Double-Layer Capacitances Caused by Ion–Solvent Interaction in the Form of Langmuir-Typed Concentration Dependence

Variations of the double layer capacitances (DLCs) at a platinum electrode with concentrations and kinds of salts in aqueous solutions were examined in the context of facilitating orientation of solvent dipoles. With an increase in ionic concentrations, the DLCs increased by ca. a half and then kept constant at concentrations over 1 mol dm−3. This increase was classically explained in terms of the Gouy–Chapman (GC) equation combined with the Stern model. Unfortunately, measured DLCs were neither satisfied with the Stern model nor the GC theory. Our model suggests that salts destroy hydrogen bonds at the electrode–solution interface to orient water dipoles toward the external electric field. A degree of the orientation depends on the interaction energy between the salt ion and a water dipole. The statistical mechanic calculation allowed us to derive an equation for the DLC as a function of salt concentration and the interaction energy. The equation took the Langmuir-type in the relation with the concentration. The interaction energy was obtained for eight kinds of salts. The energy showed a linear relation with the interaction energy of ion–solvent for viscosity, called the B-coefficient.

Electronic structure of aqueous two-dimensional photocatalyst

The electronic structure, in particular the band edge position, of photocatalyst in presence of water is critical for photocatalytic water splitting. We propose a direct and systematic density functional theory (DFT) scheme to quantitatively predict band edge shifts and their microscopic origins for aqueous 2D photocatalyst, where thousands of atoms or more are able to be involved. This scheme is indispensable to correctly calculate the electronic structure of 2D photocatalyst in the presence of water, which is demonstrated in aqueous MoS2, GaS, InSe, GaSe and InS. It is found that the band edge of 2D photocatalysts are not rigidly shifted due to water as reported in previous studies of aqueous systems. Specifically, the CBM shift is quantitatively explained by geometric deformation, water dipole and charge redistribution effect while the fourth effect, i.e., interfacial chemical contact, is revealed in the VBM shift. Moreover, the revealed upshift of CBM in aqueous MoS2 should thermodynamically help carriers to participate in hydrogen evolution reaction (HER), which underpin the reported experimental findings that MoS2 is an efficient HER photocatalyst. Our work paves the way to design 2D materials in general as low-cost and high-efficiency photocatalysts.

Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data

<div>We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.</div><div>To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. </div>

Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data

<div>We report a calculation scheme on water molecular dipole and quadrupole moments in the liquid phase through a Deep Neural Network (DNN) model. Employing the the Maximally Localized Wannier Functions (MLWF) for the valence electrons, we obtain the water moments through a post-process on trajectories from \textit{ab-initio} molecular dynamics (AIMD) simulations at the density functional theory (DFT) level. In the framework of the deep potential molecular dynamics (DPMD), we develop a scheme to train a DNN with the AIMD moments data. Applying the model, we calculate the contributions from water dipole and quadrupole moments to the electrostatic potential at the center of a cavity of radius 4.1 \AA\ as -3.87 V, referenced to the average potential in the bulk-like liquid region.</div><div>To unravel the ion-independent water effective local potential contribution to the ion hydration free energy, we estimate the 3rd cumulant term as -0.22 V from simulations totally over 6 ns, a time-scale inaccessible for AIMD calculations. </div>

The Application of Non-Esterified Biodiesel in a Diesel Engine

Nowadays, various environmental regulations are being strengthened because of air pollution caused by exhaust gas emission of the automobiles. Biodiesel has been recognized as an alternative energy resource since it can be used without the modification of existing diesel engines and contains oxygen in itself, so the engine performance didn’t have large differences in comparison with the diesel fuel but remarkably reduces smoke emissions. The main objective of this study is to investigate usability of non-esterified biodiesel as an alternative fuel in a common rail direct injection diesel engine. The non-esterified biodiesel has not generated glycerin in esterificaion process and reduced the 20 percent of cost because it has not used methanol in the production process. Experiments were conducted by using the 5 percentage of biodiesel and 4 percentage of biodiesel with 1 percentage of WDP (water dipole power) in diesel fuel. Based on the experimental analysis the smoke emission of biodiesel was reduced significantly, but power, torque, and brake specific energy consumption was similar in comparison with commercial diesel fuel.