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Updated: May 2, 2026

Preparation of Binary and Ternary Deep Eutectic Systems
Published on: October 31, 2019
The melting properties of choline chloride in the representation of deep eutectic system phase diagrams
Ahmad Alhadid1, Mirjana Minceva2, João A P Coutinho3
1College of Engineering and Technology, American University of the Middle East, Kuwait.
Abstract:
Choline chloride (ChCl) is the cornerstone of deep eutectic solvents (DES), yet its melting properties remain an important source of uncertainty in the thermodynamic description of eutectic systems. Since ChCl decomposes before melting, reported values of its melting temperature (597 to 687 K) and enthalpy (4.3 and 13.8 kJ mol-1) vary widely, while for the heat capacity change between solid and liquid states, only an estimate exists at 298.15 K. All these factors propagate inconsistencies into solid-liquid equilibrium (SLE) modelling, activity coefficient estimation, and data-driven approaches such as machine learning. In this work, we critically assess the melting properties of ChCl reported in the literature and evaluate their impact on the representation of phase diagrams of eutectic systems. By analysing the description of representative binary systems (ChCl + ionic compound, ChCl + water, and ChCl + urea), we demonstrate that neglecting the heat capacity change upon melting can lead to a substantial underestimation of the melting enthalpy. Thermodynamic modelling using NRTL-based approaches further reveals a strong interdependence between melting properties, liquid-phase nonideality, and the interpretation of experimental SLE data. Our results indicate that melting enthalpy values between 8 and 10 kJ mol-1, combined with a non-negligible heat capacity change upon melting in the range 20-40 J mol-1 K-1, provide the most consistent description across systems. This study clarifies the role of ChCl melting properties in the thermodynamic description of eutectic systems, providing a robust framework for enhancing the reliability of phase diagram interpretation, model development, and data-driven predictions in DES research.
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