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arXiv 2020年

作者： Zhang, Wang Wang, Hao Wang, Hongtao You En Chan, John Liu, Hailong Zhang, Biao Zhang, Yuan-Fang Agarwal, Komal Yang, Xiaolong Low, Hong Yee Ge, Qi Yang, Joel K.W. Engineering Product Development Singapore University of Technology and Design Singapore487372 Singapore Nanofabrication Department Institute of Materials Research and Engineering Singapore138634 Singapore Northwestern Polytechnical University 127 West Youyi Road Xi'an710072 China Digital Manufacturing and Design Centre Singapore University of Technology and Design Singapore487372 Singapore National Key Laboratory of Science and Technology on Helicopter Transmission Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

Four-dimensional (4D) printing of shape memory polymer (SMP) imparts time responsive properties to 3D structures. Here, we explore 4D printing of a SMP in the submicron length scale, extending its applications to nanophononics. We report a new SMP photoresist based on Vero Clear achieving print features at a resolution of ~300 nm half pitch using two-photon polymerization lithography (TPL). Prints consisting of grids with size-tunable multi-colours enabled the study of shape memory effects to achieve large visual shifts through nanoscale structure deformation. As the nanostructures are flattened, the colours and printed information become invisible. Remarkably, the shape memory effect recovers the original surface morphology of the nanostructures along with its structural colour within seconds of heating above its glass transition temperature. The high-resolution printing and excellent reversibility in both microtopography and optical properties promises a platform for temperature-sensitive labels, information hiding for anti-counterfeiting, and tunable photonic devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Morphology

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Tensor-network-based machine learning of non-Markovian quantum processes

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Physical Review A 2020年第6期102卷 062414-062414页

作者： Chu Guo Kavan Modi Dario Poletti 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 School of Physics and Astronomy Monash University Victoria 3800 Australia Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road Singapore 487372

We show how a tensor-network-based machine learning algorithm can learn the structures of generic, non-Markovian, quantum stochastic processes. First, a process is represented as a matrix product operator (MPO) and trained with a database of local input states at different times and the corresponding time-nonlocal output state. We then apply the algorithm to predict the output state of a process at different times for a system that is coupled to a spin-chain environment. We then reconstruct the full process, and we quantify the non-Markovian memory by means of the bond dimension of the MPO for various properties of the system, of the environment, and of their interaction. Our study paves the way for a possible experimental investigation into the process tensor and its properties, and an effective characterization of noise in quantum devices.

关键词： Learning Machine learning Quantum benchmarking Quantum control

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Tensor network based machine learning of non-Markovian quantum processes

arXiv

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arXiv 2020年

作者： Guo, Chu Modi, Kavan Poletti, Dario Henan Key Laboratory of Quantum Information and Cryptography ZhengzhouHenan450000 China School of Physics and Astronomy Monash University Victoria3800 Australia Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore

We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a database of local input states at different times and the corresponding time-nonlocal output state. In particular, we analyze a qubit coupled to an environment and predict output state of the system at different time, as well as reconstruct the full system process. We show how the bond dimension of the MPO, a measure of non-Markovianity, depends on the properties of the system, of the environment and of their interaction. Hence, this study opens the way to a possible experimental investigation into the process tensor and its properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Data-Driven design Space Exploration and Exploitation for design for Additive Manufacturing (vol 141, 101101, 2019)

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JOURNAL OF MECHANICAL design 2019年第11期141卷 1-12页

作者： Xiong, Yi Pham Luu Trung Duong Wang, Dong Park, Sang-In Ge, Qi Raghavan, Nagarajan Rosen, David W. Digital Manufacturing and Design Centre Singapore University of Technology and Design Digital Manufacturing and Design Centre Singapore University of Technology and Design Robotics Institute School of Mechanical EngineeringState Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong University Department of Mechanical Engineering and Robotics Incheon National University Science and Math Cluster and Digital Manufacturing and Design Centre Singapore University of Technology and Design Engineering Product Development Pillar and Digital Manufacturing and Design Centre Singapore University of Technology and Design Digital Manufacturing and Design Centre Singapore University of Technology and Design The G. W. Woodruff School of Mechanical Engineering Georgia Institute of Technology

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

关键词： design

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Context-oriented modularization of product development processes using matrix-based clustering 20

Context-oriented modularization of product development proce...

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20th International Dependency and Structure Modeling Conference, DSM 2018

作者： Hollauer, Christoph Thomas, Robin Rhodes, Donna H. Lindemann, Udo Laboratory for Product Development and Lightweight Design TUM Boltzmannstraße 15 Garching bei München85748 Germany Systems Engineering Advancement Research Initiative MIT United States

ISBN: (纸本)9783000574924

System modularization is a common and well-established approach to reduce system complexity. However, methodical approaches for the modularization of product development processes (PDPs) can hardly be found in the literature. The work that exists focuses only on interdependencies between process elements when modularizing the process. This paper proposes a modularization method for PDPs, basing the modularization on the context of the respective process, while still also taking process-internal interdependencies into account. The matrix-based approach applies a clustering algorithm that uses process context data to group process elements into modules. The modular PDP can then be tailored into project-specific PDPs based on the project context at hand. The design and application of lean and efficient project-specific PDPs has promising potential to reduce product development effort and costs. © 2018 Lehrstuhl für Produktentwicklung und Leichtbau.

关键词： product development

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User activity detection and channel estimation for grant-free random access in LEO satellite-enabled internet-of-things

arXiv

引用

arXiv 2020年

作者： Zhang, Zhaoji Li, Ying Huang, Chongwen Guo, Qinghua Liu, Lei Yuen, Chau Guan, Yong Liang State Key Laboratory of Integrated Services Network Xidian University Xi'an710071 China Engineering Product Development Pillar Singapore University of Technology and Design Singapore119613 Singapore School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia 1-1 Asahidai Nomi Ishikawa923-1292 Japan School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

With recent advances on the dense low-earth orbit (LEO) constellation, LEO satellite network has become one promising solution to providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism, and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli-Rician message passing with expectation maximization (BR-MP-EM) algorithm is proposed for this terrestrial-satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation maximization (EM) method is employed in the outer iterations to update the hyper-parameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Advances in information processing and biological imaging using flat optics

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年 391-411页

作者： Xinwei Wang Huijie Hao Jian Liu Jiubin Tan Haoyu Li Xumin Ding Xiaoyuan He Peng Xie Guangwei Hu Hao Wang Patrice Genevet Yu Luo Advanced Microscopy and Instrumentation Research Center School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China Key Lab of Ultra-Precision Intelligent Instrumentation Harbin Institute of Technology Ministry of Industry and Information Technology Harbin China Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Product Development Singapore University of Technology and Design Singapore Singapore Physics Department Colorado School of Mines Golden CO USA National Key Laboratory of Microwave Photonics Nanjing University of Aeronautics and Astronautics Nanjing China

Flat optical components (metasurfaces) made from artificial electromagnetic materials (metamaterials) have opened new possibilities for the manipulation of electromagnetic waves within compact multifunctional devices. This field encompasses the development of individual optical elements and their integration into systems for use in real-world applications, especially in optical information processing, light detection and ranging (LiDAR), augmented or virtual reality, and biological imaging. This comprehensive Review highlights advances in the use of flat optics in analog computational information processing and imaging applications and emphasizes their fundamental role in transfer function engineering. The natural synergy between flat optics and computational technologies is described in relation to advanced imaging and microscopy solutions that have the potential for simultaneous information acquisition and processing. An outlook on future developments, including critical insights for both newcomers and experts in this field, is also provided.

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High-Entropy Deep Eutectic Solvent Achieves Ultra-Low Polarization Zinc Anode Chemistry

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Angewandte Chemie 2025年第22期137卷

作者： Nengze Wang Ming Zhang Xiaohe Ren Mengxuan Sun Tianning Pian Pinji Wang Yang-feng Cui Xiaojun Yao Chunyang Jia Hui Ying Yang National Key Laboratory of Electronic Thin Films and Integrated Devices School of Integrate Circuit Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Pillar of Engineering Product Development Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Singapore School of Materials and Energy University of Electronic Science and Technology of China Chengdu 610054 China School of Physics University of Electronic Science and Technology of China Chengdu 610054 China Centre for Artificial Intelligence Driven Drug Discovery Faculty of Applied Sciences Macao Polytechnic University Macao 999078 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 China

Deep eutectic solvents (DESs), a new class of green solvents, have emerged as promising candidates for electrolytes due to their exceptional electrochemical stability. However, the DES always exhibits unacceptable polarization of cells due to its high viscosity and low ionic conductivity, which limit its practical application in batteries. In this work, the cell polarization of DES is effectively reduced to a level comparable to that of pure aqueous electrolytes by increasing the entropy of DES via the disorder-making strategy of solvation structure. Theoretically, constructing the high-entropy deep eutectic solvent (HEDES) promotes the formation of more aggregates with high disorder, which accelerates mass transfer kinetics. As a result, the proposed HEDES (configuration entropy 17 times higher than traditional electrolyte) can achieve durable zinc plating/stripping behavior for more than 2000 h with low polarization and still maintain good stability even in extreme environments. The cylindrical zinc-ion capacitor under gram-level loading can achieve a high discharge current up to 3 A, providing ideas for the design of electrolytes for zinc-based energy storage devices.

关键词： Deep eutectic solvent High-entropy material Solid electrolyte interphase Solvation structure Zn-ion battery

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Modeling crystallographic anisotropy in interfacial sliding phenomenon in nanoindentation of nanoscale Cu/Nb accumulated rolling bonding (ARB) multilayered materials

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. S. Budiman R. Sahay F. E. Gunawan C. Harito H. Gunawan I. Radchenko E. Navarro S. Escoubas T. Cornelius O. Thomas N. Raghavan Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Xtreme Materials Lab Engineering Product Development Singapore University of Technology and Design (SUTD) Singapore Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Center for Advancing Materials Performance from the Nanoscale (CAMP-nano) State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University (XJTU) Xi'an China Aix-Marseille Université CNRS IM2NP UMR 7334 Marseille France

Nanoindentation is widely used to investigate mechanical properties of materials at small scales. In this work, owing to the presence of contrasting interfaces along rolling (RD) and transverse direction (TD), a 16 nm accumulative roll bonding (ARB) Cu/Nb nanolaminate as a test material due to its crystallographic anisotropy was used. Nanoindentation was performed and then Scanning Probe Microscopy (SPM) data was conducted to measure the pile-up height along RD and TD in the nanoscale Cu/Nb multilayered materials. It was found that the 16 nm Cu/Nb ARB nanolayered material along RD showed higher surface pile-up than TD attributed the observation to the variation in the Cu/Nb interface plasticity along RD and TD. Further, a comparation and validation of experimental indentation data by performing 2D axisymmetric Finite Element Analysis (FEA) indicated that the simulation compares well with the experimentally generated load-displacement curves, while qualitative agreement was obtained with the pileup data. It is believed that the characterization of surface pile-up is very important for enabling the nanoscale Cu/Nb ARB nanolaminates as the novel stretchable/flexible metallic conductor materials (with emerging applications such as e-skins, human-computer interaction, and soft robotics) that may prove crucial for the global green and sustainability efforts (soft robots for collecting plastic trashes in ocean).

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The potential of ti3c2tx nano-sheets (MXenes) for nanoscale solid lubrication revealed by friction force microscopy

arXiv

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arXiv 2020年

作者： Rodriguez, A. Jaman, M.S. Acikgoz, O. Wang, B. Yu, J. Grützmacher, P.G. Rosenkranz, A. Baykara, M.Z. Department of Mechanical Engineering University of California Merced MercedCA95343 United States Key Laboratory of Marine New Materials and Related Technology Ningbo Institute of Material Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Institute of Engineering Design and Product Development Tribology Research Group TU Wien Vienna1060 Austria Department of Chemical Engineering Biotechnology and Materials FCFM Universidad de Chile Santiago Chile

Ti3C2TX nano-sheets (MXenes) are an emerging class of two-dimensional materials with outstanding potential to be employed in energy storage, catalysis, and triboelectric applications, on length scales ranging from the nano- to macroscopic. Despite rapidly accelerating interest in this new class of materials, their nanoscale frictional properties and in particular, their potential for solid lubrication on the nanoscale, have not been explored in detail yet. In this short communication, we present the first results on the nanoscale frictional characteristics of MXenes, in the form of a friction map obtained on an isolated Ti3C2Tx nano-sheet deposited on a silicon dioxide substrate via friction force microscopy. Our experiments reveal that few-layer Ti3C2Tx nano-sheets indeed act as solid lubricants on the nanoscale, reducing friction on the silicon dioxide substrates, although not as effectively as few-layer graphene. The results reported here pave the way for further studies focusing on nanoscale solid lubrication achieved by Ti3C2TX (MXene) nanosheets. Copyright © 2020, The Authors. All rights reserved.

关键词： Nanosheets

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