Dissertation/Thesis Abstract

Primitive Model Simulations and Mean-Field Studies of Electric Double Layers
by Giera, Brian, Ph.D., University of California, Santa Barbara, 2014, 140; 3637410
Abstract (Summary)

When a charged surface, such as an electrode, colloid, or protein, is submerged into an electrolyte or ionic liquid, ions within the fluid rearrange into electric double layers (EDLs) that electrostatically screen the interfacial charge. The electrostatic potential and ion distributions within EDLs have long been described by mean-field local-density approximations (LDAs) that assume flat electrodes, uncorrelated ions, and bulk forms for the chemical potential. The objective of this work is to elucidate LDA failure mechanisms and supplement or supplant mean-field treatments of electrochemical systems that fail to capture correlated behavior. We develop an exceedingly general method, which requires no a priori model and identifies whether EDLs in a given electrolyte can obey a LDA, or whether more advanced approaches (e.g. integro-differential equations, atomistic simulations, etc.) are required, irrespective of the source of LDA breakdown. We combine continuum-level theoretical studies with complementary simulations in order to critically assess the accuracy of LDA models of implicit solvent electrolytes with equal and differently sized ions. We also pose a novel LDA model that seeks to address solvation, polarizability, and finite-size interactions present in actual and simulated EDLs with explicit solvent.

Indexing (document details)
Advisor: Squires, Todd M., Shell, M. Scott
Commitee: Chmelka, Bradley F., Gibou, Frederic, Kober, Edward M.
School: University of California, Santa Barbara
Department: Chemical Engineering
School Location: United States -- California
Source: DAI-B 76/02(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Chemical engineering
Keywords: Electric double layer, Electrochemistry, Ion interactions, Local-density approximations, Mean-field theory, Molecular dynamics
Publication Number: 3637410
ISBN: 9781321201864
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