Thermal Conductivity of Metastable Ionic Liquid [$C_{2}mim$][$CH_{3}SO_{3}$]

TL;DR

This study measures the thermal conductivity of a novel ionic liquid, [$C_{2}mim$][$CH_{3}SO_{3}$], revealing liquid metastability below its melting point and challenging existing predictive models for heat transfer applications.

Contribution

It provides detailed thermal conductivity data for a new ionic liquid and investigates its metastable liquid phase, offering insights for heat transfer fluid applications.

Findings

Thermal conductivity measured between 278 and 355 K with 2% uncertainty.

Melting temperature determined as 307.8 ± 1 K.

Existing models failed to predict the experimental data accurately.

Abstract

Ionic liquids have been suggested as new engineering fluids, namely in the area of heat transfer, as alternatives to current biphenyl and diphenyl oxide, alkylated aromatics and dimethyl polysiloxane oils, which degrade above 200 {\deg}C and pose some environmental problems. Recently, we have proposed 1-ethyl-3-methylimidazolium methanesulfonate, [$C_{2}mim$][$CH_{3}SO_{3}$], as a new heat transfer fluid, because of its thermophysical and toxicological properties. However, there are some interesting points raised in this work, namely the possibility of the existence of liquid metastability below the melting point (303 K) or second order-disorder transitions ($\lambda$-type) before reaching the calorimetric freezing point. This paper analyses in more detail this zone of the phase diagram of the pure fluid, by reporting accurate thermal-conductivity measurements between 278 and 355 K with an estimated uncertainty of 2% at a 95% confidence level. A new value of the melting temperature is also reported, $T_{melt}$ = 307.8 $\pm$ 1 K. Results obtained support liquid metastability behaviour in the solid-phase region and permit the use of this ionic liquid at a heat transfer fluid at temperatures below its melting point. Thermal conductivity models based on Bridgman theory and estimation formulas were also used in this work, failing to predict the experimental data within its uncertainty.