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计划的支持下，莱斯大学的兰德斯教授正在努力了解和优化在多孔材料中发生的过程。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。兰德斯团队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、腐蚀和生物学非常重要。已经证实，当光与分子和蛋白质相互作用时，有可能操纵它。例如，兰德斯教授已经证明，通过塑造光的相位，可以成像比相机帧速率更快的事件。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中的快速动力学。这项研究的跨学科性质为参与研究的学生提供了在光谱学和材料科学以及图像处理和现代信息理论的交汇点上的独特体验，并延续了赖斯大学在科学和技术领域开展跨学科活动的强大历史。这笔赠款支持兰德斯教授为高中教师提供培训机会，将尖端科学纳入他们的课程材料，以及她为创建暑期科学编程课程所做的新努力。最近，一种新的显微技术--超时间分辨显微镜(STEM)被发展起来。概念验证测量表明，Strem可以将传统广域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，将代表着解决驱动一系列界面材料属性的多尺度、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 Strem，这是一种用于量化多孔材料中的非线性动力学和结构的化学成像方法。假设更好的三维亚绕射空间信息，再加上改进的时间分辨率和信号处理算法，可以揭示发生在多孔界面上的非均匀质量传输、化学和生物机制。该项目将在硬件和软件方面进行创新，以提高时间和二维空间分辨率。此外，还将开发一种新的算法来在3-D中跟踪。最后，新的显微镜将被用来获取和管理一个机器学习库，该库能够区分常见的分析物、样本和仪器条件。在这个项目中，通过提高空间和时间分辨率，获得了一种优化的新仪器，用于表征多孔介质中的多标量物理和化学分离。此外，该项目将产生从大型3-D数据集中提取信息的新算法。在应用方面，更详细地描述毛孔和渠道中的质量传输是朝着预测性分离迈出的一步，目前根据经验进行了优化，每年用于工业、政府和学术目的的资金总计数十亿美元。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.