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Register a new userImproving Accuracy of Electrochemical Capacitance and Solvation Energetics in First-Principles Calculations
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Added by
Ravishankar Sundararaman
Publication date
2018
fields
Physics
and research's language is
English
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Reliable first-principles calculations of electrochemical processes require accurate prediction of the interfacial capacitance, a challenge for current computationally-efficient continuum solvation methodologies. We develop a model for the double layer of a metallic electrode that reproduces the features of the experimental capacitance of Ag(100) in a non-adsorbing, aqueous electrolyte, including a broad hump in the capacitance near the Potential of Zero Charge (PZC), and a dip in the capacitance under conditions of low ionic strength. Using this model, we identify the necessary characteristics of a solvation model suitable for first-principles electrochemistry of metal surfaces in non-adsorbing, aqueous electrolytes: dielectric and ionic nonlinearity, and a dielectric-only region at the interface. The dielectric nonlinearity, caused by the saturation of dipole rotational response in water, creates the capacitance hump, while ionic nonlinearity, caused by the compactness of the diffuse layer, generates the capacitance dip seen at low ionic strength. We show that none of the previously developed solvation models simultaneously meet all these criteria. We design the Nonlinear Electrochemical Soft-Sphere solvation model (NESS) which both captures the capacitance features observed experimentally, and serves as a general-purpose continuum solvation model.
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Quantum-chemical processes in liquid environments impact broad areas of science, from molecular biology to geology to electrochemistry. While density-functional theory (DFT) has enabled efficient quantum-mechanical calculations which profoundly impact understanding of atomic-scale phenomena, realistic description of the liquid remains a challenge. Here, we present an approach based on joint density-functional theory (JDFT) which addresses this challenge by leveraging the DFT approach not only for the quantum mechanics of the electrons in a solute, but also simultaneously for the statistical mechanics of the molecules in a surrounding equilibrium liquid solvent. Specifically, we develop a new universal description for the interaction of electrons with an arbitrary liquid, providing the missing link to finally transform JDFT into a practical tool for the realistic description of chemical processes in solution. This approach predicts accurate solvation free energies and surrounding atomic-scale liquid structure for molecules and surfaces in multiple solvents without refitting, all at a fraction of the computational cost of methods of comparable detail and accuracy. To demonstrate the potential impact of this method, we determine the structure of the solid/liquid interface, offering compelling agreement with more accurate (but much more computationally intensive) theories and with X-ray reflectivity measurements.
The performance of gold nanoparticles (NPs) in applications depends critically on the structure of the NP-solvent interface, at which the electrostatic surface polarization is one of the key characteristics that affects hydration, ionic adsorption, and electrochemical reactions. Here, we demonstrate significant effects of explicit metal polarizability on the solvation and electrostatic properties of bare gold NPs in aqueous electrolyte solutions of sodium salts of various anions (Cl$^-$, BF$_4$$^-$, PF$_6$$^-$, Nip$^-$(nitrophenolate), and 3- and 4-valent hexacyanoferrate (HCF)), using classical molecular dynamics simulations with a polarizable core-shell model of the gold atoms. We find considerable spatial heterogeneity of the polarization and electrostatic potentials on the NP surface, mediated by a highly facet-dependent structuring of the interfacial water molecules. Moreover, ion-specific, facet-dependent ion adsorption leads to large alterations of the interfacial polarization. Compared to non-polarizable NPs, polarizability modifies water local dipole densities only slightly, but has substantial effects on the electrostatic surface potentials, and leads to significant lateral redistributions of ions on the NP surface. Besides, interfacial polarization effects on the individual monovalent ions cancel out in the far field, and effective Debye-Huckel surface potentials remain essentially unaffected, as anticipated from continuum `image-charge concepts. Hence, the explicit charge response of metal NPs is crucial for the accurate description and interpretation of interfacial electrostatics (as, e.g., for charge transfer and interface polarization in catalysis and electrochemistry).
Lithium niobate (LiNbO$_3$, LN) plays an important role in holographic storage, and molybdenum doped LiNbO$_3$ (LN:Mo) is an excellent candidate for holographic data storage. In this paper, the basic features of Mo doped LiNbO$_3$, such as the site preference, electronic structure, and the lattice distortions, have been explored from first-principles calculations. Mo substituting Nb with its highest charge state of +6 is found to be the most stable point defect form. The energy levels formed by Mo with different charge states are distributed in the band gap, which are responsible for the absorption in the visible region. The transition of Mo in different charge states implies molybdenum can serve as a photorefractive center in LN:Mo. In addition, the interactions between Mo and intrinsic or extrinsic point defects are also investigated in this work. Intrinsic defects $tt V_{Li}^-$ could cause the movement of the $tt Mo_{Nb}^+$ energy levels. The exploration of Mo, Mg co-doped LiNbO$_3$ reveals that although Mg ion could not shift the energy level of Mo, it can change the distribution of electrons in Mo and Mg co-doped LN (LN:Mo,Mg) which help with the photorefractive phenomenon.
Among the beyond Li-ion battery chemistries, nonaqueous Li-O$_2$ batteries have the highest theoretical specific energy and as a result have attracted significant research attention over the past decade. A critical scientific challenge facing nonaqueous Li-O$_2$ batteries is the electronically insulating nature of the primary discharge product, lithium peroxide, which passivates the battery cathode as it is formed, leading to low ultimate cell capacities. Recently, strategies to enhance solubility to circumvent this issue have been reported, but rely upon electrolyte formulations that further decrease the overall electrochemical stability of the system, thereby deleteriously affecting battery rechargeability. In this study, we report that a significant enhancement (greater than four-fold) in Li-O$_2$ cell capacity is possible by appropriately selecting the salt anion in the electrolyte solution. Using $^7$Li nuclear magnetic resonance and modeling, we confirm that this improvement is a result of enhanced Li$^+$ stability in solution, which in turn induces solubility of the intermediate to Li$_2$O$_2$ formation. Using this strategy, the challenging task of identifying an electrolyte solvent that possesses the anti-correlated properties of high intermediate solubility and solvent stability is alleviated, potentially providing a pathway to develop an electrolyte that affords both high capacity and rechargeability. We believe the model and strategy presented here will be generally useful to enhance Coulombic efficiency in many electrochemical systems (e.g. Li-S batteries) where improving intermediate stability in solution could induce desired mechanisms of product formation.
We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H$_2$O$_2$ and CH$_3$SO$_3$H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.
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