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Electrodeposition of Dysprosium in pyrrolidinium triflate ionic liquid at ambient temperature: Unraveling system efficiency and impact of solvation interplays on the reduction process
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Goldilocks and the three glymes: How Na+ solvation controls Na–O 2 battery cycling
In this work we report a framework to understand the role of solvent-salt interactions and how they mediate the performance of sodium-air/O 2 batteries. The utilization of suitable electrolyte materials remains a point of major concern within the research community, as their stability and decomposition pathways during cycling are intimately connected with capacity and cycle life. Glyme based solvents have been widely utilized in Na–O 2 batteries, however, to date no clear correlation between solvent/electrolyte properties and battery performance has been given. Herein, we have examined the effect of glyme chain length (ethylene glycol dimethyl ether DME; diethylene glycol dimethyl ether, DEGDME; and tetraethylene glycol dimethyl ether, TEGME) on the cycling behaviour of Na–O 2 batteries and conclude that overall cell performance is highly dependent on solvent selection, salt concentration and rate of discharge/charge. We demonstrate how solvent selection helps define cell chemistry and performance by linking salt-solvent interactions to enthalpy of dissolution - and subsequently to sodium battery electrolyte properties - through the combination of both experimental and theoretical methodologies. The approaches detailed in this study could be used to predictively prepare electrolytes for Li-air batteries, other glyme-based electrochemical systems and low temperature applications.
Intercalation mechanism of solvated magnesium electrolytes on spinel cathode surfaces
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Solvation coordination compounds of scandium chloride from the dehydration of scandium chloride hexahydrate
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The effect of lanthanoid defects on anionic solvation of Li in Li6.5La2+xZr1.5Ta0.5O12 from x = 0 to x = 1.2 garnet
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Controlling the Interface between Salts, Solvates, Co-crystals, and Ionic Liquids with Non-stoichiometric Protic Azolium Azolates [Plus Supporting Information]
In this work, a non-stoichiometric approach to control the solid-state behavior of protic ionic liquids (PILs) was demonstrated by direct mixing of 4,5-dicyanoimidazole (HDCNim) with either 1-ethylimidazole (C 2 im) or 1-butylimidazole (C 4 im) in different mole fractions. Isolation and characterization of three crystalline materials (all having melting points < 100 °C, thus fitting the PIL definition) revealed three different but closely related systems. The structure of [HC 2 im][DCNim] consists of a confused proton system, which is so named for the fast proton exchange between basic and acidic fragments and the formation of hydrogen-bonded anionic oligomers. The compound [HC 2 im][DCNim]·HDCNim consists of an oligomeric delocalized anion (a confused proton located between two azole fragments, in which one acts as a neutral moiety while the other as an ionic fragment, DCNim-H-DCNim) and [HC 4 im][DCNim]·HDCNim can be classified as a salt co-crystal. We believe that these observations will allow a deeper understanding of the behavior of “confused protons” and provide additional strategies for controlling the properties of important classes of materials such as active pharmaceutical ingredients.
Uncovering the 3d and 4d Electronic Interactions in Solvated Ru Complexes with 2p3d Resonant Inelastic X-ray Scattering
The electronic structure and dynamics of ruthenium complexes are widely studied given their use in catalytic and light-harvesting materials. Here we investigate three model Ru complexes, [Ru III (NH 3 ) 6 ] 3+ , [Ru II (bpy) 3 ] 2+ , and [Ru II (CN) 6 ] 4– , with L 3 -edge 2p3d resonant inelastic X-ray scattering (RIXS) to probe unoccupied 4d valence orbitals and occupied 3d orbitals and to gain insight into the interactions between these levels. In this study, the 2p3d RIXS maps contain a higher level of spectral information than the L 3 X-ray absorption near edge structure (XANES). This study provides a direct measure of the 3d spin–orbit splittings of 4.3, 4.0, and 4.1 eV between the 3d 5/2 and 3d 3/2 orbitals of the [Ru III (NH 3 ) 6 ] 3+ , [Ru II (bpy) 3 ] 2+ , and [Ru II (CN) 6 ] 4– complexes, respectively.
Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics
Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the lambda-exchange of free energy perturbation (FEP). Further, two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/lambda-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.
Charge Transport in Solvated Donor–Acceptor Functionalized Peptoids: Molecular Dynamics and Rate Theory
Scalable solar-energy conversion requires photoactive materials that combine the efficiency of natural photosynthetic systems with the stability and processability needed for practical applications. Achieving reliable charge transport in soft, self-assembled organic materials remains challenging, as structural fluctuations and environmental effects strongly influence charge-transfer (CT) rates. Here, we present a broadly applicable computational framework for evaluating CT rates in the condensed phase, combining Fermi’s golden rule rate theory with inputs from all-atom molecular dynamics (MD) simulations and first-principles electronic-structure calculations. The approach does not rely on system-specific parametrization and is applicable to a wide range of soft and disordered materials. We demonstrate the applicability and usefulness of the framework on redox-active peptoids functionalized with iron–porphyrin (Fe–P) complexes, a bioinspired platform with programmable donor–acceptor units and tunable three-dimensional organization. The calculated CT rates exhibit strong sensitivity to molecular conformation, with variations spanning several orders of magnitude. This dependence is shown to arise from the pronounced variation in diabatic electronic coupling with the relative orientations and separations of the Fe–P complexes across the conformational ensemble. The framework provides a consistent route for connecting atomistic structure to CT kinetics in the condensed phase and enables analysis of structure–rate relations in organic semiconducting systems.
Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential
Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive ab-initio molecular dynamics simulations. Here, we present a deep neural network potential trained using accurate ab-initio data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the pK a , as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of ab-initio methods is required.