Investigation of the adsorption behaviour of acetone at the surface of ice. A grand canonical Monte Carlo simulation study

Literature Information

Publication Date 2008-09-08
DOI 10.1039/B808466A
Impact Factor 3.676
Authors

Paul N. M. Hoang, Sylvain Picaud


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Abstract

The adsorption isotherm of acetone at the surface of Ih ice has been determined by a set of grand canonical Monte Carlo simulations at 200 K, by varying the chemical potential of acetone in the simulations. The obtained isotherm can be described by the Langmuir theory up to a certain relative pressure value (i.e., about 0.07); above which the isotherm increasingly deviates from the Langmuir form. This deviation mainly originates from the increasing importance of the lateral dipolar interactions. Further, above this pressure the adsorption sites are no longer equivalent: the adsorbed acetone molecules are aligned in three different ways. In one of these orientations the acetone molecule forms two, while in another one it forms one hydrogen bond with the surface waters, whereas in the third preferred orientation no hydrogen bonding occurs between the adsorbed molecule and the ice surface.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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