Metal particle nucleation in laminar jets

Jun Liu, Sean C. Garrick

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Gas to particle conversion in the form of nucleation within various flow systems plays a significant role in a variety of industrial and natural processes. Recently developed surface tension models offer increased accuracy in the modeling of metal particle nucleation. These models facilitate the probing of the effects of fluid, scalar, and thermal transport on nucleation in an accurate manner. In this work we investigate the formation of metal nanoparticles in laminar flows. The flows consist of metal vapor diluted in argon issuing into a cooler argon stream. The fluid, thermal, and chemical fields are obtained by solving the Navier Stokes, enthalpy, and mass-fraction transport equations while nucleation is simulated via a homogeneous nucleation model with size-dependent surface tension. This approach is attractive in that it promises to be more accurate than the classical nucleation theory (CNT) while maintaining much of its simplicity when coupling with fluid dynamics. The results show that the size-dependent surface tension nucleation model is more accurate than CNT and agrees well with physical data. Physically, the sensitivity of the saturation ratio to changes in temperature is shown to be greater than its sensitivity to mass fraction, highlighting the significance of differential molecular transport of energy and mass and the significance of non-unity Lewis numbers. More significantly, the size-dependent surface tension approach suggests that certain metals may have a maximum nucleation rate and further cooling-a strategy employed to increase particle nucleation rates-will actually decrease particle nucleation.

Original languageEnglish (US)
Article number073304
JournalPhysics of Fluids
Volume24
Issue number7
DOIs
StatePublished - Jul 10 2012

Bibliographical note

Funding Information:
Funding for this research was provided by the Institute on the Environment, and the University of Minnesota. Computational resources were provided by the Minnesota Supercomputing Institute.

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