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

光电纳米材料的功能特性往往来源于其尺寸；例如，我们可以通过降维获得新的亚稳定材料，从波长尺度材料获得强光-物质相互作用，或者由于大的表面积与体积比而增强环境感知能力。然而，许多重要的纳米材料是使用“自下而上”或自导向技术生产的，这可能导致几何形状和材料质量的异质性。为了实现未来的光电器件和电路，我们必须了解并减少这种异质性。该项目的目标是让学生开发用于高通量表征的工具和技术，以便以单元素灵敏度测量和优化此类纳米材料。在此过程中，学生还将开发一个图像和光谱测量数据集，以实现该项目的第二个目标。一个具有多参数异质性的足够大的数据集可以用来理解几何形状、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该数据集，作为将纳米材料中的无序性作为多样化数据集的新途径。该项目的主要成果是高通量技术的发展，对纳米级材料（特别是III-V半导体和基于量子点的）无序的具体理解，用于测量的数据存储的开发以及挖掘这些数据的分析工具，以便更深入地了解纳米级材料的生长和应用。

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.