Abstract
The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing in situ transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Data mining of particle evolution statistics allows us to determine physically reasonable values for the model parameters, quantify the particle size at which the Gibbs-Thomson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. This approach can be applied to a wide range of supported nanoparticle systems, allowing quantitative insight into the mechanisms that control their evolution in reactive environments.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 5324-5329 |
| Number of pages | 6 |
| Journal | Nano letters |
| Volume | 21 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 23 2021 |
Funding
J.P.H. and E.A.S. acknowledge support through the National Science Foundation, Division of Materials Research, Metals and Metallic Nanostructures Program under Grant 1809398. This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC00127044. P.W.V. acknowledges financial assistance under award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design. We would like to thank Dmitri Zakharov and Kim Kisslinger of the Center for Functional Nanomaterials for assisting with experiments, Katherine Elbert and Christopher Murray from the University of Pennsylvania Department of Chemistry for help with nanoparticle synthesis and self-assembly.
Keywords
- catalyst stability
- data mining
- in situ transmission electron microscopy
- supported catalysts
ASJC Scopus subject areas
- Bioengineering
- General Chemistry
- General Materials Science
- Condensed Matter Physics
- Mechanical Engineering