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Advanced Functional materials 2020年第40期30卷

作者： Maura R. O'Neill Eric Acome Shannon Bakarich Shane K. Mitchell Julia Timko Christoph Keplinger Robert F. Shepherd Department of Mechanical and Aerospace Engineering Cornell University 124 Hoy Road Ithaca NY 14850 USA Paul M. Rady Department of Mechanical Engineering University of Colorado Boulder 427 1111 Engineering Drive Boulder CO 80309 USA Vehicle Technology Directorate US Army Research Laboratory 2800 Powder Mill Road Adelphi MD 20783 USA Materials Science and Engineering Program University of Colorado Boulder 4001 Discovery Drive Boulder CO 80309 USA

Visible-light-gated reconfigurable rotation of nanomotors in electric fields

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arXiv 2018年

作者： Liang, Zexi Fan, Donglei Materials Science and Engineering Program University of Texas at Austin AustinTX78712 United States Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States

Highly efficient and widely applicable working mechanisms that allow nanomaterials and devices to respond to external stimuli with controlled mechanical motions could make far-reaching impact to reconfigurable, adaptive, and robotic nanodevices. Here, we report an innovative mechanism that allows multifold reconfiguration of mechanical rotation of semiconductor nanoentities in electric (E) fields by visible light stimulation. When illuminated by light in the visible to infrared range, the rotation speed of semiconductor Si nanowires in electric fields can instantly increase, decrease, and even reverse the orientation depending on the intensity of the applied light and the AC E-field frequency. This multifold rotation configuration is highly efficient, instant, and facile. Switching between different modes can be simply controlled by the light intensity at an AC frequency. An array of experimentations, theoretical analysis, and simulations are carried out to understand the underlying principle, which can be attributed to the optically tunable polarization of Si nanowires in aqueous suspension and an external electric field. Finally, leveraging this newly discovered effect, we successfully differentiate semiconductor and metallic nanoentities in a non-contact and non-destructive manner. This research could inspire a new class of reconfigurable nanoelectromechanical and nanorobotic devices for optical sensing, communication, molecule release, detection, nanoparticle separation, and microfluidic automation. Copyright © 2018, The Authors. All rights reserved.

关键词： Nanowires

Optothermal Nanoscissors for Versatile Low-Power Patterning of Atomic-Thin Two-Dimensional materials

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Optothermal Nanoscissors for Versatile Low-Power Patterning ...

Conference on Lasers and Electro-Optics

作者： Jingang Li Linhan Lin Xiaolei Peng Yuebing Zheng Materials Science & Engineering Program and Texas Materials Institute The University of Texas at Austin Austin TX USA Department of Mechanical Engineering The University of Texas at Austin Austin TX USA University of Texas at Austin Austin TX US

We report a new technique - optothermal nanoscissors - to achieve fast, on-demand and arbitrary patterning of different atomic-thin two-dimensional materials using the highly localized plasmonic heating effect.

ISBN: (纸本)9781538657331;9781943580422

关键词： materials science and technology mechanical engineering"

An efficient and accurate framework for calculating lattice thermal conductivity of solids:AFLOW-AAPL Automatic Anharmonic Phonon Library

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npj Computational materials 2017年第1期3卷 79-88页

作者： Jose J.Plata Pinku Nath Demet Usanmaz Jesus Carrete Cormac Toher Maarten de Jong Mark Asta Marco Fornari Marco Buongiorno Nardelli Stefano Curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Institute of Materials Chemistry TU WienA-1060 ViennaAustria Department of Materials Science and Engineering University of CaliforniaBerkeley210 Hearst Memorial Mining BuildingBerkeleyUSA SpaceX 1 Rocket RoadHawthorneCA 90250USA Department of Physics and Science of Advanced Materials Program Central Michigan UniversityMount PleasantMI 48859USA Department of Physics and Department of Chemistry University of North TexasDentonTXUSA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA

One of the most accurate approaches for calculating lattice thermal conductivity,κ_(l),is solving the Boltzmann transport equation starting from third-order anharmonic force *** addition to the underlying approximations of ab-initio parameterization,two main challenges are associated with this path:high computational costs and lack of automation in the frameworks using this methodology,which affect the discovery rate of novel materials with ad-hoc ***,the Automatic Anharmonic Phonon Library(AAPL)is *** efficiently computes interatomic force constants by making effective use of crystal symmetry analysis,it solves the Boltzmann transport equation to obtain κ_(l),and allows a fully integrated operation with minimum user intervention,a rational addition to the current high-throughput accelerated materials development framework ***“experiment ***”study of the approach is shown,comparing accuracy and speed with respect to other available packages,and for materials characterized by strong electron localization and *** AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

关键词： properties. harmonic calculating

End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

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End-to-end symmetry preserving inter-atomic potential energy...

32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

Compressive strength evaluation and phase analysis of pulp capping materials based on carbonate apatite-SCPC using different concentration of SCPC and calcium hydroxide

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Key engineering materials 2018年 782卷 15-20页

作者： Cahyanto, Arief Restunaesha, Muga Zakaria, Myrna Nurlatifah Rezano, Andri El-Ghannam, Ahmed Department of Dental Materials Science and Technology Faculty of Dentistry Universitas Padjadjaran Jl. Raya Bandung-Sumedang KM 21 Jatinangor45363 Indonesia Graduate Student of Faculty of Dentistry Universitas Padjadjaran Jl. Raya Bandung-Sumedang KM 21 Jatinangor45363 Indonesia Department of Endodontology and Operative Dentistry Program Study of Dentistry Faculty of Medicine Universitas Jenderal Achmad Yani Jl. Terusan Jenderal Sudirman PO BOX 148 Cimahi Indonesia Department of Anatomy Physiology and Cell Biology Faculty of Medicine Universitas Padjadjaran Jl. Raya Bandung-Sumedang KM 21 Jatinangor45363 Indonesia Department of Mechanical Engineering and Engineering Science University of North Carolina at Charlotte CharlotteNC28223 United States

The mechanical strength of pulp capping material based on carbonate apatite and silica calcium-phosphate composite (CO3Ap-SCPC) is one of the key factors for the success of the material in protecting the vitality of the pulp during the formation of apatite and dentin reparative. Modifying the material in the powder phase was known to increase the mechanical strength. The purpose of this study was to determine whether the addition of SCPC and calcium hydroxide in pulp capping materials based on CO3Ap-SCPC would affect the compressive strength of this pulp capping material. In this study, three cement groups were used, each group consisted of dicalcium phosphate anhydrous and vaterite which added by SCPC concentration 0%, 5% and 10% and calcium hydroxide concentration 0%, 5% and 10%, respectively. All groups were tested by a compressive strength test and X-Ray diffraction (XRD) for phase analysis. The mean value of compressive strength with addition of 0% SCPC and 10% Ca(OH)2 was 16.54 ± 1.35 MPa, addition of 5% SCPC and 5% Ca(OH)2 was 18.55 ± 2.81 MPa, addition of 10% SCPC and 0% Ca(OH)2 was 9.22 ± 1.21 MPa. The addition of SCPC and Ca(OH)2 show statistically significant in compressive strength (p2 could improve the mechanical strength and the apatite formation of the CO3Ap-SCPC pulp capping material. © 2018 Trans Tech Publications, Switzerland.

关键词： Compressive strength

Picosecond spin orbit torque switching

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arXiv 2019年

作者： Jhuria, Kaushalya Hohlfeld, Julius Pattabi, Akshay Martin, Elodie Córdova, Aldo Ygnacio Arriola Shi, Xinping Conte, Roberto Lo Petit-Watelot, Sebastien Rojas-Sanchez, Juan Carlos Malinowski, Gregory Mangin, Stéphane Lemaître, Aristide Hehn, Michel Bokor, Jeffrey Wilson, Richard B. Gorchon, Jon Université de Lorraine CNRS IJL NancyF-54000 France Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Universidad Nacional de Ingeniería Avenida Túpac Amaru 210 Rímac Lima Peru Department of Mechanical Engineering and Materials Science and Engineering Program University of California RiversideCA92521 United States CNRS Université Paris Sud Université Paris-Saclay Palaiseau91120 France Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States

Reducing energy dissipation while increasing speed in computation and memory is a long-standing challenge for spintronics research1. In the last 20 years, femtosecond lasers have emerged as a tool to control the magnetization in specific magnetic materials at the picosecond timescale2-4. However, the use of ultrafast optics in integrated circuits and memories would require a major paradigm shift. An ultrafast electrical control of the magnetization is far preferable for integrated systems. Here we demonstrate reliable and deterministic control of the out-of-plane magnetization of a 1 nm-thick Co layer with single 6 ps-wide electrical pulses that induce spin orbit torques on the magnetization. We can monitor the ultrafast magnetization dynamics due to the spin orbit torques on sub-picosecond timescales, thus far accessible only by numerical simulations. Due to the short duration of our pulses, we enter a counter-intuitive regime of switching where heat dissipation assists the reversal. Moreover, we estimate a low energy cost to switch the magnetization, projecting to below 1fJ for a (20 nm)3 cell. These experiments prove that spintronic phenomena can be exploited on picosecond time-scales for full magnetic control and should launch a new regime of ultrafast spin torque studies and applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetization

Touch sensors with overlapping signals: Concept investigation on planar sensors with resistive or optical transduction

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arXiv 2018年

作者： Piacenza, Pedro Hannigan, Emily Baumgart, Clayton Xiao, Yuchen Park, Steve Behrman, Keith Dang, Weipeng Espinal, Jeremy Hussain, Ikram Kymissis, Ioannis Ciocarlie, Matei Department of Mechanical Engineering Columbia University New YorkNY10027 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Columbia Engineering ENG Summer Research Program

Traditional methods for achieving high localization accuracy on tactile sensors usually involve a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt to non-planar geometries. We propose a method where sensing terminals are embedded in a volume of soft material. mechanical strain in this material results in a measurable signal between any two given terminals. By having multiple terminals and pairing them against each other in all possible combinations, we obtain a rich signal set using few wires. We mine this data to learn the mapping between the signals we extract and the contact parameters of interest. Our approach is general enough that it can be applied with different transduction methods, and achieves high accuracy in identifying indentation location and depth. Moreover, this method lends itself to simple fabrication techniques and makes no assumption about the underlying geometry, potentially simplifying future integration in robot hands. Copyright © 2018, The Authors. All rights reserved.

关键词： Tactile sensors

Surface tension assisted dynamic and uniform size generation of 3D spherical hydrogels (STA-DUH) 22

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Surface tension assisted dynamic and uniform size generation...

22nd International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2018

作者： Koduri, Manohar Prasad Garden, Tom Hunt, John A. Henstock, James Curran, Judith Tseng, Fan-Gang International Intercollegiate PhD Program NTHU Hsinchu Taiwan Department of Mechanical Materials and Aerospace School of Engineering University of Liverpool United Kingdom School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom Institute of Ageing and Chronic Disease William Henry Duncan Building University of Liverpool United Kingdom Engineering and System Science Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Taiwan

ISBN: (纸本)9781510897571

Technologies like additive manufacturing, microfluidics, and electrospray show a possibility of generating a variety of 3-Dimensional structures but seriously suffer in adapting and utilizing the wide variety of available biocompatible materials because of innate diverse physical and chemical properties of biomaterials. It is very important to have a technology which can be viable and dynamically adaptable for different types of materials in generating uniform structures. This work presents a novel cell-laden hydrogel generation using the surface tension phenomenon in controlling the size of the hydrogel. Copyright © (2018) by Chemical and Biological Microsystems Society. All rights reserved.

关键词： Surface tension

A transfer learning approach for microstructure reconstruction and structure-property predictions

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arXiv 2018年

作者： Li, Xiaolin Zhang, Yichi Zhao, He Burkhart, Craig Brinson, L. Catherine Chen, Wei Theoretical and Applied Mechanics Program Northwestern University EvanstonIL60208 United States Department of Mechanical Engineering Northwestern University EvanstonIL60208 United States Global Materials Science Division Goodyear Tire and Rubber Company AkronOH44305 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States

Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture