Advances in Data Driven Quantitative Materials Characterisation

数据驱动的定量材料表征的进展

• 批准号: RGPIN-2022-04762
• 负责人: Britton, Thomas
• 金额: $ 3.35万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758187
• 关键词: Advances; Data; Driven; Quantitative; Materials

In this programme of work, my team and I will develop new tools and approaches to understand the microstructure in advanced materials used in clean energy applications. Advances in characterisation are essential for the effective and timely development of engineering materials, and this enables us to have a direct link between the manufacture of materials and their long-term performance in high value, high risk, and societally useful applications. These tools will be developed through the solving of challenges posed by high tech industries where I have established collaborations and expertise, e.g. developing lightweight alloys for lower CO2 aerospace applications; longer lasting and safer materials for nuclear reactors; and quantifying the next generation of additive manufactured alloys and optimising manufacturing routes. To achieve this, I am going to lead my team in the development of new microstructural characterisation tools, including sparse data collection, correlative microscopy and new modes of imaging in two and three dimensions. Advances in two-dimensional characterisation will involve the development and use of direct electron detectors for STEM and SEM and diffraction pattern based microstructural imaging and characterisation. These will be supplemented through the use of correlative approaches to combine other microstructural signals, e.g. energy dispersive X-ray spectroscopy (EDX), to provide chemical and structural information and increase the contrast of each measurement point. We will further their use also via direct control of the electron beam, to enable us to sample large areas quickly and efficiently, e.g. via sampling of one point per microstructural domain, as well as using data science tools (including local clustering algorithms, such as principal component analysis) to maximise the signal to noise for very small precipitates. Recent investment at UBC, via a CFI IF project, will enable me to develop these techniques for full 3D characterisations using the high volume pFIB instrument. My new tools are well suited towards the characterisation of metals and ceramics, which have direct and clear use in our low carbon future (e.g. future light weight aerospace applications, hydrogen transport networks, more efficient manufacturing routes) and they also have potential to be applied to a wide range of other material systems. In the clean energy space, I am excited to work with partners to develop our approaches to explore solar cell materials, including perovskites, as well as geomaterials where we need to have understanding of the structure and phase transformations in future CO2 storage reservoirs and minerals found in sites where development of geothermal energy systems will be developed.

在这项工作计划中，我和我的团队将开发新的工具和方法，以了解清洁能源应用中使用的先进材料的微观结构。表征方面的进展对于工程材料的有效和及时开发至关重要，这使我们能够在材料的制造和它们在高价值、高风险和社会有用的应用中的长期性能之间建立直接的联系。这些工具将通过解决高科技行业带来的挑战来开发，我在这些行业建立了合作和专业知识，例如，开发用于较低二氧化碳航空航天应用的轻质合金；用于核反应堆的更持久和更安全的材料；以及量化下一代添加剂制造的合金和优化制造路线。为了实现这一目标，我将带领我的团队开发新的微结构表征工具，包括稀疏数据收集、相关显微镜和二维和三维成像的新模式。二维表征方面的进展将涉及用于STEM和扫描电子显微镜的直接电子探测器的开发和使用以及基于衍射图案的微结构成像和表征。将通过使用相关方法来结合其他微结构信号，例如能量色散X射线光谱学(EDX)，以提供化学和结构信息，并增加每个测量点的对比度，从而补充这些信息。我们还将通过直接控制电子束来进一步使用它们，以使我们能够快速高效地对大片区域进行采样，例如通过对每个微结构域一个点进行采样，以及使用数据科学工具(包括本地聚类算法，如主成分分析)来最大化极小沉淀物的信噪比。最近在UBC的投资，通过CFI IF项目，将使我能够开发这些技术，使用大容量pFIB仪器进行全3D表征。我的新工具非常适合金属和陶瓷的表征，它们在我们的低碳未来具有直接和明确的用途(例如，未来的轻质航空航天应用、氢运输网络、更高效的制造路线)，它们还有可能应用于广泛的其他材料系统。在清洁能源领域，我很高兴能与合作伙伴合作，开发我们的方法来探索太阳能电池材料，包括钙钛矿，以及岩土材料，在这些材料中，我们需要了解未来二氧化碳储存库的结构和相变，以及将开发地热系统的地点发现的矿物。