Reversible temperature-induced shape transition of Pt nanoparticles supported on Al <sub>2</sub> O <sub>3</sub>

Supported platinum catalysts are widely used in industry for hydrogenation reactions. The variations of the electronic and geometric properties of Pt nanoparticles due to temperature can greatly affect their reactivity. In this work, we use <i>in situ</i> X-ray absorption spectroscopy and environmental transmission electron microscopy to study the effect of H<sub>2</sub> and temperature on the shape and electronic properties of 1.8 nm average diameter Pt nanoparticles supported on Al<sub>2</sub>O<sub>3</sub>. We utilize actively trained machine learning potentials with uniform acceptance force-bias Monte Carlo (fbMC) to estimate the structural distribution of Pt<sub>15</sub>/γ-Al<sub>2</sub>O<sub>3</sub> (110) clusters at finite temperatures. Our predicted cluster geometries are consistent with experimental data showing the nanoparticles reversibly change shape from 3D hemispheres at low temperatures (35-100 °C) to 2-2.5D rafts at higher temperatures (200-400 °C). Furthermore, experiments and computations indicate that the contraction in Pt-Pt bond distances and higher electron density on Pt at higher temperatures are attributed primarily to the change in nanoparticle shape and associated increased interaction with Al<sub>2</sub>O<sub>3</sub>. Our results show the fluxional nature of supported Pt nanoparticles driven by temperature changes.

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Publication Details

Journal
Nanoscale Advances
Published
2025-11-07
DOI
https://doi.org/10.1039/d5na00930h
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04

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article

Reversible temperature-induced shape transition of Pt nanoparticles supported on Al 2 O 3

Ayman M. Karim, Adam S. Hoffman, Simon R. Bare, Vinson Liao et al.
2 citations
Nanoscale Advances
Machine Learning in Materials Science
1.04
article

Reversible temperature-induced shape transition of Pt nanoparticles supported on Al 2 O 3

Ayman M. Karim, Adam S. Hoffman, Simon R. Bare, Vinson Liao, Salman A. Khan, Dionisios G. Vlachos, Md Raian Yousuf, Abhijit Shrotri, Piaoping Yang, Thomas K. Hansen, Ricardo Pool-Mazun
article en
2 citations

Abstract

Supported platinum catalysts are widely used in industry for hydrogenation reactions. The variations of the electronic and geometric properties of Pt nanoparticles due to temperature can greatly affect their reactivity. In this work, we use <i>in situ</i> X-ray absorption spectroscopy and environmental transmission electron microscopy to study the effect of H<sub>2</sub> and temperature on the shape and electronic properties of 1.8 nm average diameter Pt nanoparticles supported on Al<sub>2</sub>O<sub>3</sub>. We utilize actively trained machine learning potentials with uniform acceptance force-bias Monte Carlo (fbMC) to estimate the structural distribution of Pt<sub>15</sub>/γ-Al<sub>2</sub>O<sub>3</sub> (110) clusters at finite temperatures. Our predicted cluster geometries are consistent with experimental data showing the nanoparticles reversibly change shape from 3D hemispheres at low temperatures (35-100 °C) to 2-2.5D rafts at higher temperatures (200-400 °C). Furthermore, experiments and computations indicate that the contraction in Pt-Pt bond distances and higher electron density on Pt at higher temperatures are attributed primarily to the change in nanoparticle shape and associated increased interaction with Al<sub>2</sub>O<sub>3</sub>. Our results show the fluxional nature of supported Pt nanoparticles driven by temperature changes.

Nanoscale AdvancesVol. 8(2)
Hokkaido University (JP), SLAC National Accelerator Laboratory (US), Stanford Synchrotron Radiation Lightsource (US), University of Virginia (US), Virginia Tech (US), University of Delaware (US), Indian Institute of Technology Kanpur (IN), Technical University of Denmark (DK)
U.S. Department of Energy, Hokkaido University, Office of Science, Basic Energy Sciences, SLAC National Accelerator Laboratory
Openalex Percentile: Top 21%
Machine Learning in Materials Science
1.04
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