Electrochemical carbon dioxide (CO 2 ) reduction is an emerging technology for efficiently recycling CO 2 into fuel, and many studies of this reaction are focused on developing advanced catalysts with high activity, selectivity, and durability. Of these catalysts, oxide-derived metal nanoparticles, which are prepared by reducing a metal oxide, have received considerable attention due to their catalytic properties. However, the mechanism of the nanoparticles' activity enhancement is not well-understood. Recently, it was discovered that the catalytic activity is quantitatively correlated to the surface density of grain boundaries (GBs), implying that GBs are mechanistically important in electrochemical CO 2 reduction. Here, using extensive density functional theory (DFT) calculations modelling the atomistic structure of GBs on the Au (111) surface, we suggest a mechanism of electrochemical CO 2 reduction to CO mediated by GBs; the broken local spatial symmetry near a GB tunes the Au metal-to-adsorbate π-backbonding ability, thereby stabilizing the key COOH intermediate. This stabilization leads to a decrease of ~200 mV in the overpotential and a change in the rate-determining step to the second reduction step, of which are consistent with previous experimental observations. The atomistic and electronic details of the mechanistic role of GBs during electrochemical CO 2 reduction presented in this work demonstrate the structure-activity relationship of atomically disordered metastable structures in catalytic applications.

We study two types of intrinsic uncertainties, statistical errors and system size effects, in estimating shear viscosity via equilibrium molecular dynamics simulations, and compare them with the corresponding uncertainties in evaluating the self-diffusion coefficient. Uncertainty quantification formulas for the statistical errors in the shear-stress autocorrelation function and shear viscosity are obtained under the assumption that shear stress follows a Gaussian process. Analyses of simulation results for simple and complex fluids reveal that the Gaussianity is more pronounced in the shear-stress process (related to shear viscosity estimation) compared with the velocity process of an individual molecule (related to self-diffusion coefficient). At relatively high densities corresponding to a liquid state, we observe that the shear viscosity exhibits complex size-dependent behavior unless the system is larger than a certain length scale, and beyond which, reliable shear viscosity values are obtained without any noticeable scaling behavior with respect to the system size. We verify that this size-dependent behavior is configurational and relate the characteristic length scale to the shear-stress correlation length.

We study the intrinsic nature of the finite system-size effect in estimating shear viscosity of dilute and dense fluids within the framework of the Green-Kubo approach. From extensive molecular dynamics simulations, we observe that the size effect on shear viscosity is characterized by an oscillatory behavior with respect to system size L at high density and by a scaling behavior with an L −1 correction term at low density. Analysis of the potential contribution in the shear-stress autocorrelation function reveals that the former is configurational and is attributed to the inaccurate description of the long-range spatial correlations in finite systems. Observation of the long-time inverse-power decay in the kinetic contribution confirms its hydrodynamic nature. The L −1 correction term of shear viscosity is explained by the sensitive change in the long-time tail obtained from a finite system. * ckim103@ucmerced.edu arXiv:1907.08773v1 [physics.chem-ph]