43
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Thermodynamic activity in Zn–Cu–Sn–In liquid solder alloys: a comprehensive analysis using the molecular interaction volume model

, &
Pages 265-276 | Received 08 Dec 2023, Accepted 21 Feb 2024, Published online: 18 Mar 2024
 

Abstract

The molecular interaction volume model (MIVM) was employed to estimate the activity of each component in Zinc–Copper–Tin–Indium (Zn–Cu–Sn–In) quaternary liquid alloys at 1023 K, keeping indium content constant, i.e. xIn = 0.1, and varying molar ratios of Cu and Sn (xCu/xSn). Furthermore, the model has been used to determine the activity of each component of all sub-binary systems, namely Cu–Zn at 1200 K, Sn–Zn at 750 K, In–Zn at 700 K, Cu–Sn at 1400 K, Cu–In at 1073 K, In–Sn at 700 K, and Zn–Cu–In liquid ternary alloys at 1023 K for three molar ratios of Cu and In, i.e. xCu/xIn = 1:2, 1:1, and 2:1. We found that the activity deviations of Zn from Raoult’s law transform from positive to negative as the xCu/xIn ratio changes from 1:2 to 2:1. The estimated values were analyzed with the correspondent experimental results in the case of ternary and all the binary liquid alloys. There is a notable agreement observed between theoretical forecasts and practical findings in both binary and ternary systems. This work provides a thorough thermodynamic study of the Zn, Cu, Sn, and In quaternary systems.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 726.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.