure ◽  
Spin Reorientation ◽  
Atomic Scale ◽  
First Principles Calculations ◽  
Atomic Lattice ◽  
Depth Analysis ◽  
Scanning Tunnelling ◽  
Magnetic Orders ◽  
Tunnelling Microscopy ◽  
Spin Texture

AbstractIntrinsic antiferromagnetism in van der Waals (vdW) monolayer (ML) crystals enriches our understanding of two-dimensional (2D) magnetic orders and presents several advantages over ferromagnetism in spintronic applications. However, studies of 2D intrinsic antiferromagnetism are sparse, owing to the lack of net magnetisation. Here, by combining spin-polarised scanning tunnelling microscopy and first-principles calculations, we investigate the magnetism of vdW ML CrTe2, which has been successfully grown through molecular-beam epitaxy. We observe a stable antiferromagnetic (AFM) order at the atomic scale in the ML crystal, whose bulk is ferromagnetic, and correlate its imaged zigzag spin texture with the atomic lattice structure. The AFM order exhibits an intriguing noncollinear spin reorientation under magnetic fields, consistent with its calculated moderate magnetic anisotropy. The findings of this study demonstrate the intricacy of 2D vdW magnetic materials and pave the way for their in-depth analysis.

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