TY - JOUR

T1 - Prediction of Temperature Dependence of Impurity Diffusion Coefficients in Liquid Metal Based on a Hard-Sphere Model from Measurements Using Shear Cell Technique and Stable Density Layering

AU - Shiinoki, Masato

AU - Yamada, Noriyuki

AU - Tanaka, Anna

AU - Suzuki, Shinsuke

N1 - Funding Information:
This work was supported by Grant-in-Aid for Scientific Research (C) Grant Number JP19K04990, Grant-in-Aid for JSPS Fellows Grant Number JP20J14950, Grant-in-Aid from the Mitsubishi Materials Corporation in the 2019 fiscal year, and conducted as a part of the research project in 2019 for Research Assistant in Kagami Memorial Research Institute for Materials Science and Technology, Waseda Univ. Furthermore, we would like to thank the Environmental Safety Center, Waseda University, for the sample analysis; and Kimura Foundry Co., Ltd. for the financial support.
Publisher Copyright:
© 2021, The Minerals, Metals & Materials Society and ASM International.

PY - 2022/2

Y1 - 2022/2

N2 - This study aims to establish predictive formulas for the temperature dependence of impurity diffusion coefficient based on a hard-sphere (HS) model and the measurement results. The impurity diffusion coefficients of Sb, Bi, and In in liquid Sn were measured using the shear cell technique and stable density layering at 773 K and 973 K (500 °C and 700 °C, respectively) with suppression of natural convection. The temperature dependence of the impurity diffusion coefficient can be predicted by multiplying the ratio of the solvent to the solute of the following three factors by the self-diffusion coefficient of the solvent as the slope: (i) the square of mean atomic diameter, (ii) the first peak of the pair distribution function calculated by the HS model, and (iii) the square root of the converted atomic weight. If the ratio of the atomic diameter is close to one, the temperature dependence of the impurity diffusion coefficient can also be predicted with an accuracy similar to the abovementioned relationship by multiplying the following two factors by the self-diffusion coefficient of the solvent as the slope: (i) the atomic diameter ratio of the solvent to the solute and (ii) the thermodynamic factor. The predictive formulas based on the HS model showed an accuracy of approximately ±10 pct for the experimental values from 573 K to 973 K (300 °C to 700 °C).

AB - This study aims to establish predictive formulas for the temperature dependence of impurity diffusion coefficient based on a hard-sphere (HS) model and the measurement results. The impurity diffusion coefficients of Sb, Bi, and In in liquid Sn were measured using the shear cell technique and stable density layering at 773 K and 973 K (500 °C and 700 °C, respectively) with suppression of natural convection. The temperature dependence of the impurity diffusion coefficient can be predicted by multiplying the ratio of the solvent to the solute of the following three factors by the self-diffusion coefficient of the solvent as the slope: (i) the square of mean atomic diameter, (ii) the first peak of the pair distribution function calculated by the HS model, and (iii) the square root of the converted atomic weight. If the ratio of the atomic diameter is close to one, the temperature dependence of the impurity diffusion coefficient can also be predicted with an accuracy similar to the abovementioned relationship by multiplying the following two factors by the self-diffusion coefficient of the solvent as the slope: (i) the atomic diameter ratio of the solvent to the solute and (ii) the thermodynamic factor. The predictive formulas based on the HS model showed an accuracy of approximately ±10 pct for the experimental values from 573 K to 973 K (300 °C to 700 °C).

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U2 - 10.1007/s11663-021-02319-y

DO - 10.1007/s11663-021-02319-y

M3 - Article

AN - SCOPUS:85119530084

SN - 1073-5615

VL - 53

SP - 29

EP - 40

JO - Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science

JF - Metallurgical and Materials Transactions B: Process Metallurgy and Materials Processing Science

IS - 1

ER -