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Cited by 356

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Heat Dissipation for Au Particles in Aqueous Solution: Relaxation Time versus Size

Hu, Min, Hartland, Gregory V.·2002·The Journal of Physical Chemistry B

Abstract

The rate of energy dissipation from Au nanoparticles to their surroundings has been examined by pump−probe spectroscopy. These experiments were performed for particles suspended in aqueous solution, with average sizes ranging from 4 to 50 nm in diameter. The results show that energy relaxation is a very nonexponential process. Fitting the data to a stretched exponential function yields a characteristic time scale for relaxation that varies from ca. 10 ps for the smallest particles examined (∼4 nm diameter) to almost 400 ps for the 50 nm diameter particles. The relaxation times are proportional to the square of the radius, but do not depend on the initial temperature of the particles (i.e., the pump laser power). For very small particles, the time scale for energy dissipation is comparable to the time scale for electron−phonon coupling, which implies that significant energy loss occurs before the electrons and phonons reach thermal equilibrium within the particle.

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Metadata

Authors
Hu, M., & Hartland, G. V.
Venue
The Journal of Physical Chemistry B
Published
2002-06-21
Volume
106
Issue
28
Pages
7029-7033
ISSN
  • 1520-5207 (ISSN-L)
  • 1520-6106
OpenAlex
W1984994174
Cited by
356
References
1
FWCI
8.62
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Heat Dissipation for Au Particles in Aqueous Solution: Relaxation Time versus Size. https://app.cochat.ai/papers/10.1021/jp020581+
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Hu, M., & Hartland, G. V. (2002). Heat Dissipation for Au Particles in Aqueous Solution: Relaxation Time versus Size. The Journal of Physical Chemistry B. https://doi.org/10.1021/jp020581%2B

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.