Instrument Development: 4-D Super Time Resolved Microscopy (4-D STReM) for Understanding Dynamics in Porous Materials

在化学系化学测量和成像项目的支持下，莱斯大学的兰德斯教授正在努力了解和优化多孔材料内部的过程。该项目的目标是开发一种具有前所未有的空间和时间分辨率的新型显微镜。兰德斯小组的新显微镜可以研究罕见事件如何影响多孔材料的效率，这些材料对催化、分离科学、腐蚀和生物学都很重要。人们已经确定，当光与分子和蛋白质相互作用时，操纵它是可能的。例如，兰德斯教授已经证明，通过改变光的相位，可以以比相机帧速率更快的速度对事件进行成像。通过结合新的数学和物理工具，目前的项目将产生一种新的仪器，以优化的三维空间和时间分辨率成像和跟踪多孔材料的快速动力学。这项研究工作的跨学科性质为参与的学生提供了光谱和材料科学，图像处理和现代信息理论界面的独特体验，并延续了莱斯大学在科学和技术领域跨学科活动的悠久历史。这笔拨款支持Landes教授为高中教师提供培训机会，将前沿科学纳入他们的课程材料，以及她创建夏季科学编程课程的新努力。近年来，一种新的显微镜技术被称为超时间分辨显微镜（STREM）。概念验证测量表明，STREM可以将传统宽视场相机的时间分辨率提高至少20倍。这一发展，如果与3d成像方法和信号处理的最新进展相结合，代表了解决驱动一系列界面材料特性的多尺度非线性动力学的机会。因此，当前项目的目标是开发和优化4-D stream，这是一种用于量化多孔材料非线性动力学和结构的化学成像方法。假设更好的三维亚衍射空间信息，加上改进的时间分辨率和信号处理算法，揭示了多孔界面上发生的非均匀质量传递、化学和生物机制。该项目将涉及硬件和软件的创新，以提高时间和二维空间分辨率。此外，还将开发一种新的三维跟踪算法。最后，新的显微镜将用于获取和管理一个机器学习库，该库能够区分常见的分析物、样品和仪器条件。通过提高空间和时间分辨率，本项目获得了一种新的仪器，用于表征多孔介质中分离的多标量物理和化学。此外，该项目将产生从大型3d数据集中提取信息的新算法。在应用方面，对孔隙和通道中物质运输的更详细描述是向预测分离迈出的一步，目前这是经验优化的，每年为工业、政府和学术目的带来数十亿美元的收益。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 Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Landes at Rice University is working to understand and optimize processes that occur within porous materials. The goal of the project is to develop a new type of microscope with unprecedented space and time resolution. The Landes group's new microscope allows the study of how rare events impact the efficiency of porous materials that are important for catalysis, separations science, corrosion, and biology. It has been established that it is possible to manipulate light as it interacts with molecules and proteins. For example, Professor Landes has already shown that by shaping light's phase, events faster than the camera frame rate can be imaged. By incorporating new mathematical and physical tools, the current project will result in a new instrument to image and track fast dynamics in porous materials with optimized 3-D space and time resolution. The interdisciplinary nature of this research effort provides participating students with a unique experience at the interface of spectroscopy and materials science, as well as image processing and modern information theory, and continues the strong history of cross-disciplinary activities in science and technology at Rice University. This grant supports Professor Landes to provide training opportunities to high school teachers to incorporate cutting edge science into their course materials, as well as her new effort to create a summer scientific programming course. Recently, a new microscopy technique called super temporal-resolved microscopy (STREM) was developed. Proof-of-concept measurements showed that STREM can improve the time resolution of traditional wide-field cameras by at least twenty times. This development, if combined with recent advances in 3-D imaging methods and signal processing, represents an opportunity to resolve the multiscale, nonlinear dynamics that drive a range of interfacial materials properties. Thus, the current project's objective is to develop and optimize 4-D STREM, a chemical imaging method for quantifying the nonlinear dynamics and structures in porous materials. It is hypothesized that better 3-D sub-diffraction spatial information, coupled with improved time resolution and signal processing algorithms, reveals heterogeneous mass transport, chemical, and biological mechanisms occurring at porous interfaces. The project will involve innovations in both hardware and software to improve the temporal and 2-D spatial resolution. Additionally, a new algorithm is to be developed to track in 3-D. Finally, the new microscope is to be used to acquire and curate a machine learning library capable of differentiating among common analyte, sample, and instrument conditions. A new instrument optimized for characterizing the multiscalar physics and chemistry that underlie separations in porous media, by improving both spatial and temporal resolution is obtained in this project. Further, the project will result in new algorithms to extract information from large 3-D data sets. In terms of applications, a more detailed description of mass transport in pores and channels is a step towards predictive separations, which are currently optimized empirically, amounting to billions of dollars each year for industry, government, and academic purposes.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.