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Gold Nanoparticle Melting: Effects of Size, Support Interaction, and Orientation
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Richard Palmer
Small Structures, Volume: 7, Issue: 1, Start page: e202500590
Swansea University Authors:
Theo Pavloudis, Richard Palmer
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DOI (Published version): 10.1002/sstr.202500590
Abstract
An understanding of nanoparticle (NP) melting is essential for both fundamental nanoscience and the design of high-temperature catalytic systems. We investigate the melting behavior of truncated octahedral gold NPs, ranging in size from 2 to 4 nm, supported on their edges, (100) or (111) facets, usi...
| Published in: | Small Structures |
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| ISSN: | 2688-4062 2688-4062 |
| Published: |
Wiley
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71131 |
| Abstract: |
An understanding of nanoparticle (NP) melting is essential for both fundamental nanoscience and the design of high-temperature catalytic systems. We investigate the melting behavior of truncated octahedral gold NPs, ranging in size from 2 to 4 nm, supported on their edges, (100) or (111) facets, using molecular dynamics simulations, with a machine-learning force field trained on density functional theory data. We systematically examine the effects of NP size, support interactions, and orientational dependence by applying spring-like constraints to specific facets or edges. Our results show that NP melting follows the liquid nucleation and growth model, with surface disorder preceding rapid melting at a critical temperature. Constraining the atoms to simulate contact with a support consistently raises the melting temperature, with stronger effects for smaller clusters, and for (100) facets compared with (111) facets, that is, there is an orientational effect. Importantly, the extent of the offset in melting temperature is quite independent of the interaction strength, implying that all support interactions can significantly stabilize small NPs. These findings provide a framework for more accurate predictions of nanoscale melting in practical catalytic environments. |
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| Keywords: |
force fields, gold, machine learning, melting, nanoclusters, nanoparticles |
| College: |
Faculty of Science and Engineering |
| Funders: |
Computational resources were provided by the Greek Research & Technology Network (GRNET) in the “ARIS” National HPC infrastructure under the project NOUS (017012), the project DataMind (555141862428) of AWS, and the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government. |
| Issue: |
1 |
| Start Page: |
e202500590 |