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Updated: Feb 7, 2026

Gold Nanoparticle Synthesis
Published on: July 10, 2021
Study of Morphology and Optical Properties of Gold Nanoparticle Aggregates under Different pH Conditions
Darson D Li1, Xi Gu1, Victoria Timchenko1
1School of Mechanical and Manufacturing Engineering , The University of New South Wales (UNSW) , Sydney , New South Wales 2052 , Australia.
Abstract:
Gold nanoparticle (GNP) aggregation has a strong influence on the plasmonic resonance and hence the effectiveness in various photothermal applications. In relation to this, a comprehensive numerical model is developed to simulate and characterize the GNP aggregation process at various particle volume fractions and base fluid pH levels. Computational fluid dynamics techniques are utilized to model the base fluid, whereas discrete phase modeling is adopted in determining the nanoparticle trajectories. Two-way coupling is implemented to handle the particle-fluid interactions. Discrete dipole approximation approach is utilized to further examine the absorption and scattering efficiency of various GNP aggregate structures. At lower particle volume fraction, short chain-like structures are formed in the particle aggregation process, with a more complex interconnected "particle network" structure formed at higher particle volume fractions. With the three base fluid pH levels investigated, GNP aggregates are more compact with larger fractal dimensions and higher mean coordination numbers at pH = 3.5, whereas a more "loose" structure formed at pH = 6.7 and 9.4 because of larger electrostatic repulsive forces as a result of changes in the zeta potential and Debye length of the GNPs. Among the typical GNP aggregate structures characterized in this paper, the chain-like tetramer demonstrates the highest absorption efficiency of 1.83 at 700 nm wavelength-comparable to the plasmonic resonance of a nanorod-which lies in the optical window of biological tissue. Predictions of GNP optical properties are found to be in good agreement with the published experimental data.
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