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Rational development of next-generation shape memory alloys

下一代形状记忆合金的合理发展

基本信息

批准号：
1808162
负责人：
Joost Vlassak
金额：
$ 43.35万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-15 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808162&HistoricalAwards=false
关键词：
Rational development next generation shape

项目摘要

Non-technical AbstractShape memory alloys (SMAs) are materials that undergo large shape changes when their temperatures are changed. This unique property makes SMAs ideal for the fabrication of powerful yet lightweight actuators for a variety of applications. While SMAs have already been adopted in a number of commercial technologies - from image stabilizers in smart phones to tools for minimally invasive endovascular surgery - the relatively low operating temperatures of commercially available SMAs (typically between room temperature and 100 degrees C) make them unsuitable for many applications. New alloys are needed that operate at either much higher or much lower temperatures to prevent inadvertent actuation by ambient temperature fluctuations or to allow operation under adverse conditions. In this project, a framework for the rational design of novel SMAs will be developed. The framework will couple high-throughput computational and experimental techniques using a physical model that quantitatively relates material properties to physical parameters. This framework will be used to screen a range of alloys with the goal of identifying new families of SMAs with tailored shape memory properties in terms of operating temperature and functional stability. The computational strategy will start with a rapid screening for alloy endpoints that exhibit relevant characteristics of shape memory alloys. Physical models will then be trained to rapidly interpolate the thermomechanical properties of complex alloy compositions in the region spanning the endpoints. Materials systems of interest identified in the computational phase will then be investigated using state-of-the art experimental techniques. The methodology developed in this project and the ensuing fundamental understanding of the phase transformation responsible for the shape memory effect will provide a pathway to the targeted design of novel SMAs that may be used in a broad range of applications. The methodology is also easily transferred to the design and understanding of other classes of active materials that may be used in sensors or actuators. The computational methods that will be developed in this project fit very well within a graduate curriculum for computational materials science and will be incorporated in a graduate course on computational materials design. This project will also provide the context for a summer internship program for undergraduate students and for high-school students from local public schools that will provide a learning environment for students to experiment with materials, processing, modeling, and data science. Technical AbstractShape memory alloys (SMAs) undergo large recoverable shape changes as a result of thermoelastic martensitic transformations. These alloys have actuation energy densities that are an order of magnitude higher than any other solid-state actuator and are therefore of interest for lightweight and robust actuation systems. Nitinol, the most commonly used SMA, has actuation temperatures slightly above ambient temperature. To fully realize the potential of SMAs, new alloys are needed that can operate at either much higher or lower temperatures. In this project, a framework for the rational design of novel SMAs will be developed. The framework will couple high-throughput computational and experimental techniques using a physical model that quantitatively relates material properties to structural parameters. This framework will be used to screen a range of alloys with a goal of identifying new families of SMAs with tailored shape memory properties in terms of transformation temperature, hysteresis and stability. The effort will initially focus on known cubic binary and ternary phases with Fe, Cu, or Ni as the main component, and will expand as necessary. The computational screening strategy will start with a rapid screening for alloy endpoints that exhibit relevant characteristics of thermoelastic martensitic transformations and stability. Promising structures will be investigated in more detail, focusing on the temperature dependence of their properties. Physical models will then be trained to rapidly interpolate thermomechanical properties of complex alloy compositions in the region spanning the endpoints. Materials systems of interest identified in the computational phase will be investigated experimentally using sputter-deposited composition spreads combined with combinatorial nanocalorimetry and resistivity measurement techniques. The methodology developed in this project and the ensuing fundamental understanding will provide a pathway to the targeted design of novel SMAs that serve a broad range of applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术型形状记忆合金(SMA)是一种在温度变化时会发生较大形状变化的材料。这一独特的特性使SMA成为制造强大而轻便的执行器的理想选择，适用于各种应用。虽然SMA已经被许多商业技术采用--从智能手机中的图像稳定器到微创血管内手术工具--但商业上可用的SMA的相对较低的工作温度(通常在室温到100摄氏度之间)使它们不适合许多应用。需要在高得多或低得多的温度下工作的新合金，以防止环境温度波动造成的不经意的启动或允许在不利条件下工作。在这个项目中，将为新型SMA的合理设计开发一个框架。该框架将使用将材料属性与物理参数定量关联的物理模型，将高通量计算技术和实验技术结合起来。这一框架将被用来筛选一系列合金，目标是确定在工作温度和功能稳定性方面具有量身定制的形状记忆性能的新的SMA家族。计算策略将从快速筛选表现出形状记忆合金相关特征的合金终点开始。然后将对物理模型进行培训，以在跨越终点的区域内快速内插复杂合金成分的热机械性能。在计算阶段确定的感兴趣的材料系统然后将使用最先进的实验技术进行研究。本项目中开发的方法以及随后对导致形状记忆效应的相变的基本了解将为可能在广泛应用中使用的新型SMA的目标设计提供一条途径。这种方法也可以很容易地转移到其他类型的活性材料的设计和理解上，这些材料可能用于传感器或执行器。在这个项目中开发的计算方法非常适合计算材料科学的研究生课程，并将被纳入计算材料设计的研究生课程。该项目还将为本科生和当地公立学校的高中生提供暑期实习计划的背景，该计划将为学生提供一个学习环境，让他们试验材料、加工、建模和数据科学。技术摘要形状记忆合金(SMA)由于热弹性马氏体相变而发生大量可回复的形状变化。这些合金的致动能量密度比任何其他固态致动器都高一个数量级，因此对轻便和坚固的致动系统非常有意义。镍钛合金是最常用的形状记忆合金，其致动温度略高于环境温度。为了充分发挥SMA的潜力，需要能够在更高或更低温度下运行的新合金。在这个项目中，将为新型SMA的合理设计开发一个框架。该框架将使用将材料属性与结构参数定量关联的物理模型，将高通量计算技术和实验技术结合起来。这一框架将被用来筛选一系列合金，目标是确定在相变温度、滞后和稳定性方面具有量身定制的形状记忆性能的新的SMA家族。这项工作最初将专注于已知的以Fe、Cu或Ni为主要成分的立方二元和三元相，并将根据需要扩大。计算筛选策略将从快速筛选表现出热弹性马氏体相变和稳定性相关特征的合金终点开始。有希望的结构将被更详细地研究，重点是它们的性质与温度的关系。然后，将对物理模型进行培训，以在跨越终点的区域内快速插入复杂合金成分的热机械性质。在计算阶段确定的感兴趣的材料系统将使用溅射沉积的成分扩散结合组合纳米量度和电阻率测量技术进行实验研究。在这个项目中开发的方法和随之而来的基本理解将为服务于广泛应用的新型SMA的有针对性的设计提供一条途径。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。