The activity and lifetime of heterogeneous catalysts are intimately linked with their structural stability in reactive environments. However, it can be challenging to understand and predict how reactive environments lead to nanoparticle coarsening via center of mass motion and Ostwald ripening and how evaporation can lead to mass loss. In this work, we develop and exploit advanced data analysis tools to track the temporal evolution of nanoparticles as a function of time, temperature, and reactive environment using transmission electron microscopy. The first portion of the talk will describe our development of a fast and highly accurate image segmentation approach based on deep learning. We describe how a systematic investigation of dataset preparation, neural network architecture, and accuracy evaluation lead to a tool for determining the size and shape of nanoparticles in high pixel resolution TEM images. In the second half of the talk, we will show how we exploit this approach to generate rich data regarding the complexities of nanoparticle coarsening, ripening, and evaporation. Au nanoparticles created through colloidal synthesis approaches undergo a combination of both evaporation and diffusive mass transport. We have developed an analytical model that describes this process and shows how local and long-range particle interactions through diffusive transport affect the evaporation process. The extensive data of the evolution of several hundred particles allows us to determine physically reasonable values for the model parameters, quantify the particle size at which Gibbs-Thompson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. Furthermore, by exploring how particles evolve as a function of temperature, we can determine the activation energy of the process with high accuracy. Kinetic Monte Carlo simulations and Density Functional Theory calculations indicate that evaporation proceeds through the formation of specific surface facets, and with computational determination of the activation of the process closely matching the experimental observations. Throughout the second half of the presentation, emphasis will be placed on the ways that data quantification through automated image processing allows extraction of detailed physical information from the process of nanoparticle evaporation.
Eric A. Stach1,2 James P. Horwath1, Colin Lehman-Chong3, Leena Vyas1, Aleksandra Vojvodic3, Peter W. Voorhees4
1. Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, PA 19104.
2. Laboratory for Research on the Structure of Matter, University of Pennsylvania, Philadelphia, PA 19104.
3. Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA 19104.
4. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208.
stach@seas.upenn.edu, +1 765 3463460
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