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

在化学系大分子、超分子和纳米化学（MSN）项目的支持下，康涅狄格大学的赵静教授和陈坤教授以及布朗大学的陈欧教授将先进的荧光显微镜技术与数据科学方法相结合，研究钙钛矿量子点（QDs）的闪烁行为。来自单个量子点的荧光不是恒定的，而是呈现出发光的周期，随后是黑暗的周期。简而言之，他们眨眼。闪烁是量子点的普遍行为，理解其机制对许多应用至关重要。然而，确定闪烁机制是具有挑战性的，因为它必须一次研究一个量子点，并且必须研究多个量子点才能得出有意义的结论。为了应对这一挑战，赵教授、陈教授和陈教授将与他们的学生一起应用现代统计学习方法来分析具有不同成分的钙钛矿量子点中的荧光闪烁。这些发现可能会对量子点在从LED显示屏到生物成像等应用中的应用产生影响。研究方法和发现将整合到拓展活动和虚拟化学实验室模块中，以及向化学学生介绍数据科学技术的研讨会中。该项目将应用现代统计学习和数据科学方法来分析单个钙钛矿量子点的荧光波动，目的是获得机制洞察力，从而避免在解释单粒子测量数据时经常遇到的隐含偏差。该项目将利用三位研究人员的知识和技能，他们是材料科学、单粒子光谱和现代数据科学方法方面的专家，测量和分析大量钙钛矿量子点的荧光信号。该团队将使用光学显微镜技术检查具有不同组成的正离子和阴离子位点、组成和晶格畸变的量子点，并通过应用统计严谨的分析方法，更深入地了解控制其眨眼行为的光物理机制。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.