Changes in electrodic reaction rates due to elastic stress and stress-induced surface patterns

Enhancement of the Rate of Surface Reactions by the Elastocapillary Effect

We examined the effect of deformability of a solid substrate on the kinetics of a surface reaction that occurs between chemical species present in it and a liquid dispensed on it. In particular, we have dispensed aqueous solutions of gold and silver salt as sessile drops or as a liquid pool on a cross-linked film of poly(dimethylsiloxane) (PDMS). The PDMS surface contains organosilane (SiH), which reduces the salt, producing metallic nanoparticles at the solid-liquid interface. These experiments reveal that, for a sufficiently soft solid, the reaction proceeds about three times faster in the drop mode than in the pool mode. The reaction conditions in both cases remain exactly identical except that, for the drop, the vertical component of the liquid surface tension deforms the solid substrate at the three-phase contact line. We have estimated the solid-liquid and solid-air interfacial energy, which along with the surface energy of the liquid gives an estimate of excess free energy. This energy is found to be different for the drop and pool modes. By considering that this excess free energy decreases the activation energy barrier for the reaction, we have shown that the reaction rate constant in the drop mode should indeed exceed that in the pool mode by about three times.

A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures

Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.

Eigenstress model for electrochemistry of solid surfaces

Thermodynamic analysis and molecular dynamics simulations were conducted to systematically study the size-dependent electrochemical response of solids. By combining the generalized Young-Laplace equation with the popular Butler-Volmer formulation, the direct influence of surface stress on solid film electrochemical reactions was isolated. A series of thermodynamic formulas were developed to describe the size-dependent electrochemical properties of the solid surface. These formulas include intrinsic surface elastic parameters, such as surface eigenstress and surface elastic modulus. Metallic films of Au, Pt, Ni, Cu and Fe were studied as examples. The anodic current density of the metal film increased, while the equilibrium potential decreased with increasing solid film thickness.