Collaborative Research: Dynamics of Short Range Order in Multi-Principal Element Alloys

合作研究：多主元合金中的短程有序动力学

• 批准号: 2348955
• 负责人: Gregory Thompson
• 金额: $ 35.99万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348955&HistoricalAwards=false
• 关键词: Collaborative; Research; Dynamics; Short; Range

NON-TECHNICAL SUMMARYNearly all metals used in practice are alloys, meaning that they are a mixture of different types of metal atoms. Depending on the alloy, different atomic types arrange in either an ordered or disordered way within a crystal. Alloys where the different atoms have a disordered or random arrangement can benefit from improved properties including increased strength or corrosion resistance. It has recently been proposed that in some disordered alloys the actual atomic arrangement could be more subtle, appearing to be ordered (or non-random) over short distances and disordered over longer distances. This phenomenon, known as short range ordering, is implicated in the exceptional properties of a recently developed group of alloys known as multi-principal element alloys (MPEAs) that have particular promise for next-generation applications in power generation and national defense. However, one difficulty is that short range ordering remains problematic to measure. This research will use a microscope called an atom probe to detect the types and locations of individual atoms, but with an uncertainty that can make the measurement of subtle ordering inconclusive. Here, artificial intelligence will be used to detect any short range ordering present within the data. Using this new means of analysis, the project will then test the idea that short range ordering in stable conditions is independent of how the alloy was made. In doing so, this project will have a broad impact on understanding how to engineer short range ordering in alloys by means of their processing, all of which has implications for how MPEAs will develop as next-generation materials. Alongside the research, artificial intelligence teaching modules will be created to expose and educate middle and high school students in the use of this emerging technology. These will be disseminated through a workshop for secondary school teachers on how to integrate materials-centric examples into physics, chemistry, and physical science classrooms.TECHNICAL SUMMARYMulti-principal element alloys (MPEAs), often called high-entropy alloys, are an emerging alloy class with initial evidence of exceptional mechanical properties and significant compositional design flexibility. However, the rational design of MPEAs is hindered by a lack of fundamental knowledge about the chemical short range order (SRO), which are the local correlations in the distribution of atomic species. This project will characterize the evolution of SRO in a model CrCoNi MPEA to evaluate two hypotheses; first, that SRO in MPEAs reaches a stable state that is independent of the fabrication conditions, and second, that the relaxation time to the stable SRO is governed by diffusion kinetics (which themselves depend on the SRO). Samples will be fabricated by vacuum arc melting, direct current sintering, and high-pressure torsion consolidation to generate measurably different initial SRO states. SRO characterization will be done by atom probe tomography (APT) cross-correlated with electron scattering for a pair distribution function as well as energy filtered high resolution transmission electron microscopy imaging. The APT datasets will be analyzed by a machine learning approach where the data is modeled as a sample from an underlying pairwise-interaction Markov point process. Experimental data from a series of isothermal annealing experiments will be used to calibrate a mathematical model for the mutual interactions of the SRO and the self-diffusivity. The model will be used to develop time-temperature-SRO diagrams for MPEAs to be integrated into service. The project is expected to deliver: (1) Maturation of a fundamental scientific understanding of MPEAs’ compositional stability from which these materials can be deployed into service in extreme environments. (2) A robust machine learning APT analysis method that expands the technique to address high solute clustering characteristics in alloys. (3) The development of the next-generation STEM workforce at the graduate level as well as through secondary school teachers via a materials camp that instructs how to incorporate materials into the physics, chemistry, and physical science curriculum.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.

实践中使用的所有金属的非技术总结都是合金，这意味着它们是不同类型的金属原子的混合物。取决于合金，不同的原子类型的排列，无论是在晶体中以有序或无序的方式排列。不同原子具有干扰或随机排列的合金可以受益于提高强度或腐蚀性的提高特性。最近有人提出，在某些无序合金中，实际原子排列可能更微妙，似乎在短距离内被订购（或非随机），并且在较长距离内被无序。这种现象被称为短距离排序，在最近开发的合金组的特性中实施了称为多主体元素合金（MPEAS），这些合金对发电和国防中的下一代应用具有特别有望。但是，一个困难的是，短距离排序仍然是有问题的测量。这项研究将使用称为原子探针的显微镜来检测单个原子的类型和位置，但不确定性可以使微妙的订购尚无定论。在这里，人工智能将用于检测数据中存在的任何短距离顺序。然后，使用这种新的分析方法，该项目将测试以下想法：在稳定条件下进行短距离顺序是这样做的，该项目将对如何通过其处理方式来理解如何在合金中进行短距离排序，所有这些都对MPEAS将如何作为下一代材料开发。除研究外，还将创建人工智能教学模块，以揭露和教育中学生，以使用这种新兴技术。这些将通过中学教师的研讨会进行散布，以了解如何将以材料为中心的例子整合到物理​​，化学和物理科学课堂中。技术摘要多质元素合金（MPEAS）通常称为高凝胶合金，具有出色的合金类，具有出色的机械性能和重要的合成性设计和重要的合成性设计。但是，由于缺乏有关化学短距离顺序（SRO）的基本知识，这是原子种分布中的局部相关性，因此阻碍了MPEA的合理设计。该项目将在模型CRCONI MPEA中表征SRO的演变，以评估两个假设。首先，MPEA中的SRO达到了独立于制造条件的稳定状态，其次，稳定SRO的放松时间受扩散动力学的控制（本身取决于SRO）。样品将通过真空弧熔化，直流烧结和高压扭转巩固来制造样品，以生成可测量的不同初始SRO状态。 SRO表征将通过与电子散射以及配对分布函数以及能量过滤的高分辨率透射电子显微镜成像交叉相关的原子探针断层扫描（APT）进行。 APT数据集​​将通过机器学习方法分析，其中将数据从基础成对互动Markov点过程中建模为样本。来自一系列等温退火实验的实验数据将用于校准SRO相互作用和自扩张率的数学模型。该模型将用于开发用于将MPEA集成到服务中的MPEA的时间温度图。预计该项目将提供：（1）对MPEA的综合稳定性的基本科学理解的成熟，这些理解可以在极端环境中将这些材料部署到服务中。 （2）一种健壮的机器学习APT分析方法，该方法扩展了技术以解决合金中高固体聚类特性。 （3）通过材料营的材料训练营，通过中学训练营的发展下一代STEM劳动力的发展，该材料训练营指示如何将材料纳入物理，化学和物理科学课程中。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被视为珍贵的支持。