SEARCH · Engineering Papers
Results for “Solvation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ion-Exchange and Cation Solvation Reactions in Ti 3 C 2 MXene
Ti 3 C 2 and other two-dimensional transition metal carbides known as MXenes are currently being explored for many applications involving intercalated ions, from electrochemical energy storage, to contaminant sorption from water, to selected ion sieving. We report here a systematic investigation of ion exchange in Ti 3 C 2 MXene and its hydration/dehydration behavior. We have investigated the effects of the presence of LiCl during the chemical etching of the MAX phase Ti 3 AlC 2 into MXene Ti 3 C 2 T x (T stands for surface termination) and found that the resulting MXene has Li + cations in the interlayer space. We successfully exchanged the Li + cations with K + , Na + , Rb + , Mg 2+ , and Ca 2+ (supported by X-ray photoelectron and energy-dispersive spectroscopy) and found that the exchanged material expands on the unit-cell level in response to changes in humidity, with the nature of expansion dependent on the intercalated cation, similar to behavior of clay minerals; stepwise expansions of the basal spacing were observed, with changes consistent with the size of the H 2 O molecule. Thermogravimetric analysis of the dehydration behavior of these materials shows that the amounts of H 2 O contained at ambient humidity correlates simply with the hydration enthalpy of the intercalated cation, and that the diffusion of the exiting H 2 O proceeds with kinetics similar to clays. Furthermore, these results have implications for understanding, controlling, and exploiting structural changes and H 2 O sorption in MXene films and powders utilized in applications involving ions, such as electrochemical capacitors, sensors, reverse osmosis membranes, or contaminant sorbents.
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.
Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models
Explore the source record for details and available documents.
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.
Spectroscopy of Reactive Complexes and Solvated Clusters: A Bottom-Up Approach Using Cryogenic Ion Traps
Not Available
Infrared Spectroscopy of Li + Solvation in Diglyme: Ab Initio Molecular Dynamics and Experiment
Not Available
Observing Solvation Dynamics with Simultaneous Femtosecond X-ray Emission Spectroscopy and X-ray Scattering
Explore the source record for details and available documents.
Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network
Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.
Decoding Oxyanion Aqueous Solvation Structure: A Potassium Nitrate Example at Saturation
We present that the ability to probe the structure of a salt solution at the atomic scale is fundamentally important for our understanding of many chemical reactions and their mechanisms. The capability of neutron diffraction to “see” hydrogen (or deuterium) and other light isotopes is exceptional for resolving the structural complexity around the dissolved solutes in aqueous electrolytes. We have made measurements using oxygen isotopes on aqueous nitrate to reveal a small hydrogen-bonded water coordination number (3.9 ± 1.2) around a nitrate oxyanion. This is compared to estimates made using the existing method of nitrogen isotope substitution and those of computational simulations (>5–6 water molecules). The low water coordination number, combined with a comparison to classical molecular dynamics simulations, suggests that ion-pair formation is significant. Finally, this insight demonstrates the utility of experimental diffraction data for benchmarking atomistic computer simulations, enabling the development of more accurate intermolecular potentials.