Big datasets for nanomaterials; High-throughput imaging and spectroscopy of nano-opto-electronics

纳米材料大数据集；

• 批准号: 2519969
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2519969
• 关键词: Big; datasets; nanomaterials; throughput; imaging

Optoelectronic nanomaterials often derive their functional properties from their size; for instance, we can obtain new meta-stable materials over reduced dimensionality, strong light-matter interaction from wavelength-scale materials, or enhanced environmental sensing ability due to a large surface-area to volume ratio. However, many important nanomaterials are produced using "bottom-up" or self-directed techniques, which can lead to heterogeneity in geometry and material quality. To enable future optoelectronic devices and circuits, we must understand and reduce this heterogeneity.The objective of this project is for the student to develop tools and techniques for high-throughput characterization in order to measure and optimize such nanomaterials with single-element sensitivity. In the process, the student will also develop a dataset of imagery and spectroscopy measurements which enable a second objective of this project. A sufficiently large dataset with multi-parameter heterogeneity can be used to understand the interplay between geometry, material quality, and functional performance. The student will develop analytical approaches to mine this dataset using statistical and machine learning approaches, as a new route to exploit the disorder in nanomaterials as a diverse data set.The primary outputs of this project are the development of high-throughput techniques, specific understanding of disorder in nanoscale materials (specifically III-V semiconductor and quantum-dot based), the development of a datastore for measurements and the analytical tools to mine this for deeper understanding of growth and application of nanoscale materials.

光电纳米材料通常从它们的尺寸获得它们的功能特性;例如，我们可以获得新的亚稳态材料，降低维度，从波长尺度材料中获得强的光-物质相互作用，或者由于大的表面积与体积比而增强环境传感能力。然而，许多重要的纳米材料是使用“自下而上”或自主技术生产的，这可能导致几何形状和材料质量的不均匀性。为了使未来的光电器件和电路，我们必须了解和减少这种异质性。这个项目的目标是为学生开发高通量表征的工具和技术，以测量和优化这种纳米材料与单元素的灵敏度。在此过程中，学生还将开发图像和光谱测量数据集，从而实现本项目的第二个目标。具有多参数异质性的足够大的数据集可用于了解几何形状、材料质量和功能性能之间的相互作用。学生将使用统计和机器学习方法开发分析方法来挖掘此数据集，作为利用纳米材料中的无序作为多样化数据集的新途径。该项目的主要成果是开发高通量技术，（特别是基于III-V半导体和量子点的），开发用于测量和分析的工具来挖掘这一点，以更深入地了解纳米材料的生长和应用。