High temporal resolution TEM imaging of dynamic processes in heterogenous catalysts

非均相催化剂动态过程的高时间分辨率 TEM 成像

• 批准号: 2113841
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2113841
• 关键词: temporal; resolution; TEM; imaging; dynamic

The overall aim of this project is to study changes in metallic and other nanoparticles involved in heterogeneous catalysis at ms timescales on order to probe transient structural changes. This project will use recent developments in fast direct electron detectors for transmission electron microscopy to achieve the necessary timing resolution. The potential impact of these studies is a better understanding of catalytic processes of interest to the industrial sponsor including automotive exhaust emissions, Fischer Tropsch catalysts and electrocatalysis in fuel cells and related systems. Initially the project will use new detectors operating at kHz frame rates to record the dynamic changes as catalysts evolve as a function of time and temperature giving particular insights into the early stages of catalyst activation and subsequent poisoning and deactivation. A key aim is to "handshake" the temporal resolutions accessible through molecular dynamics calculations and related computational simulations with those available experimentally. If this can be achieved it will potentially enable local nanoscale studies of the chemical kinetics of complex systems based on real space image data. An additional aim is to develop the use of machine learning based on convolution neural networks to develop image analysis tools suitable for analysing large data sets containing millions of images. In particular it will be necessary to develop automated approaches, similar to those used in structural biology to identify and classify significant numbers of nanoparticles in order to obtain reasonable statistics from heterogeneous ensembles. The research proposed relies heavily on unique instrumentation available at the electron Physical Sciences Imaging Centre. Specifically a new high speed direct electron detector operating at a frame rate in excess of 2KHz in 12 bit counting mode will be used to acquire time series data and the availability of both heating and in-situ gas reaction cells will facilitate studies of model catalyst systems under close to in-operando conditions. Within all of the above aims and objectives it will be necessary to ensure that the methods developed are robust to low dose data acquisition to ensure that electron beam induced effects are minimised. The final aim of the project will be to extend the methodologies developed to micron timescales. This will involve the use of a new time resolved TEM, unique in the UK currently under design and construction and due for delivery to the Rosalind Franklin Institute in 2020. The project falls within the EPSRC energy and physical sciences research areas The project is funded by Johnson Matthey plc through the iCase initiative

这个项目的总体目标是研究金属和其他纳米粒子在ms时间尺度上参与多相催化的变化，以探测瞬时结构变化。该项目将使用最新发展的快速直接电子探测器的透射电子显微镜，以实现必要的时间分辨率。这些研究的潜在影响是更好地了解工业赞助商感兴趣的催化过程，包括汽车尾气排放，费托催化剂和燃料电池及相关系统中的电催化。最初，该项目将使用以kHz帧速率运行的新探测器来记录催化剂随时间和温度变化的动态变化，从而特别了解催化剂活化的早期阶段以及随后的中毒和失活。一个关键的目标是“握手”的时间分辨率可通过分子动力学计算和相关的计算模拟与实验。如果能够实现这一点，它将有可能实现基于真实的空间图像数据对复杂系统化学动力学的局部纳米级研究。另一个目标是开发基于卷积神经网络的机器学习的使用，以开发适合分析包含数百万图像的大型数据集的图像分析工具。特别是有必要开发自动化方法，类似于结构生物学中使用的方法，以识别和分类大量的纳米颗粒，以便从异质性集合中获得合理的统计数据。拟议的研究在很大程度上依赖于电子物理科学成像中心提供的独特仪器。具体而言，一个新的高速直接电子检测器，在超过2KHz的帧速率在12位计数模式下运行，将被用来获取时间序列数据和加热和原位气体反应池的可用性将有利于模型催化剂系统的研究接近在operando条件下。在所有上述目的和目标中，有必要确保所开发的方法对低剂量数据采集具有鲁棒性，以确保电子束诱导效应最小化。该项目的最终目标是将所开发的方法扩展到微米时间尺度。这将涉及使用新的时间分辨TEM，目前正在设计和建造中，在英国是独一无二的，并将于2020年交付给Rosalind富兰克林研究所。该项目福尔斯属于EPSRC能源和物理科学研究领域。该项目由约翰逊万丰公司通过iCase计划资助。

项目成果

nNPipe: a neural network pipeline for automated analysis of morphologically diverse catalyst systems

• DOI: 10.1038/s41524-022-00949-7
• 发表时间: 2023-02
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Kevin P. Treder;Chen Huang;Cameron G. Bell;T. Slater;Manfred E. Schuster;Doğan Özkaya;Judy S. Kim;A. Kirkland