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Three-Dimensionally Printed Mechanical Metamaterials With Thermally Tunable Auxetic Behavior

Physical Review Applied 2019年第4期11卷 044074-044074页

作者： Zeang Zhao Chao Yuan Ming Lei Le Yang Qiang Zhang Haosen Chen H. Jerry Qi Daining Fang State Key Laboratory for Turbulence and Complex Systems & Center for Applied Physics and Technology College of Engineering Peking University Beijing 100871 P. R. China The George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta Georgia 30332 USA Digital Manufacturing and Design Center Singapore University of Technology and Design Singapore 487372 Singapore Institure of Advanced Structure Technology Beijing Institute of Technology Beijing 100081 P. R. China

The extraordinary properties of mechanical metamaterials are created from the geometrical design of their internal structures. As a result, the nature of classical mechanical metamaterials usually would not change and one geometrical design only corresponds to one set of mechanical behaviors. This restricts the application of metamaterials where an adaptive behavior is desirable. In this work, we demonstrate the dynamic and on-demand regulation of mechanical metamaterials through a combination of structural design and multimaterial 3D printing. We design a soft periodic lattice metamaterial containing two distinct deformation modes. One deformation mode is controlled by the zig-zagged topological defects, and the other one is controlled by the thermal activation of the responsive materials imbedded in the lattice. By regulating the deformation mode with ambient temperature, the effective Poisson ratio of the lattice can be intentionally switched between negative values and positive values. The mechanism of thermally tunable deformation is investigated both experimentally and theoretically, resulting in a predictive design space of the metamaterial. We believe our design principle opens up a new route for the exploration of metamaterials exhibiting extraordinary and programmable mechanical behaviors.

关键词： Elastic deformation Poisson ratio Smart materials Thermal properties 3-dimensional systems Auxetic materials Metamaterials

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Conditional physics informed neural networks

arXiv

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arXiv 2021年

作者： Kovacs, Alexander Exl, Lukas Kornell, Alexander Fischbacher, Johann Hovorka, Markus Gusenbauer, Markus Breth, Leoni Oezelt, Harald Yano, Masao Sakuma, Noritsugu Kinoshita, Akihito Shoji, Tetsuya Kato, Akira Schrefl, Thomas Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning Viktor Kaplan-Straße 2E Wiener Neustadt2700 Austria Department for Integrated Sensor Systems Danube University Krems Viktor Kaplan-Straße 2E Wiener Neustadt2700 Austria Department of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria Wolfgang Pauli Institute Oskar-Morgenstern-Platz 1 Vienna1090 Austria Research Platform MMM Mathematics-Magnetism-Materials Oskar-Morgenstern-Platz 1 Vienna1090 Austria Advanced Materials Engineering Div. Toyota Motor Corporation 1200 Mishuku Susono Shizuoka410-1193 Japan

We introduce conditional PINNs (physics informed neural networks) for estimating the solution of classes of eigenvalue problems. The concept of PINNs is expanded to learn not only the solution of one particular differential equation but the solutions to a class of problems. We demonstrate this idea by estimating the coercive field of permanent magnets which depends on the width and strength of local defects. When the neural network incorporates the physics of magnetization reversal, training can be achieved in an unsupervised way. There is no need to generate labeled training data. The presented test cases have been rigorously studied in the past. Thus, a detailed and easy comparison with analytical solutions is made. We show that a single deep neural network can learn the solution of partial differential equations for an entire class of *** Codes I.2.6 Copyright © 2021, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Experimental Validation of Nussbaum Gain Adaptive Control for a Polymer Composite Based Electromagnetic Micromirror 36

Experimental Validation of Nussbaum Gain Adaptive Control fo...

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第36届中国控制会议

作者： Jiazheng Tan Weijie Sun John T.W.Yeow Department of Automation Science and Engineering South China University of Technology Advanced Micro/Nano Devices Laboratory Systems Design EngineeringUniversity of Waterloo

ISBN: (纸本)9781538629185

In this article, an adaptive controller based on Nussbaum gain technique and backstepping method is designed for a polymer composite based hard-magnetic micromirror. With the Nussbaum gain exploited, the unknown control direction problem is solved. As a result, the closed-loop system with proposed controller design is able to track the desired reference signal asymptotically. The effectiveness of the adaptive controller is verified by experimental results on a micromirror-based platform.

关键词： Nussbaum gain Backstepping design Unknown control direction Electromagnetic micromirror

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First-principles study of bulk and two-dimensional structures of the AMnBi family of materials (A=K,Rb,Cs)

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Physical Review B 2020年第3期102卷 035444-035444页

作者： Ziming Zhu Chunyan Liao Si Li Xiaoming Zhang Weikang Wu Zhi-Ming Yu Rui Yu Wei Zhang Shengyuan A. Yang Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Research Laboratory for Quantum Materials Singapore University of Technology and Design Singapore 487372 Singapore School of Materials Science and Engineering Hebei University of Technology Tianjin 300130 China Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems and School of Physics Beijing Institute of Technology Beijing 100081 China School of Physics and Technology Wuhan University Wuhan 430072 China Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials College of Physics and Energy Fujian Normal University Fuzhou 350117 China School of Physics and Technology Center for Quantum Transport and Thermal Energy Science Nanjing Normal University Nanjing 210023 China

Magnetic materials with high mobilities are intriguing subjects of research from both fundamental and application perspectives. Based on first-principle calculations, we investigate the physical properties of the already synthesized AMnBi (A=K, Rb, Cs) family materials. We show that these materials are antiferromagnetic (AFM), with Néel temperatures above 300 K. They contain AFM ordered Mn layers, while the interlayer coupling changes from ferromagnetic (FM) for KMnBi to AFM for RbMnBi and CsMnBi. We find that these materials are narrow gap semiconductors. Owing to the small effective mass, the electron carrier mobility can be very high, reaching up to 105cm2/(Vs) for KMnBi. In contrast, the hole mobility is much suppressed, typically lower by two orders of magnitude. We further study their two-dimensional (2D) single layer structures, which are found to be AFM with fairly high mobility ∼103cm2/(Vs). Their Néel temperatures can still reach room temperature. Interesting, we find that the magnetic phase transition is also accompanied by a metal-insulator phase transition, with the paramagnetic metal phase possessing a pair of nonsymmorphic-symmetry-protected 2D spin-orbit Dirac points. Furthermore, the magnetism can be effectively controlled by the applied strain. When the magnetic ordering is turned into FM, the system can become a quantum anomalous Hall insulator with gapless chiral edge states.

关键词： Edge states First-principles calculations Magnetic phase transitions Spin-orbit coupling Topological materials Transport phenomena

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How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

arXiv

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arXiv 2023年

作者： Bosoni, Emanuele Beal, Louis Bercx, Marnik Blaha, Peter Blügel, Stefan Bröder, Jens Callsen, Martin Cottenier, Stefaan Degomme, Augustin Dikan, Vladimir Eimre, Kristjan Flage-Larsen, Espen Fornari, Marco Garcia, Alberto Genovese, Luigi Giantomassi, Matteo Huber, Sebastiaan P. Janssen, Henning Kastlunger, Georg Krack, Matthias Kresse, Georg Kühne, Thomas D. Lejaeghere, Kurt Madsen, Georg K.H. Marsman, Martijn Marzari, Nicola Michalicek, Gregor Mirhosseini, Hossein Müller, Tiziano M.A. Petretto, Guido Pickard, Chris J. Poncé, Samuel Rignanese, Gian-Marco Rubel, Oleg Ruh, Thomas Sluydts, Michael Vanpoucke, Danny E.P. Vijay, Sudarshan Wolloch, Michael Wortmann, Daniel Yakutovich, Aliaksandr V. Yu, Jusong Zadoks, Austin Zhu, Bonan Pizzi, Giovanni Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain Univ. Grenoble-Alpes CEA IRIG-MEM-L Sim Grenoble38000 France LausanneCH-1015 Switzerland Institute for Materials Chemistry Technical University of Vienna Getreidemarkt 9/165-TC ViennaA-1060 Austria Peter Grünberg Institut Institute for Advanced Simulation Forschungszentrum Jülich JARA JülichD-52425 Germany Forschungszentrum Jülich JülichD-52425 Germany Department of Electromechanical Systems and Metal Engineering Ghent University Belgium Ghent University Belgium Institute of Atomic and Molecular Sciences Academia Sinica Taipei10617 Taiwan Norwegian EuroHPC Competence Center Sigma2 AS Norway SINTEF Industry Materials Physics Oslo Norway Department of Physics Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Université catholique de Louvain Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium LausanneCH-1015 Switzerland Kongens Lyngby2800 Denmark VilligenCH-5232 Switzerland University of Vienna Faculty of Physics Center for Computational Materials Science Kolingasse 14-16 ViennaA-1090 Austria VASP Software GmbH Sensengasse 8 ViennaA-1090 Austria Helmholtz-Zentrum Dresden-Rossendorf GörlitzD-02826 Germany Center for Sustainable Systems Design University of Paderborn PaderbornD-33098 Germany OCAS NV ArcelorMittal Global R&D Gent Pres. J. F. Kennedylaan 3 ZelzateB-9060 Belgium Dynamics of Condensed Matter Theoretical Chemistry University of Paderborn PaderbornD-33098 Germany HPE HPC EMEA Research Lab BaselCH-4051 Switzerland Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba Sendai980-8577 Japan Department of Materials Science and Engineering McMaster University 1280 Main Street West HamiltonONL8S 4L8 Canada ePot

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). © 2023, CC BY.

关键词： Density functional theory

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Development of electric cart for improving walking ability — application of control theory to assistive technology

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Science China(Information Sciences) 2017年第12期60卷 262-270页

作者： Jinhua SHE Yasuhiro OHYAMA Min WU Hiroshi HASHIMOTO School of Engineering Tokyo University of Technology School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Master Program of Innovation for Design & Engineering Advanced Institute of Industrial Technology

This paper explains the development of an electric cart that helps the elderly maintain or improve their physical strength. Unlike commercially available ones, it has a pedal unit that provides some exercise for a user in training his lower limbs. An impedance model describes the feeling of pushing the pedals. The largest pedal load is determined based on a pedaling experiment. An H∞ controller is designed for each of the largest pedal load and virtually no load. A control law, which is based on the concept of dynamic parallel distributed compensation, is designed using the rating of perceived exertion of a driver as a criterion to choose a pedal load between the largest and almost zero. Five university students and twelve elderly people participated experiments to verify the system design and the validity of the system.

关键词： aging Borg’s scale dynamic parallel distributed compensation electrical cart H_∞ control Karvonen formula lower limbs pedaling rating of perceived exertion

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Biodegradable Polymersomes with Structure Inherent Fluorescence and Targeting Capacity for Enhanced Photo-Dynamic Therapy

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Angewandte Chemie 2021年第32期133卷

作者： Dr. Shoupeng Cao Yifeng Xia Dr. Jingxin Shao Beibei Guo Yangyang Dong Dr. Imke A. B. Pijpers Prof. Dr. Zhiyuan Zhong Prof. Dr. Fenghua Meng Dr. Loai K. E. A. Abdelmohsen Dr. David S. Williams Prof. Dr. Jan C. M. van Hest Bio-Organic Chemistry Institute for Complex Molecular Systems Eindhoven University of Technology P.O. Box 513 (STO 3.41) 5600 MB Eindhoven The Netherlands Biomedical Polymers Laboratory and Jiangsu Key Laboratory of Advanced Functional Polymer Design and Application College of Chemistry Chemical Engineering and Materials Science Soochow University Suzhou 215123 P. R. China School of Cellular and Molecular Medicine University of Bristol Bristol UK

Biodegradable nanostructures displaying aggregation-induced emission (AIE) are desirable from a biomedical point of view, due to the advantageous features of loading capacity, emission brightness, and fluorescence stability. Herein, biodegradable polymers comprising poly (ethylene glycol)-block-poly(caprolactone-gradient-trimethylene carbonate) (PEG-P(CLgTMC)), with tetraphenylethylene pyridinium-TMC (PAIE) side chains have been developed, which self-assembled into well-defined polymersomes. The resultant AIEgenic polymersomes are intrinsically fluorescent delivery vehicles. The presence of the pyridinium moiety endows the polymersomes with mitochondrial targeting ability, which improves the efficiency of co-encapsulated photosensitizers and improves therapeutic index against cancer cells both in vitro and in vivo. This contribution showcases the ability to engineer AIEgenic polymersomes with structure inherent fluorescence and targeting capacity for enhanced photodynamic therapy.

关键词： aggregation-induced emission biodegradable polymersomes mitochondria targeting photo-dynamic therapy polycarbonates

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A Small-Molecule Drug for the Self-Checking of Mitophagy

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Angewandte Chemie 2025年第10期137卷

作者： Ms. Yanan Gao Mr. Qingjie Bai Ms. Youxiao Ren Dr. Xintian Shao Mr. Mengrui Zhang Dr. Luling Wu Prof. Simon E. Lewis Prof. Tony D. James Prof. Xiaoyuan Chen Prof. Qixin Chen State Key Laboratory of Advanced Drug Delivery and Release Systems School of Pharmaceutical Sciences Neck-Shoulder and Lumbocrural Pain Hospital Medical Science and Technology Innovation Center Shandong First Medical University & Shandong Academy of Medical Sciences Jinan Shandong 250117 PR China Department of Chemistry University of Bath Bath BA2 7AY U.K. School of Chemistry and Chemical Engineering Henan Normal University Xinxiang 453007 People's Republic of China Departments of Diagnostic Radiology Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and College of Design and Engineering National University of Singapore Singapore 119074 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore Nanomedicine Translational Research Program Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore Theranostics Center of Excellence (TCE) Yong Loo Lin School of Medicine National University of Singapore 11 Biopolis Way Helios Singapore 138667 Institute of Molecular and Cell Biology Agency for Science Technology and Research (A*STAR) 61 Biopolis Drive Proteos Singapore 138673 Singapore Department of Pharmacy and Pharmaceutical Sciences National University of Singapore Lower Kent Ridge Road 4 Science Drive 2 117544 Singapore

Mitophagy, particularly in the context of drugs that disrupt mitochondrial membrane potential (MMP), represents a critical focus in pharmacology. However, the discovery and evaluation of MMP-disrupting drugs are often hampered using commercially available marker molecules that target similar or identical zones. These markers can significantly interfere with, obscure, or amplify the functional effects of MMP-targeting drugs, frequently leading to clinical failures. In response to this challenge, we propose a “one-two punch” drug design strategy that integrates both target-zone drug functionality and non-target zone biological reporting within a single small-molecule drug. We have developed a novel proof-of-concept mitophagy self-check drug ( MitoSC ) that exhibits dual-color and dual-localization properties. The functional component of this system is a variable MitoSC that disrupts mitochondrial membrane potential (MMP) homeostasis, thereby inducing mitophagy. Upon activation, this component transforms into a blue-fluorescent monomer ( MitoSC-fun ) specifically within the mitochondrial target zone. Concurrently, the biological reporting component is represented by a red-fluorescent monomer ( MitoSC-rep ) that localizes to lysosomes, the non-target zone. As mitophagy progresses, the fluorescent signals from MitoSC-rep (lysosomes) and MitoSC-fun (mitochondria) converge, enabling real-time monitoring of the mitophagic process. This strategy combines potent drug functionality with robust biological reporting, thereby minimizing interference and eliminating the complexities associated with external detection. Our findings underscore the potential of a single-molecule drug to exert target-zone specific actions while simultaneously providing non-target zone self-checking, offering a new perspective for drug design.

关键词： Mitochondria Lysosomes Subcellular dynamics Dual localization Self-checking drug

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U-shaped Feature Extractor Used on Mask R-CNN for Cell Nuclei Image Segmentation

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Journal of Physics: Conference Series 2020年第1期1646卷

作者： Qing Zhu Xiaoyang Huang Liansheng Wang Jonathan Li Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen FJ 361005 China Department of Geography and Environmental Management and Department of Systems Design Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada

The cell nuclei segmentation of is a challenging task in microscopy image analysis. The problems of noise, small cell nuclei, and few training data samples in the data set will all affect the effectiveness of the model to varying degrees. This paper presents a new approach to nuclear image segmentation based on convolutional neural networks. Our approach is based on Mask R-CNN with some modification, which combines low-level semantic features for model training. In order to make the model specific to each low-level feature map, the attention mechanism was used to assign weights to each low-level feature map, making the model learning more purposeful. Our method achieves an average precision value of 62.8%, which is 2.7% higher than that of the Mask R-CNN (ResNet50) basic model and 6.3% of the Mask R-CNN (ResNet101) basic model.

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