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Machine Learning for Revealing Spatial Dependence among Nanoparticles: Understanding Catalyst Film Dewetting via Gibbs Point Process Models
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    C: Surfaces, Interfaces, Porous Materials, and Catalysis

    Machine Learning for Revealing Spatial Dependence among Nanoparticles: Understanding Catalyst Film Dewetting via Gibbs Point Process Models
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    • Ahmed Aziz Ezzat
      Ahmed Aziz Ezzat
      Department of Industrial & Systems Engineering, Rutgers University, New Brunswick, New Jersey 08901-8554, United States
    • Mostafa Bedewy*
      Mostafa Bedewy
      Department of Industrial Engineering, Department of Chemical and Petroleum Engineering, Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
      *Email: [email protected]
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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2020, 124, 50, 27479–27494
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    https://doi.org/10.1021/acs.jpcc.0c07765
    Published December 9, 2020
    Copyright © 2020 American Chemical Society

    Abstract

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    We combine in situ environmental transmission electron microscopy (E-TEM) with automated image processing and statistical machine learning to uniquely formulate interpretable mathematical models and accurate simulation tools for complex nanoscale phenomena involving coupled physical and chemical processes and interactions that are otherwise hard to model. In particular, there is a need for a better understanding, characterization, and prediction of the proximity effects among dense populations of metal nanocatalysts as they form and evolve over time. Here, we leverage point process theory, a branch of statistical machine learning, to “learn” the spatial dependencies among ensembles of adjacent alumina-supported iron nanoparticles from a time sequence of E-TEM images. We construct a set of point process models to make statistical inferences about the nature of spatial dependencies that govern the rapid formation, or “popping” of nanoparticles during thin film dewetting, concomitant with metal reduction in the presence of acetylene at 750 °C. We show that nanoparticles exhibit strong dispersion behavior, i.e., new nanoparticles pop in dispersed locations at a predictable distance from their existing territorial neighbors. We also show that Gibbs point processes adequately describe the pairwise interactions underlying such time-dependent spatial variations. Further, we build on our machine-learned models to develop a computational simulation tool capable of producing accurate spatiotemporal simulations of nanoparticle formation at finer time resolutions and larger spatial domains than those of experimental observations. This is a much needed capability to overcome current limitations in computational methods supporting the design, analysis, and control of the collective behavior of nanocatalyst populations.

    Copyright © 2020 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.0c07765.

    • CVD microreactor for in situ study of catalyst nanoparticle formation by dewetting inside E-TEM; demonstration of inadequacy of off-the-shelf software packages for E-TEM image segmentation; time evolution of image-specific d0 for all 22 E-TEM images; image segmentation results for all 22 E-TEM images; histograms of nanoparticle size distribution for all 22 segmented E-TEM images; formulations for λ(s) with likelihood test ratio results for image #22; point process modeling results for the first four E-TEM images (i.e., at t = 0.00–2.00 s); Voronoi tessellations, density maps, and histograms of Voronoi distances for all 22 E-TEM images; algorithm for STIM; time evolution of the estimated values for the Softcore model parameters (both from fitting of experimental data and from STIM extrapolations); time evolution of Voronoi tessellation and Voronoi distances from simulation results; spatially and temporally extended simulation results using both softcore and HPP models with comparing the dynamics of strongest interaction and nearest-neighbor distance; videos of nanoparticle popping dynamics showing locations, areal density, and particle count as a function of time (PDF)

    • Time evolution of areal density heat maps and number of nanoparticles for our temporally extended simulations (first case study) (MP4)

    • Time evolution of areal density heat maps and number of nanoparticles for our spatially extended simulations (second case study) (MP4)

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    This article is cited by 13 publications.

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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2020, 124, 50, 27479–27494
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.0c07765
    Published December 9, 2020
    Copyright © 2020 American Chemical Society

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