arXiv subjects

Kara D. Fong

Publications and source records attributed to Kara D. Fong.

8 recordsLinked to original sources

arXiv2026

Breaking Water at Graphene Defects

Water dissociation at solid surfaces underpins processes ranging from corrosion and catalysis to electrochemistry and photovoltaics. Defects often serve as reactive sites for dissociation, yet how solvation influences water dissociation at such sites remains poorly understood. Here, we use state-of-the-art machine-learned interatomic potentials to explore water dissociation at defective graphene-water interfaces. We show that solvation qualitatively changes the reaction mechanism at a graphene single vacancy (SV), opening pathways that are absent for an isolated water molecule. Whereas the gas-phase process proceeds via a single concerted channel, the solvated SV splits water through two competing pathways: a basic route forming SV-H and OH-(aq), and an acidic route forming SV-OH and H3O+(aq). These lower-barrier pathways produce distinct chemisorbed intermediates that enhance graphene-water adsorption. Accordingly, even a simple carbon vacancy gives rise to unexpectedly rich interfacial chemistry, coupling surface chemistry to interfacial charge and wettability, with implications for carbon functionalization and nanofluidic transport.

physics.chem-ph↗

arXiv2026

False Metallization in Short-Ranged Machine Learned Interatomic Potentials

Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs are short-ranged and do not accurately capture long-ranged electrostatic interactions. At interfaces with polar solvents, such as water, this deficiency can drive unphysical long-distance dipolar alignment far away from the interface. Here we reveal that neglecting long-ranged physics leads to spurious metallization of the water layer due to artificially large fluctuations of the total solvent dipole, similar to the electron rearrangement observed to prevent polar catastrophes at polar interfaces. This metallization is eliminated in MLIPs that explicitly include long-ranged electrostatics. Our results showcase a fundamental flaw of short-ranged MLIPs, highlighting that long-ranged electrostatics are essential for studying systems with a polar-liquid component, especially if one is interested in electronic properties.

physics.chem-ph↗

arXiv2025

Breaking the Air-Water Paradigm: Ion Behavior at Hydrophobic Solid-Water Interfaces

Hydrophobic solid-water interfaces underpin processes in nanofluidics, electrochemistry, and energy technologies. Microscopic insights into these systems are often inferred from our understanding of the air-water interface, which is assumed to exhibit similar behavior. Here, we challenge this paradigm by combining heterodyne-detected vibrational sum-frequency generation spectroscopy with machine-learning molecular dynamics simulations at first-principles accuracy to investigate the graphene-NaCl(aq) interface as a prototypical hydrophobic solid-water system. Spectroscopic results suggest that ions have a minimal effect on the structure of the interfacial water, while simulations reveal that Na$^{+}$ and Cl$^{-}$ accumulate densely at the surface. Together, these findings reveal a new adsorption mechanism that departs from the established air-water interface paradigm, where interfacial ion adsorption is typically associated with, and often detected through, pronounced alteration of the interfacial water alignment and orientation. This difference arises because ions cannot penetrate the solid boundary and reside at a similar depth as the interfacial water molecules. As a consequence, large ion populations can be accommodated within the extended two-dimensional hydrogen-bond network at the interface, causing only minor local distortions but significant changes to its longer-range connectivity. These results reveal a distinct mechanism of electrolyte organization at aqueous-carbon interfaces, relevant to energy applications, where performance is highly sensitive to the local organization of interfacial water.

physics.chem-ph↗

arXiv2025

How reactive is water at the nanoscale and how to control it?

Nanoconfined water plays a key role in nanofluidics, electrochemistry, and catalysis, yet its reactivity remains a matter of debate. Prior studies have reported both enhanced and suppressed water self-dissociation relative to the bulk, but without a consistent explanation. Here, using enhanced sampling molecular dynamics with machine-learned potentials trained at first-principles accuracy, we investigate dissociation behavior in water confined within 2D slit pores and nanodroplets, using graphene and hexagonal boron nitride as model materials. We find that reactivity is extremely sensitive to water density, confinement width, geometry, material flexibility, and surface chemistry. Despite this complexity, we show that chemical potential -- together with interfacial interactions -- governs dissociation trends and explains the variability observed in prior studies. This thermodynamic perspective reconciles previous contradictions and reveals how nanoscale environments can drastically shift water reactivity. Our findings provide molecular-level insight and offer a design lever for modulating water chemistry at the nanoscale.

physics.chem-ph↗

arXiv2025

Spontaneous Surface Charging and Janus Nature of the Hexagonal Boron Nitride-Water Interface

Boron, nitrogen and carbon are neighbors in the periodic table and can form strikingly similar twin structures-hexagonal boron nitride (hBN) and graphene-yet nanofluidic experiments demonstrate drastically different water friction on them. We investigate this discrepancy by probing the interfacial water and atomic-scale properties of hBN using surface-specific vibrational spectroscopy, atomic-resolution atomic force microscopy (AFM), and machine learning-based molecular dynamics. Spectroscopy reveals that pristine hBN acquires significant negative charges upon contacting water at neutral pH, unlike hydrophobic graphene, leading to interfacial water alignment and stronger hydrogen bonding. AFM supports that this charging is not defect-induced. pH-dependent measurements suggest OH- chemisorption and physisorption, which simulations validate as two nearly equally stable states undergoing dynamic exchange. These findings challenge the notion of hBN as chemically inert and hydrophobic, revealing its spontaneous surface charging and Janus nature, and providing molecular insights into its higher water friction compared to carbon surfaces.

physics.chem-ph↗

arXiv2025

Protons accumulate at the graphene-water interface

Water's ability to autoionize into hydroxide and hydronium ions profoundly influences surface properties, rendering interfaces either basic or acidic. While it is well-established that protons show an affinity to the air-water interface, a critical knowledge gap exists in technologically relevant surfaces like the graphene-water interface. Here we use machine learning-based simulations with first-principles accuracy to unravel the behavior of the hydroxide and hydronium ions at the graphene-water interface. Our findings reveal that protons accumulate at the graphene-water interface, with the hydronium ion predominantly residing in the first contact layer of water. In contrast, the hydroxide ion exhibits a bimodal distribution, found both near the surface and towards the interior layers. Analysis of the underlying electronic structure reveals local polarization effects, resulting in counterintuitive charge rearrangement. Proton propensity to the graphene-water interface challenges the interpretation of surface experiments and is expected to have far-reaching consequences for ion conductivity, interfacial reactivity, and proton-mediated processes.

physics.chem-ph↗

arXiv2022

Inferring global dynamics from local structure in liquid electrolytes

Ion transport in concentrated electrolytes plays a fundamental role in electrochemical systems such as lithium ion batteries. Nonetheless, the mechanism of transport amid strong ion-ion interactions remains enigmatic. A key question is whether the dynamics of ion transport can be predicted by the local static structure alone, and if so what are the key structural motifs that determine transport. In this paper, we show that machine learning can successfully decompose global conductivity into the spatio-temporal average of local, instantaneous ionic contributions, and relate this ``local molar conductivity" field to the local ionic environment. Our machine learning model accurately predicts the molar conductivity of electrolyte systems that were not part of the training set, suggesting that the dynamics of ion transport is predictable from local static structure. Further, through analysing this machine-learned local conductivity field, we observe that fluctuations in local conductivity at high concentration are negatively correlated with total molar conductivity. Surprisingly, these fluctuations arise due to a long tail distribution of low conductivity ions, rather than distinct ion pairs, and are spatially correlated through both like- and unlike-charge interactions. More broadly, our approach shows how machine learning can aid the understanding of complex soft matter systems, by learning a function that attributes global collective properties to local, atomistic contributions.

cond-mat.soft↗

arXiv2020

Transport phenomena in electrolyte solutions: Non-equilibrium thermodynamics and statistical mechanics

The theory of transport phenomena in multicomponent electrolyte solutions is presented here through the integration of continuum mechanics, electromagnetism, and non-equilibrium thermodynamics. The governing equations of irreversible thermodynamics, including balance laws, Maxwell's equations, internal entropy production, and linear laws relating the thermodynamic forces and fluxes, are derived. Green-Kubo relations for the transport coefficients connecting electrochemical potential gradients and diffusive fluxes are obtained in terms of the flux-flux time correlations. The relationship between the derived transport coefficients and those of the Stefan-Maxwell and infinitely dilute frameworks are presented, and the connection between the transport matrix and experimentally measurable quantities is described. To exemplify application of the derived Green-Kubo relations in molecular simulations, the matrix of transport coefficients for lithium and chloride ions in dimethyl sulfoxide is computed using classical molecular dynamics and compared with experimental measurements.

cond-mat.stat-mech↗