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Page 22 of 25 Park et al. J Mater Inf 2023;3:5 https://dx.doi.org/10.20517/jmi.2022.37

the miscibility gap data reported in the present study. It is concluded that the previously reported “sharp-
peaked” miscibility gap was in error. Kang and Pelton discussed that many immiscible alloys exhibit a
“flattened” miscibility gap, which should be explained by considering non-random mixing (clustering)
[24]
between atoms . The MQM is a suitable model for treating non-random mixing. The BW random mixing
model might be used, but with more adjustable model parameters. Furthermore, Kang and Pelton
[24]
demonstrated that the MQM performs well when extrapolating the binary miscibility gap to higher-order
systems, which is essential in developing a multicomponent thermodynamic database for numerous tramp
elements in steel.

CONCLUSIONS
Thermodynamic modeling of the Fe-Sn binary system was carried out with new experimental investigations
employing DSC, electromagnetic levitation technique, and contact angle measurement. The experiments
provided key experimental data which helped the modeling of liquid alloy’s Gibbs energy. The monotectic
temperature (Liquid → bcc + Liquid ) and binodal for the liquid miscibility gap were measured, which
1
2
could resolve discrepancies reported in the literature. The liquid phase was modeled using the MQM in the
pair approximation, which was known to be superior in modeling solutions exhibiting demixing tendencies.
The following points were improved during the present thermodynamic modeling:

(1) Description of the previously controversial miscibility gap

(2) Gibbs energies of FeSn and FeSn 2

(3) Low temperature solubility of Sn in bcc

The developed database can be used as a part of a larger database for steel systems containing tramp
elements such as Sn.

DECLARATIONS
Authors’ contributions
Performed experimental works: Park WB, Bernhard M, Presoly P
Performed literature survey: Park WB, Bernhard M
Performed thermodynamic modeling Park WB, Bernhard M
Writing manuscript: Bernhard M, Park WB, Kang YB
Fund acquisition: Kang YB

Availability of data and materials
Not applicable.

Financial support and sponsorship
This research was supported by Basic Research program (NRF-2021R1F1A1049973), Brain Pool program
(NRF-2022H1D3A2A01081708), both funded by the Ministry of Science and ICT through the National
Research Foundation of Korea.

Conflicts of interest
All authors declared that there are no conflicts of interest.