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An augmented numerical inverse method for determining the composition-dependent interdiffusivities in alloy systems by using a single diffusion couple

Published online by Cambridge University Press:  18 July 2016

Weimin Chen ,
Jing Zhong  and
Lijun Zhang
Weimin Chen
Affiliation:
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, People's Republic of China
Jing Zhong
Affiliation:
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, People's Republic of China
Lijun Zhang*
Affiliation:
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, People's Republic of China
*
Address all correspondence to L. Zhang at xueyun168@gmail.com; lijun.zhang@csu.edu.cn
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Abstract

By solving the problems in the previous pragmatic method [Scr. Mater.90–91, 53–56 (2014)] and including the interdiffusion flux as the criteria, an augmented numerical inverse method was proposed and realized in a house-made code. The proposed augmented numerical inverse method was successfully applied to high-throughput determination of the composition-dependent interdiffusivities in different solid solution alloys ranging from binary, ternary to multicomponent systems by using a single diffusion couple. Moreover, the advance features of the augmented numerical inverse method were also demonstrated.

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Type
Research Letters
Information
MRS Communications , Volume 6 , Issue 3 , September 2016 , pp. 295 - 300
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Copyright
Copyright © Materials Research Society 2016 

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References

1. Dayananda, M.A. and Sohn, Y.H.: A new analysis for the determination of ternary interdiffusion coefficients from a single diffusion couple. Metall. Mater. Trans. A 30, 535 (1999).Google Scholar
2. Bouchet, R. and Mevrel, R.: A numerical inverse method for calculating the interdiffusion coefficients along a diffusion path in ternary systems. Acta Mater. 50, 4887 (2002).Google Scholar
3. Jaques, A.V. and LaCombe, J.C.: A stable and efficient regression approach for determination of coefficients in linear multicomponent diffusion. J. Phase Equilib. Diffus. 33, 181 (2012).Google Scholar
4. Morral, J.E. and Hopfe, W.D.: Validation of multicomponent diffusivities using one diffusion couple. J. Phase Equilib. Diffus. 35, 666 (2014).Google Scholar
5. Santra, S. and Paul, A.: Estimation of intrinsic diffusion coefficients in a pseudo-binary diffusion couple. Scr. Mater. 103, 18 (2015).Google Scholar
6. Belova, I.V., Sohn, Y.H., and Murch, G.E.: Measurement of tracer diffusion coefficients in an interdiffusion context for multicomponent alloys. Philos. Mag. Lett. 95, 416 (2015).CrossRefGoogle Scholar
7. Kaufman, L. and Ågren, J.: CALPHAD, first and second generation–birth of the materials. Scr. Mater. 70, 3 (2014).CrossRefGoogle Scholar
8. Olson, G.B. and Kuehmann, C.J.: Materials genomics: from CALPHAD to flight. Scr. Mater. 70, 25 (2014).Google Scholar
9. Gottstein, G.: Integral Materials Modeling: Towards Physics–Based Through–Process Models (Wiley–VCH Verlag GmBH&Co. KGaA, Germany, 2007).CrossRefGoogle Scholar
10. Chen, W., Zhang, L., Du, Y., Huang, B., and Tang, C.: A pragmatic method to determine the composition-dependent interdiffusivities in ternary systems by using a single diffusion couple. Scr. Mater. 90–91, 53 (2014).Google Scholar
11. Xu, H., Chen, W., Zhang, L., Du, Y., and Tang, C.: High-throughput determination of the composition-dependent interdiffusivities in Cu-rich fcc Cu–Ag–Sn alloys at 1073 K. J. Alloys Compd. 644, 687 (2015).Google Scholar
12. Kirkaldy, J.S. and Young, D.J.: Diffusion in the Condensed State (The Institute of Metals, London, 1987).Google Scholar
13. Manning, J.R.: Diffusion and the Kirkendall shift in binary alloys. Acta Metall. 15, 817 (1967).CrossRefGoogle Scholar
14. Chen, W., Zhang, L., Du, Y., and Huang, B.: Viscosity and diffusivity in melts: from unary to multicomponent systems. Philos. Mag. 94, 1552 (2014).Google Scholar
15. Dayananda, M.A.: An analysis of concentration profiles for fluxes, diffusion depths, and zero-flux planes in multicomponent diffusion. Metall. Trans. A 14, 1851 (1983).Google Scholar
16. Ben Abdellah, A., Gasser, J.G., Bouziane, K., Grosdidier, B., and Busaidi, M.: Experimental procedure to determine the interdiffusion coefficient of miscibility gap liquid alloys: case of GaPb system. Phys. Rev. B 76, 174203 (2007).Google Scholar
17. Lechelle, J., Noyau, S., Aufore, L., Arredondo, A., and Audubert, E.: Volume interdiffusion coefficient and uncertainty assessment for polycrystalline materials. Diffus.– Fundamentals.org. 17, 1 (2012).Google Scholar
18. Chen, J., Zhang, C., Wang, J., Chen, W., Tang, Y., Zhang, L., and Du, Y.: Thermodynamic description, diffusivities and atomic mobilities in binary Ni–Os system. CALPHAD 50, 118 (2015).Google Scholar
19. Zhang, W.: Thermodynamics and kinetic analysis of the formation of microstructure in multi-component graded cemented carbides and its experimental investigation. Ph.D. Thesis, Central South University, Changsha, 2015.Google Scholar
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