news in SolUtion Chemistry Community

  The distribution of ions at the air/water interface plays a decisive role in many natural processes. Several studies have reported that larger ions tend to be surface-active, implying ions are located on top of the water surface, thereby inducing electric fields that determine the interfacial water structure. Here we challenge this view by combining surface-specific heterodyne-detected vibrational sum-frequency generation with neural network-assisted ab initio molecular dynamics simulations. Our results show that ions in typical electrolyte solutions are, in fact, located in a subsurface region, leading to a stratification of such interfaces into two distinctive water layers. The outermost surface is ion-depleted, and the subsurface layer is ion-enriched. This surface stratification is a key element in explaining the ion-induced water reorganization at the outermost air/water interface. 

   his work reports the computation and modeling of the self-diffusivity (D*), shear viscosity (η*), and thermal conductivity (κ*) of the Mie fluid. The transport properties were computed using equilibrium molecular dynamics simulations for the Mie fluid with repulsive exponents (λr) ranging from 7 to 34 and at a fixed attractive exponent (λa) of 6 over the whole fluid density (ρ*) range and over a wide temperature (T*) range. The computed database consists of 17,212, 14,288, and 13,099 data points for self-diffusivity, shear viscosity, and thermal conductivity, respectively. The database is successfully validated against published simulation data. The above-mentioned transport properties are correlated using artificial neural networks (ANNs). Two modeling approaches were tested: a semiempirical formulation based on entropy scaling and an empirical formulation based on density and temperature as input variables. For the former, it was found that a unique formulation based on entropy scaling does not yield satisfactory results over the entire density range due to a divergent and incorrect scaling of the transport properties at low densities. For the latter empirical modeling approach, it was found that regularizing the data, e.g., modeling ρ*D* instead of D*, ln η* instead of η*, and ln κ* instead of κ*, as well as using the inverse of the temperature as an input feature, helps to ease the interpolation efforts of the artificial neural networks. The trained ANNs can model seen and unseen data over a wide range of density and temperature. Ultimately, the ANNs can be used alongside equations of state to regress effective force field parameters from volumetric and transport data. 

  The molecular details of an electrocatalytic interface play an essential role in the production of sustainable fuels and value-added chemicals. Many electrochemical reactions exhibit strong cation-dependent activities, but how cations affect reaction kinetics is still elusive. We report the effect of cations (K+, Li+, and Ba2+) on the interfacial water structure using second-harmonic generation (SHG) and classical molecular dynamics (MD) simulation. The second- (χH2O(2)) and third-order (χH2O(3)) optical susceptibilities of water on Pt are smaller in the presence of Ba2+ compared to those of K+, suggesting that cations can affect the interfacial water orientation. MD simulation reproduces experimental SHG observations and further shows that the competition between cation hydration and interfacial water alignment governs the net water orientation. The impact of cations on interfacial water supports a cation hydration-mediated mechanism for hydrogen electrocatalysis; i.e., the reaction occurs via water dissociation followed by cation-assisted hydroxide/water exchange on Pt. Our study highlights the role of interfacial water in electrocatalysis and how innocent additives (such as cations) can affect the local electrochemical environment.