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| 1 | 界面增强多晶点阵结构的耐撞吸能性能显示文摘3D打印技术有力促进了金属点阵材料的发展,而碰撞吸能是点阵材料的重要应用领域之一,为此综述了课题组近期在界面增强点阵吸能方面的研究。受金属材料微观变形机理中晶界强化机制的启发,通过在点阵结构中引入晶界和孪晶界等宏观界面构型,构造了含多个界面的多晶点阵结构,研究其耐撞吸能性能。具体而言,构造了胞元构型为简单立方、面心立方和三斜晶系的不同多晶点阵结构试件,通过一系列参数化有限元模拟,并结合增材制造技术开展验证性实验,研究了晶粒尺寸(晶界密度)、界面两侧晶向差、界面取向角度等参数对结构压溃变形模式和吸能性能的影响,发现对称性强的界面(如孪晶界)可以增强点阵结构的吸能性能。进一步研究发现,描述材料微观强化机理的Hall-Petch关系仍然适用于所提宏观多晶点阵结构。该研究可为发展新型轻质点阵吸能结构提供一定的参考。 | 杨帆 卞奕杰 王鹏 李浦昊 张思远 范华林 | 2022 | 高压物理学报2022，36，2： | 0 |
| 2 | Encoding reprogrammable properties into magneto-mechanical materials via topology optimization显示文摘The properties of materials and structures typically remain fixed after being designed and manufactured.There is a growing interest in systems with the capability of altering their behaviors without changing geometries or material constitutions,because such reprogrammable behaviors could unlock multiple functionalities within a single design.We introduce an optimization-driven approach,based on multi-objective magneto-mechanical topology optimization,to design magneto-active metamaterials and structures whose properties can be seamlessly reprogrammed by switching on and off the external stimuli fields.This optimized material system exhibits one response under pure mechanical loading,and switches to a distinct response under joint mechanical and magnetic stimuli.We discover and experimentally demonstrate magneto-mechanical metamaterials and metastructures that realize a wide range of reprogrammable responses,including multi-functional actuation responses,adaptable snap-buckling behaviors,switchable deformation modes,and tunable bistability.The proposed approach paves the way for promising applications such as magnetic actuators,soft robots,and energy harvesters. | Zhi Zhao Xiaojia Shelly Zhang | 2023 | npj Computational Materials2023，，1： | 0 |
| 3 | Differential programming enabled functional imaging with Lorentz transmission electron microscopy显示文摘Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials.Information such as magnetization and electric potentials is carried by the phase of the electron wave,and is lost during image acquisition.Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images,most of which work only in the small defocus limit.Imaging at strong defoci not only carries more quantitative phase information,but is essential to the study of weak magnetic and electrostatic fields at the nanoscale.In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval.We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope.More importantly,our proposed method can go beyond recovering just the phase information.We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction. | Tao Zhou Mathew Cherukara Charudatta Phatak | 2021 | npj Computational Materials2021，，1： | 0 |
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