The Electrochemical Surface Potential Due to Classical Point Charge Models Drives Anion Adsorption to the Air-Water Interface
Marcel D. Baer, Abraham C. Stern, Yan Levin, Douglas J. Tobias, and, Christopher J. Mundy

TL;DR
This study reveals that classical point charge models predict a significant electrochemical surface potential that drives anion adsorption to the air-water interface, contrasting with ab initio results and impacting understanding of ionic distributions near hydrophobic surfaces.
Contribution
It demonstrates that electrochemical surface potential in classical models significantly influences anion adsorption, challenging ab initio findings and affecting models of ionic behavior near interfaces.
Findings
Classical point charge models predict a strong electrochemical surface potential driving anions to the interface.
This electrochemical potential contrasts with ab initio simulations suggesting anions are repelled.
Implications for modeling ionic distributions near hydrophobic surfaces and proteins.
Abstract
We demonstrate that the driving forces for ion adsorption to the air-water interface for point charge models results from both cavitation and a term that is of the form of a negative electrochemical surface potential. We carefully characterize the role of the free energy due to the electrochemical surface potential computed from simple empirical models and its role in ionic adsorption within the context of dielectric continuum theory. Our research suggests that the electrochemical surface potential due to point charge models provides anions with a significant driving force to the air-water interface. This is contrary to the results of ab initio simulations that indicate that the average electrostatic surface potential should favor the desorption of anions at the air-water interface. The results have profound implications for the studies of ionic distributions in the vicinity of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
