CAS: Collaborative Research: Integrative Learning of Fluorescence Fluctuations in Perovskite Quantum Dots Using A Data Science Assisted Single-Particle Approach

CAS：协作研究：使用数据科学辅助单粒子方法综合学习钙钛矿量子点荧光涨落

• 批准号: 2203700
• 负责人: Ou Chen
• 金额: $ 22万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203700&HistoricalAwards=false
• 关键词: CAS; Collaborative; Research; Integrative; Learning

With support from the Macromolecular, Supramolecular, and Nanochemistry (MSN) program in the Division of Chemistry, Professors Jing Zhao and Kun Chen of the University of Connecticut and Professor Ou Chen of Brown University are combining advanced fluorescence microscopy techniques with data science approaches to study the blinking behavior of individual perovskite quantum dots (QDs). The fluorescence coming from a single QD is not constant, but rather exhibits periods of light emission followed by periods of darkness. In short, they blink. Blinking is a universal behavior of QDs and understanding its mechanism is critical to many applications. However, determining the blinking mechanism is challenging because it must be studied in one QD at a time, and one must study many QDs to draw meaningful conclusions. To address this challenge, Professors Zhao, Chen, and Chen along with their students will apply modern statistical learning approaches to analyze fluorescence blinking in many perovskite QDs with varying compositions. The discoveries could have implications for how QDs are used in applications ranging from LED displays to biological imaging. The research methods and findings will be integrated into outreach activities and virtual chemistry laboratory modules, as well as workshops introducing data science techniques to chemistry students.The project will apply modern statistical learning and data science approaches to analyze fluorescence fluctuations in single perovskite QDs with the goal of gaining mechanistic insight that is free of the implicit bias often encountered when interpreting data from single-particle measurements. The project will leverage the knowledge and knowhow from three investigators who are experts in materials science, single-particle spectroscopy, and modern data science approaches to measure and analyze the fluorescence signals of a large number of individual perovskite QDs. The team will examine QDs with varying composition in their cation and anion sites, compositions, and lattice distortions using optical microscopy techniques, and through the application of statistically rigorous analysis methods, gain a deeper understanding of the photophysical mechanisms that govern their blinking behavior.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.

在化学系中的大分子，超分子和纳米化学（MSN）计划的支持下，康涅狄格大学的Jing Zhao和Kun Chen教授和Brown University的Ou Chen和Brown University的Ou Chen将高级荧光显微镜技术结合了数据科学方法，以研究数据科学方法，以研究个人perovskite量化量化量的闪烁行为。来自单个QD的荧光不是恒定的，而是表现出光发射的时期，然后是黑暗时期。简而言之，他们眨眼。眨眼是QD的普遍行为，了解其机制对于许多应用至关重要。 但是，确定闪烁的机制是具有挑战性的，因为必须一次在一个QD中进行研究，并且必须研究许多QD以得出有意义的结论。为了应对这一挑战，Zhao，Chen和Chen教授及其学生将采用现代统计学习方法来分析许多具有不同作品的钙钛矿QD中荧光闪烁。这些发现可能会对从LED显示到生物成像的应用中如何使用QD。 The research methods and findings will be integrated into outreach activities and virtual chemistry laboratory modules, as well as workshops introducing data science techniques to chemistry students.The project will apply modern statistical learning and data science approaches to analyze fluorescence fluctuations in single perovskite QDs with the goal of gaining mechanistic insight that is free of the implicit bias often encountered when interpreting data from single-particle measurements.该项目将利用三位研究人员的知识和知识，这些研究人员是材料科学，单粒子光谱和现代数据科学方法的专家，以测量和分析大量单个perovskite QD的荧光信号。该团队将使用光学显微镜技术以及通过应用统计严格的分析方法来检查其阳离子和阴离子站点，组成和晶格扭曲的QD，并通过应用统计学严格的分析方法来更深入地理解，从而更深入地理解了对闪光的机制，这些机制可以控制其眨眼的行为。这些奖项反映了NSF的构建范围和范围的构建范围。审查标准。