High temporal resolution TEM imaging of dynamic processes in heterogenous catalysts

该项目的总体目标是在毫秒时间尺度上研究金属和其他纳米颗粒参与多相催化的变化，以探测瞬态结构变化。该项目将使用最新发展的透射电子显微镜快速直接电子探测器来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，费希尔托普施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帧率工作的新型探测器，记录催化剂随时间和温度变化的动态变化，从而深入了解催化剂激活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”通过分子动力学计算和相关的计算模拟可获得的时间分辨率与那些可用的实验。如果这可以实现，它将有可能使基于真实空间图像数据的复杂系统的化学动力学的局部纳米级研究成为可能。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用于识别和分类大量纳米颗粒的方法，以便从异质集成中获得合理的统计数据。这项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。具体来说，将使用一种新的高速直接电子探测器，以超过2KHz的帧率在12位计数模式下工作，以获取时间序列数据，并且加热和原位气体反应电池的可用性将有助于在接近操作条件下研究模型催化剂系统。在上述所有目的和目标中，有必要确保所开发的方法对低剂量数据采集具有鲁棒性，以确保将电子束诱导效应降至最低。该项目的最终目标是将开发的方法扩展到微米时间尺度。这将涉及使用一种新的时间分辨率的瞬变电磁法，这种电磁法在英国是独一无二的，目前正在设计和建造中，预计将于2020年交付给罗莎琳德·富兰克林研究所。该项目属于EPSRC能源和物理科学研究领域，由庄信万丰公司通过iCase倡议资助

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