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Structural properties of hyperuniform Voronoi networks

Physical Review E 2025年第3期111卷 034123-034123页

作者： Eli Newby Wenlong Shi Yang Jiao Reka Albert Salvatore Torquato Department of Physics Pennsylvania State University University Park Pennsylvania 16802 USA Materials Science and Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton University Princeton New Jersey 08544 USA Princeton Institute of Materials Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by the complete suppression of normalized infinite-wavelength density fluctuations, as in perfect crystals, while lacking conventional long-range order, as in liquids and glasses. In this work, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, large two-dimensional (2D) Voronoi networks (graphs) containing approximately 10,000 nodes are created from a variety of different point configurations, including the antihyperuniform hyperplane intersection process (HIP), nonhyperuniform Poisson process, nonhyperuniform random sequential addition (RSA) saturated packing, and both non-stealthy and stealthy hyperuniform point processes. We carry out an extensive study of the Voronoi-cell area distribution of each of the networks by determining multiple metrics that characterize the distribution, including their average areas and corresponding variances as well as higher-order cumulants (i.e., skewness γ1 and excess kurtosis γ2). We show that the HIP distribution is far from Gaussian, as evidenced by a high skewness (γ1=3.16) and large positive excess kurtosis (γ2=16.2). The Poisson (with γ1=1.07 and γ2=1.79) and non-stealthy hyperuniform (with γ1=0.257 and γ2=0.0217) distributions are Gaussian-like distributions, since they exhibit a small but positive skewness and excess kurtosis. The RSA (with γ1=0.450 and γ2=−0.0384) and the highest stealthy hyperuniform distributions (with γ1=0.0272 and γ2=−0.0626) are also non-Gaussian because of their low skewness and negative excess kurtosis, which is diametrically opposite of the non-Gaussian behavior of the HIP. The fact that the cell-area distributions of large, finite-sized RSA and stealthy hyperuniform networks (e.g., with N≈10,000 nodes) are narrower, have larger peaks, and smaller tails than a Gaussian distribution implies that in the thermodynamic limit th

关键词： Gaussian distribution

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Application of Machine Learning Method to Model-Based Library Approach to Critical Dimension Measurement by CD-SEM

arXiv

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arXiv 2023年

作者： Guo, P. Miao, H. Zou, Y.B. Mao, S.F. Ding, Z.J. CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Anhui Hefei230027 China School of Physics and Electronic Engineering Xinjiang Normal University Xinjiang Urumqi830054 China Department of Engineering and Applied Physics University of Science and Technology of China Anhui Hefei230026 China Department of Physics Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Anhui Hefei230026 China

The model-based library (MBL) method has already been established for the accurate measurement of critical dimension (CD) of semiconductor linewidth from a critical dimension scanning electron microscope (CD-SEM) image. In this work the MBL method has been further investigated by combing the CD-SEM image simulation with a neural network algorithm. The secondary electron linescan profiles were calculated at first by a Monte Carlo simulation method, enabling to obtain the dependence of linescan profiles on the selected values of various geometrical parameters (e.g., top CD, sidewall angle and height) for Si and Au trapezoidal line structures. The machine learning methods have then been applied to predicate the linescan profiles from a randomly selected training set of the calculated profiles. The predicted results agree very well with the calculated profiles with the standard deviation of 0.1% and 6% for the relative error distributions of Si and Au line structures, respectively. This result shows that the machine learning methods can be practically applied to the MBL method for the purpose of reducing the library size, accelerating the construction of the MBL database and enriching the content of an available MBL database. © 2023, CC BY-NC-ND.

关键词： Scanning electron microscopy

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Red blood cell passage through deformable interendothelial slits in the spleen: Insights into splenic filtration and hemodynamics

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Computers in Biology and Medicine 2024年 182卷 109198-109198页

作者： Li, Guansheng Li, He Ndou, Papa Alioune Franco, Mélanie Li, Xuejin MacDonald, Ian Dao, Ming Buffet, Pierre A. Karniadakis, George Em Division of Applied Mathematics Brown University ProvidenceRI02906 United States School of Chemical Materials and Biomedical Engineering University of Georgia AthensGA30602 United States Université Paris Cité and Université des Antilles Inserm Biologie Intégrée du Globule Rouge Paris75015 France Laboratoire d'Excellence du Globule Rouge Paris75015 France Department of Engineering Mechanics and Center for X-Mechanics Zhejiang University Hangzhou Zhejiang310027 China Department of Medical Biophysics Schulich School of Medicine and Dentistry Western University LondonON Canada Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

The spleen constantly clears altered red blood cells (RBCs) from the circulation, tuning the balance between RBC formation (erythropoiesis) and removal. The retention and elimination of RBCs occur predominantly in the open circulation of the spleen, where RBCs must cross submicron-wide inter-endothelial slits (IES). Several experimental and computational studies have illustrated the role of IES in filtrating the biomechanically and morphologically altered RBCs based on a rigid wall assumption. However, these studies also reported that when the size of IES is close to the lower end of clinically observed sizes (less than 0.5 μm), an unphysiologically large pressure difference across the IES is required to drive the passage of normal RBCs, sparking debates on the feasibility of the rigid wall assumption. In this work, We propose two deformable IES models, namely the passive model and the active model, aiming to explore the impact of the deformability of IES on the filtration function of the spleen. In the passive model, we implement the worm-like string model to depict the IES's deformation as it interacts with blood plasma and allows RBC to traverse. In contrast, the active model involved regulating the IES deformation based on the local pressure surrounding the slit. To demonstrate the validity of the deformable model, we simulate the filtration of RBCs with varied size and stiffness by IES under three scenarios: (1) a single RBC traversing a single slit;(2) a suspension of RBCs traversing an array of slits, mimicking in vitro spleen-on-a-chip experiments;(3) RBC suspension passing through the 3D spleen filtration unit known as'the splenon’. Our simulation results of RBC passing through a single slit show that the deformable IES model offers more accurate predictions of the critical cell surface area to volume ratio that dictate the removal of aged RBCs from circulation compared to prior rigid-wall models. Our biophysical models of the spleen-on-a-chip indicate a hi

关键词： Hemodynamics

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Application of Machine Learning Method to Model-Based Library Approach to Critical Dimension Measurement by Cd-Sem

SSRN

引用

SSRN 2023年

作者： Guo, Peng Miao, Hong Zou, Yanbo B. Mao, Shifeng F. Ding, Zejun J. CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Anhui Hefei230027 China School of Physics and Electronic Engineering Xinjiang Normal University Xinjiang Urumqi830054 China Department of Engineering and Applied Physics University of Science and Technology of China Anhui Hefei230026 China Department of Physics Hefei National Laboratory for Physical Sciences at Microscale University of Science and Technology of China Anhui Hefei230026 China

In semiconductor manufacturing, the accurate quantification of critical dimensions (CD) remains a pivotal challenge. This study advances the existing Model-Based Library (MBL) method, integrating it with machine learning algorithms for the precise measurement of CDs from Critical Dimension Scanning Electron Microscope (CD-SEM) images. Utilizing Monte Carlo simulations, secondary electron linescan profiles were generated for silicon (Si) and gold (Au) trapezoidal line structures under varying geometrical parameters, such as top CD, sidewall angle, and height. Machine learning techniques were then deployed to predict these linescan profiles based on a randomly chosen training set. The predicted profiles exhibited remarkable fidelity to the simulated results, with standard deviations of 0.1% and 6% in the relative error distributions for Si and Au, respectively. Our findings demonstrate that machine learning approaches significantly enhance the MBL method by reducing library size, expediting database construction, and enriching the content of existing MBL databases. © 2023, The Authors. All rights reserved.

关键词： Scanning electron microscopy

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Stochastic evolution elasto-plastic modeling of a metallic glass

arXiv

引用

arXiv 2024年

作者： Xu, Bin Wu, Zhao Lu, Jiayin Shields, Michael D. Rycroft, Chris H. Bamer, Franz Falk, Michael L. Department of Materials Science and Engineering Johns Hopkins University BaltimoreMD21218 United States Institute of General Mechanics RWTH Aachen University Nordrhein-Westfalen Eilfschornsteinstraße 18 Aachen52062 Germany Department of Applied Mathematics Harvard University CambridgeMA02138 United States Department of Mathematics University of Wisconsin MadisonWI53706 United States

This paper develops a general data-driven approach to stochastic elastoplastic modeling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present inherent complexities due to their disordered structure. By harvesting statistics from simulated metallic glass shear response histories, the material state is mapped onto a two-dimensional state space consisting of the shear stress and the inelastic contribution to the potential energy. The resulting elastoplastic model is intrinsically stochastic and represented as a non-deterministic dynamical map. The state space statistics provide insights into the deformation physics of metallic glasses, revealing that two state variables are sufficient to describe the main features of the elastoplastic response. In this two-dimensional state space, the gradually quenched metallic glass rejuvenates during the initial quasi-elastic shearing, ultimately reaching a steady-state that fluctuates about a fixed point in the state space as rejuvenation and aging balance. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures

arXiv

引用

arXiv 2023年

作者： McCray, Arthur R.C. Zhou, Tao Kandel, Saugat Petford-Long, Amanda Cherukara, Mathew J. Phatak, Charudatta Materials Science Division Argonne National Laboratory LemontIL United States Applied Physics Program Northwestern University EvanstonIL United States Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Advanced Photon Source Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering Northwestern University EvanstonIL United States

The manipulation and control of nanoscale magnetic spin textures is of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging. Copyright © 2023, The Authors. All rights reserved.

关键词： High resolution transmission electron microscopy

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Author Correction: Elastic electronics based on micromesh-structured rubbery semiconductor films (vol 5, pg 881, 2022)

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NATURE ELECTRONICS 2023年第1期6卷 86-86页

作者： Guan, Ying-Shi Ershad, Faheem Rao, Zhoulyu Ke, Zhifan da Costa, Ernesto Curty Xiang, Qian Lu, Yuntao Wang, Xu Mei, Jianguo Vanderslice, Peter Hochman-Mendez, Camila Yu, Cunjiang Institute of Advanced Materials and School of Chemistry and Chemical Engineering Southeast University Nanjing China Department of Mechanical Engineering University of Houston Houston TX USA Department of Biomedical Engineering Pennsylvania State University University Park PA USA Department of Biomedical Engineering University of Houston Houston TX USA Department of Engineering Science and Mechanics Pennsylvania State University University Park PA USA Materials Science and Engineering Program University of Houston Houston TX USA Department of Materials Science and Engineering Materials Research Institute Pennsylvania State University University Park PA USA Department of Chemistry Purdue University West Lafayette IN USA Regenerative Medicine Research Texas Heart Institute Houston TX USA Molecular Cardiology Research Laboratory Texas Heart Institute Houston TX USA

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Investigation of different membrane porosities on the permeate flux of direct contact membrane distillation 6th

Investigation of different membrane porosities on the permea...

引用

6th International Conference on Composite materials and Material engineering, ICCMME 2021

作者： Elrasheedy, Asmaa Rabie, Mohammed Elshazly, Ahmed Bassyouni, Mohamed El-Moneim, Ahmed Abd Kady, Marwa El Alexandria21934 Egypt Department of Chemical Engineering Faculty of Engineering Port Said University Port Said42526 Egypt Mechanical Power Engineering Department Mansoura University El-Mansoura35516 Egypt Chemical Engineering Department Faculty of Engineering Alexandria University Alexandria21544 Egypt Materials Science Program Zewail University of Science and Technology City of Science and Technology October Gardens 6th of October Giza12578 Egypt Materials Science and Engineering Department Egypt-Japan University of Science and Technology NewBorg El-Arab Alexandria21934 Egypt Institute of Basic and Applied Sciences Egypt-Japan University of Science and Technology New Borg El-Arab Alexandria21934 Egypt Borg El-Arab City Alexandria21934 Egypt

ISBN: (纸本)9783035718195

In the present study the surface morphology of electrospun fibers at different polystyrene (PS) solution concentration was studied by SEM imaging to determine the best PS solution concentration yielding continuous uniform beadles fibers. Contact angle measurements of the optimum fabricated PS-18 membrane confirmed the super hydrophobic property of the membrane that exhibited a static water contact angle of 145o . Numerical investigation of the performance of PS-18 membrane at different membrane thicknesses and porosities on direct contact membrane distillation showed that increasing the membrane porosity increases the permeate flux considerably. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Polystyrenes

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Dual-Scale Stick-Slip Friction on Graphene/h−BN Moiré Superlattice Structure

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Physical Review Letters 2022年第22期128卷 226101-226101页

作者： Shuai Zhang Quanzhou Yao Lingxiu Chen Chengxin Jiang Tianbao Ma Haomin Wang Xi-Qiao Feng Qunyang Li Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University Beijing 100084 China State Key Laboratory of Tribology Tsinghua University Beijing 100084 China School of Materials Science and Physics China University of Mining and Technology Xuzhou 221116 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China School of Physical Science and Technology ShanghaiTech University Shanghai 201210 China

Using atomic force microscopy, we have shown that friction on graphene/h−BN superlattice structures may exhibit unusual moiré-scale stick slip in addition to the regular ones observed at the atomic scale. Such dual-scale slip instability will lead to unique length-scale dependent energy dissipation when the different slip mechanisms are sequentially activated. Assisted by an improved theoretical model and comparative experiments, we find that accumulation and unstable release of the in-plane strain of the graphene layer is the key mechanism underlying the moiré-scale behavior. This work highlights the distinct role of the internal state of the van der Waals interfaces in determining the rich dynamics and energy dissipation of layer-structured materials.

关键词： Friction Heterostructures Van der Waals systems Atomic force microscopy

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Determining parameters in generalized thermomechanics for metamaterials by means of asymptotic homogenization

arXiv

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arXiv 2022年

作者： Vazic, Bozo Abali, Bilen Emek Newell, Pania Department of Mechanical Engineering The University of Utah Salt Lake CityUT84112 United States Department of Materials Science and Engineering Division of Applied Mechanics Uppsala University Ångströmlab Box 35 Uppsala751 03 Sweden

Advancement in manufacturing methods enable designing so called metamaterials with a tailor-made microstructure. Microstructure affects materials response within a length-scale, where we model this behavior by using the generalized thermomechanics. Strain gradient theory is employed as a higher-order theory with thermodynamics modeled as a first-order theory. Developing multiphysics models for heterogeneous materials is indeed a challenge and even this "simplest" model in generalized thermomechanics causes dozens of parameters to be determined. We develop a computational model by using a given microstructure, modeled as a periodic domain, and numerically calculate all parameters by means of asymptotic homogenization. Finite element method (FEM) is employed with the aid of open-source codes (FEniCS). Some example with symmetric and random distribution of voids in a model problem verifies the method and provides an example at which length-scale we need to consider generalized thermoeleasticity in composite materials. Copyright © 2022, The Authors. All rights reserved.

关键词： Finite element method

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