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No Access Submitted: 10 May 2022 Accepted: 27 June 2022 Accepted Manuscript Online: 27 June 2022 Published Online: 21 July 2022

  • A simple theory for interfacial properties of dilute solutions

J. Chem. Phys. 157, 034703 (2022); https://doi.org/10.1063/5.0098579
Varun Mandalaparthy and W. G. Noida)
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  • Varun Mandalaparthy: Investigation (equal).
  • W. G. Noid: Investigation (equal).
ABSTRACT
Recent studies suggest that cosolute mixtures may exert significant non-additive effects upon protein stability. The corresponding liquid–vapor interfaces may provide useful insight into these non-additive effects. Accordingly, in this work, we relate the interfacial properties of dilute multicomponent solutions to the interactions between solutes. We first derive a simple model for the surface excess of solutes in terms of thermodynamic observables. We then develop a lattice-based statistical mechanical perturbation theory to derive these observables from microscopic interactions. Rather than adopting a random mixing approximation, this dilute solution theory (DST) exactly treats solute–solute interactions to lowest order in perturbation theory. Although it cannot treat concentrated solutions, Monte Carlo (MC) simulations demonstrate that DST describes the interactions in dilute solutions with much greater accuracy than regular solution theory. Importantly, DST emphasizes a fundamental distinction between the “intrinsic” and “effective” preferences of solutes for interfaces. DST predicts that three classes of solutes can be distinguished by their intrinsic preference for interfaces. While the surface preference of strong depletants is relatively insensitive to interactions, the surface preference of strong surfactants can be modulated by interactions at the interface. Moreover, DST predicts that the surface preference of weak depletants and weak surfactants can be qualitatively inverted by interactions in the bulk. We also demonstrate that DST can be extended to treat surface polarization effects and to model experimental data. MC simulations validate the accuracy of DST predictions for lattice systems that correspond to molar concentrations.

ACKNOWLEDGMENTS

Parts of this research were conducted with Advanced CyberInfrastructure computational resources provided by the Institute for CyberScience at the Pennsylvania State University (http://icds.psu.edu). In addition, parts of this research were conducted with XSEDE resources awarded by Grant No. TG-CHE170062. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation (Grant No. ACI-1548562).104104. J. Towns, T. Cockerill, M. Dahan, I. Foster, K. Gaither, A. Grimshaw, V. Hazlewood, S. Lathrop, D. Lifka, G. D. Peterson, R. Roskies, J. R. Scott, and N. Wilkins-Diehr, Comput. Sci. Eng. 16, 62 (2014). https://doi.org/10.1109/mcse.2014.80 The authors gratefully acknowledge Dr. Pho Bui and Professor Paul Cremer for many useful conversations regarding this work.

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