journal article·2014-09

Critical evaluation and thermodynamic optimization of Mg–Ga system and effect of low pressure on phase equilibria

Youn-Bae Kang, Jiwon Jeong, Sang Ho Oh

Calphad· Volume 46
Publisher page

Abstract

Abstract A complete literature review and critical evaluation of the Mg–Ga binary system are presented. All stable phases known in this system were thermodynamically modeled in the framework of CALPHAD. Liquid phase was modeled using the Modified Quasichemical Model in the pair approximation which takes into account short-range ordering. α-(Mg) solid solution of hcp structure was modeled as a substitutional solution. All intermetallic phases Mg 5 Ga 2 , Mg 2 Ga, MgGa, MgGa 2 , Mg 2 Ga 5 , as well as Ga-rich phase, were treated as stoichiometric compounds. Gas phase was assumed to behave as an ideal solution. Thermodynamic optimization was carried out by evaluating enthalpic and entropic contributions to the Gibbs free energy of all phases independently. It showed that the optimized model parameters along with the model equations could reproduce the available and reliable experimental data in the Mg–Ga system. Representation of partial excess Gibbs free energy of Mg in Ga-rich liquid was improved compared to the previous thermodynamic modeling. Low pressure phase equilibria in this system were analyzed using the developed thermodynamic model, and it was compared with a recent observation of an Mg nanopillar fabricated by Ga + ion beam in a focused ion beam (FIB), followed by in-situ heating in a transmission electron microscope (TEM). Reported surface melting of Mg nanopillar in the TEM column pressure (~10 −10 bar) is attributed to the fact that decreasing pressure enhanced sublimation of Mg from the Mg nanopillar significantly, leaving Ga-rich liquid.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Magnesium Alloys: Properties and Applications · Metal and Thin Film Mechanics · Aluminum Alloys Composites Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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