Abstract
Quantification of the restructuring and migrating behaviors of working nanocatalysts at high spatiotemporal resolution is of a rigorous challenge but of vital significance to provide insights into the microstructural intrinsic of catalytic stability under the stimuli of the reaction environment. In this work, a deep learning-driven in situ TEM video quantification has been developed, capable of identifying and tracking every nanoparticle within the multi-particles video recorded during catalytic reaction. Through this methodology, evolutionary tracks of NiAu particles during catalyzing CO2 hydrogenation and CuPd particles in a redox environment have been resolved. These quantitative behaviors of reconstruction and migration derived from in situ TEM data, for the first time, unravel the surface-anisotropic catalytic reaction over individual particle, which is consistently measured as multiple changing descriptors including particle diameter/area, circularity, and migration velocity. Such reaction and microstructure inhomogeneity deconstructed from working nanocatalyst offers convincing elucidation about the micro-dynamic mechanism of catalyst coalescence and migration. This paper highlights the merits of interdisciplinary study rooting in artificial intelligence-driven in situ TEM analysis.
S. Liu, Q. Zhao, S. Han, Z. Jia, X. Hong, W. Liu, Dynamics of catalyst nanoparticles quantified from in situ tem video, Nano Today, 2024, 59, 102505. DOI: 10.1016/j.nantod.2024.102505.
