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Kinetic data-driven development of light-interacting materials

Kinetic data-driven development of light-interacting materials
光相互作用材料的动力学数据驱动开发
批准号:
2748840
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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英文摘要
Over the past 5 years, lead halide perovskite (LHP) nanocrystals (NCs) have observed extended use in a multitude of optoelectronic applications due to their outstanding properties of which defect tolerance out competes traditionally applied chalcogenides NCs (e.g., CuInS2). Whilst significant attention has been drawn to their device performance, little recognition is paid towards their synthesis. Typically, NCs are synthesised using a batchwise hot injection protocol by which a precursor is swiftly introduced into the mother solution, containing appropriate ligands, to trigger nucleation and growth which takes place at millisecond timescales. Albeit a facile method, as-synthesised NCs suffer from dispersity in size and morphology due to the velocity at which the reaction proceeds in convolution with the time required to achieve uniform precursor concentration following injection. As a result, absence of understanding during earlier times in crystallisation renders poor control over as synthesised NCs thus detrimental optoelectronic performance. During this PhD, we attempt to probe early crystallisation kinetics of LHP and perovskite inspired materials to assess their intrinsic kinetics in governing the evolution of size and morphology. In tandem, we couple this with transient photophysical measurements to gain an understanding of defect formation during growth. Overall, we attempt to assess the interconnected relationship of synthesis, structure, and properties of confined perovskite NCs paving the way for controlled synthesis in wider optoelectronic applications.
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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: