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Published November 29, 2012 | Published
Journal Article Open

Hydrophobic Segregation, Phase Transitions and the Anomalous Thermodynamics of Water/Methanol Mixtures

Abstract

When water and methanol are mixed, the entropy of mixing decreases, whereas mixing simple liquids normally leads to an increase in entropy. One speculation on the origin of the anomaly involves formation of water icebergs next to the hydrophobic methanol group, while more recent theories point to nanoscale clustering of methanol molecules. To elucidate the origin of this effect, we carried out extensive molecular dynamics calculations on water/methanol mixtures ranging from 0 to 100% and applied the 2PT method to extract the entropy and free energy changes of each component as a function of concentration. We find that water molecules lose at most 1/35 of their liquid entropy in mixtures. Methanol molecules, on the other hand, lose 3 times as much entropy as the water molecules, and their rotational entropy contains the signature of the entropic loss. We find that methanol has a discontinuous specific heat profile in these mixtures with a maximum at 40% methanol. These results do not support the iceberg model of immobilized waters and instead suggests a molecular mechanism of hydrophobic segregation at low methanol concentration where ordering of the methanol molecules bury the hydrophobic group away from the water phase. For higher methanol concentrations, there is insufficient water to accomplish this effect, and the system freely mixes and transitions to one better described as water dissolved into methanol.

Additional Information

© 2012 American Chemical Society. Published In Issue November 29, 2012; Article ASAP: November 16, 2012; Just Accepted Manuscript November 05, 2012; Received: September 29, 2012; Revised: November 05, 2012. Partial support for this work was from the U.S. National Science Foundation [Grants CTS-0608889 and CTS-0548774]. The MSC computational facilities were provided by ARODURIP and ONR-DURIP. We thank Dr. Shiang-Tai Lin (NTU) and Dr. Mario Blanco (Caltech) for useful discussion on the 2PT method. Additional computing resources were provided by a generous allocation from the KISTI supercomputing facility.

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August 19, 2023
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