SEARCH · Search NASA
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.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
New electrolytes are critical for high‐energy lithium (Li)–sulfur (S) batteries (LSBs) to ensure their stability against Li metal anode and polysulfides (PSs) shuttling which hinder the large‐scale application of LSBs. In this study, the design principle of moderately solvating electrolytes (MSEs) for LSBs is demonstrated by using a multiple‐solvent system comprising of a highly solvating solvent, a weakly solvating solvent, and a non‐solvating solvent to create a well‐balanced electrolyte system. This resulting electrolyte significantly improves the cycle life of LSBs, achieving 300 cycles, which is twice as long as that of similar cells with the conventional electrolyte and it also ensures stable calendar life for at least seven months. The optimal MSE forms robust passivation layers enhancing the structural integrity of both S and Li metal electrodes after cycling. These virtues effectively hinder parasitic side reactions and self‐discharge behavior of LSBs. This electrolyte design principle is versatile and can be applied to other battery chemistries, providing a potential path toward the development of a more efficient and stable battery system. By addressing key challenges such as the instability of electrodes and shuttling of polysulfides, this electrolyte approach offers promising solutions for advancing LSB technology.
The development of laser-driven accelerators-on-chip has provided an opportunity to miniaturize devices for electron radiotherapy delivery. Laser-driven accelerators produce highly time-compressed electron pulses, on the 100 fs to 1 ps scale. This delivers electrons at high peak power yet low average beam current compared with conventional delivery devices, which generate pulses of approximately 3 µs. The biophysical effects of this time structure, however, are unclear. Here, we use a Monte Carlo simulation approach to explore the effects of the electron beam time structure on the production of reactive oxygen species (ROS) in water. Our results show a power law increase in the generation of hydroxyl ions per deposited electron with decreasing pulse length over the pulse length range of 10 µs to 100 fs. Similar trends were observed for hydrogen peroxide, superoxide, hydroperoxyl, hydronium and solvated electrons. In practical terms, this indicates a fourfold increase in the efficiency of free radical production for sub-picosecond pulses, relative to that of conventional microsecond pulses, for the same number of deposited electrons.
The complex distribution of functional groups in carbohydrates, coupled with their strong solvation in water, makes them challenging targets for synthetic receptors. Despite extensive research into various molecular frameworks, most synthetic carbohydrate receptors have exhibited low affinities, and their interactions with sugars in aqueous environments remain poorly understood. In this work, we present a simple pyridinium-based hydrogen-bonding receptor derived from a subtle structural modification of a well-known tetralactam macrocycle. This small structural change resulted in a dramatic enhancement of glucose binding affinity, increasing from 56 M −1 to 3001 M −1 . Remarkably, the performance of our synthetic lectin surpasses that of the natural lectin, concanavalin A, by over fivefold. X-ray crystallography of the macrocycle–glucose complex reveals a distinctive hydrogen bonding pattern, which allows for a larger surface overlap between the receptor and glucose, contributing to the enhanced affinity. Furthermore, this receptor possesses allosteric binding sites, which involve chloride binding and trigger receptor aggregation. This unique allosteric process reveals the critical role of structural flexibility in this hydrogen-bonding receptor for the effective recognition of sugars. We also demonstrate the potential of this synthetic lectin as a highly sensitive glucose sensor in aqueous solutions.
Fourier transform infrared (FTIR) absorption spectroscopy, ultrafast two-dimensional infrared (2D-IR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and density functional theory (DFT) calculations show that, in iongels composed of 1-ethyl-3-methylimidazolium bis(trifluoromethyl\-sulfonyl)imide ([C 2 C 1 Im][Tf 2 N]}) and cross-linked poly(ethylene glycol)diacrylate (cl-PEGDA), imidazolium cations solvate PEGDA chains in tight and loose complexes. H/D isotope substitution at the 2-position of the imidazolium ring simplifies the CH-stretching vibrational bands. Tight complexes are characterized by stronger, directional hydrogen bonding interactions between the 2-position of the imidazolium ring and the ethereal oxygens. The loose complexes resemble bulk ionic liquid (IL) non-specifically perturbed by the PEGDA chains.
In the context of energy applications for salts and, more specifically, in the case of molten salt reactors, the solvation of corrosion species, the nature and behavior of radiation-produced excess electrons, transient radicals, and molecular gas species all depend on the Lewis acid–base behavior of the constituent salt melt. Speciation of dissolved species and their transport properties are also influenced by the ability of the melt to form networks. This article focuses on the structural properties of melts composed of alkaline earth metal ions coupled with the Cl – anion, and the quantum mechanical behavior of Cl 2 , a typical product of the reaction of radiation-produced chlorine radicals in Cl – -based molten salts. We explore the effect of M 2+ Lewis acidity on chlorobasicity, seen in this work as the availability of Cl – ions to chemically react with Cl 2 to produce Cl 3 – .
Direct air capture (DAC) under humid ambient conditions typically requires the use of organic components, with sorbents that are purely inorganic in composition for the most part operating hundreds of degrees above room temperature. In this work, we report porous metal hydroxides as a novel class of water-tolerant, oxidatively and hydrothermally stable low-temperature sorbents that exhibit competitive DAC working capacities of 1.25 mmol/g over 5 consecutive temperature swing adsorption–desorption cycles in the presence of steam and oxygen. Aqueous miscible organic solvent treatments are used to create highly porous structures with surface areas exceeding 700 m 2 /g that capture CO 2 in the form of bicarbonates under dry conditions, and carbonates under wet conditions. Water exerts a facilitative rather than an inhibiting effect on CO 2 binding, and the presence of hydrating multilayers serves to stabilize carbonate species akin to moisture swing adsorbents except for the fact that solvation results in a remarkable (upto 10-fold) increase, not decrease, in DAC capacity. High-valent doping with cerium is used to improve DAC capacities by amplifying surface basicity, evidencing porous nickel hydroxides specifically (and porous metal hydroxides more generally) as a novel class of robust, earth-abundant DAC sorbents.
Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.
Path Integral Monte Carlo was used to calculate the Bose-Einstein condensate fraction at the surface of a helium film at T = 0:77 K, as a function of density. Moving from the center of the slab to the surface, the condensate fraction was found to initially increase with decreasing density to a maximum value of 0.9, before decreasing. Long wavelength density correlations were observed in the static structure factor at the surface of the slab. A surface dispersion relation was calculated from imaginary-time density-density correlations. Similar calculations of the superfluid density throughout He-4 droplets doped with linear impurities (HCN)(sub n) are presented. After deriving a local estimator for the superfluid density distribution, we find a decreased superfluid response in the first solvation layer. This effective normal fluid exhibits temperature dependence similar to that of a two-dimensional helium system.
Alkali metal cations are known to influence the kinetics of the hydrogen evolution reaction (HER), acting as either promoters or inhibitors to the rate-determining step depending on the metal surface, local pH, and cation concentration. Despite the importance of concentrated electrolytes for commercial electrochemical cells, the impact of cations on the HER in concentrated alkaline environments (>1 M) and in mixed cation systems remains poorly understood. Here, this study quantifies the HER kinetics at polycrystalline metal surfaces (Pt, Au, Cu, and Fe) and the equilibrium solvation environment in pure and mixed alkali metal hydroxide electrolytes at concentrations up to 3.0 M. Kinetic analyses of Au, Cu, and Fe revealed a positive cation-concentration-effect that was primarily driven by changes to the charge transfer coefficient. Multinuclear NMR spectroscopy examined the solvation of H 2 O/OH – species and the alkali cations as a function of (mixed) alkali cation concentration(s), and demonstrated rapid exchange between solvent, hydroxide, and solvated cations. Together, these findings support models where HER kinetics on noncatalytic metal surfaces in strongly alkaline conditions are primarily governed by the average polarization and polarizability of the metal/solution interface and that increasing cation activity continues to increase the transfer coefficient at metal-hydroxide concentrations up to 3.0 M. Electrolytes and additives which can outcompete weakly hydrated cations and disrupt the interfacial water structure are expected to suppress parasitic HER at electrodes for energy storage and electroplating.
Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.
Calculating stability constants for lanthanide--nitrate complexes in aqueous solution is challenging due to the complex free-energy landscapes of the participating species. In this work, we compare cluster-continuum solvation and condensed-phase approaches using universal machine learning interatomic potentials (MLIPs) for determining the stability constants of lanthanide--nitrate complexes in aqueous solutions. Within the cluster--continuum solvation framework at the B3LYP level of theory, reactions involving lanthanide coordination numbers of both 8 and 9 are found to be relevant. After an empirical linear free-energy correction, the cluster--continuum results fall on the same order-of-magnitude scale as the experimental stability constants. By contrast, MACE-MP0 MLIP underestimates lanthanide hydration numbers and gives PMF-derived stability constants with large deviations from experiment, whereas MACE-MATPES-R2SCAN improves both hydration structure and the stability-constant scale but still does not quantitatively reproduce the detailed lanthanide trend. Across both approaches, nitrate binding is best viewed as a labile coordination motif rather than a fixed mono- or bidentate structure, with hydration-shell structure influencing which configurations are favored. Overall, the cluster--continuum calculations provide a practical semi-quantitative baseline for the experimental stability-constant scale, while the explicit-solvent MLIP benchmarks show clear progress from MACE-MP0 to MACE-MATPES-r2SCAN but also highlight the need for lanthanide-targeted training, fine-tuning, or improved long-range and polarization treatments to obtain predictive thermodynamics for complex aqueous lanthanide chemistry.
We report a rare family of pyridine-coordinated iodobismuthate(III) salts supported by alkyltriphenylphosphonium and tetraphenylphosphonium cations. Reactions of BiI 3 with Ph 3 PR + I − (R = Me, Et, n Pr, n Bu, Ph) in neat pyridine, followed by crystallization, yield structurally tunable bismuth-halide-pyridine anions dictated by reagent stoichiometry. Combination of BiI 3 and Ph 3 PR + I − in 2:1 ratio produced [Ph 3 PR] 2 [BiI 5 Py], 1 (R = Me, Et, n Pr, Ph), while combination in 1:1 ratio resulted in three compounds: [Ph 3 PR][cis-BiI 4 Py 2 ], 2 (R = n Pr, Ph), [Ph 3 PR][trans-BiI 4 Py 2 ], 3 (R = Me, Et, Ph), and [Ph 3 PR] 2 [transoid-Bi 2 I 8 Py 2 ], 4 (R = Me, Et, n Pr, n Bu, Ph). In many cases, the compounds were isolated as Py or Et 2 O solvates, and in some cases, multiple degrees of solvation or polymorphism were encountered. Hirshfeld analysis of 1–4 showed the major anion–cation/anion/solvent interactions to be H⋯I, H⋯H, and C⋯H. Diffuse reflectance measurements of representative compounds, all of which were yellow-orange to red-orange, revealed bandgaps in the range of 1.9–2.2 eV, where density-of-states KS-DFT calculations attribute the absorption to metal-centered charge transfer within the anionic unit. NLMO and QTAIM analyses further indicate predominantly ionic Bi(III)–I/pyridine bonding with robust inner-sphere coordination that is insensitive to anion speciation.
Colicins are antimicrobial proteins produced by bacteria for the purpose of destroying neighboring bacteria. Colicin activity is neutralized by a specific cognate immunity protein in order to protect the host. This study investigates the structural and binding mechanisms underlying the interaction of colicin-D, -E3 and -E8 to their respective immunity proteins (ImD, Im3 and Im8) using structure prediction, molecular dynamics (MD) simulations and MM-PBSA approach of free energy calculations. High-confidence colicin-immunity (Col-Im) complex structures predicted using AlphaFold2 were subjected to MD simulations of 150 ns with GROMACS and were analyzed for the binding free energy calculation using gmx_MMPBSA. Results showed that the complex of Col_E3-Im3 exhibited the most favorable binding free energy, driven by strong van der Waals and electrostatic interactions. Col_D-ImD and Col_E8-Im8 also showed the favorable binding. Electrostatics and hydrogen bonding emerged as a key factor driving binding and stability, while polar solvation acted as a destabilizing factor across all systems. These outcomes provide an understanding of the molecular mechanisms of Col-Im systems, with potential applications for developing natural antimicrobials for food safety.
Interactions of low-energy electrons with the DNA and RNA nucleobases are known to form metastable states, known as electronic resonances. In this work, we study electron attachment to solvated uracil, an RNA nucleobase, using the orbital stabilization method at the Equation of Motion-Coupled Cluster for Electron Affinities with Singles and Doubles (EOM-EA-CCSD) level of theory with the Effective Fragment Potential (EFP) solvation method. We benchmarked the approach using multireference methods, as well as by comparing EFP and full quantum calculations. The impact of solvation on the first one particle (1p) shape resonance, formed by electron attachment to the π* LUMO orbital, as well as the first two particle one hole (2p1h) resonance, formed by electron attachment to neutral uracil's π–π* excited state, was investigated. We used molecular dynamics simulations for solvent configurations and applied charge stabilization technique-based biased sampling to procure configurations adequate to cover the entire range of the electron attachment energy distribution. The electron attachment energy in solution is found to be distributed over a wide range of energies, between 4.6 eV to 6.8 eV for the 2p1h resonance, and between −0.1 eV to 2 eV for the 1p resonance. The solvent effects were similar for the two resonances, indicating that the exact electron density of the state is not as important as the solvent configurations. Multireference calculations extended the findings showing that solvation effects are similar for the lowest four resonances, further indicating that the specific solute electron density is not as important, but rather the water configurations play the most important role in solvation effects. Lastly, by comparing bulk solvation to clusters of uracil with a few water molecules around it, we find that the impact of microsolvation is very different from that of bulk solvation.
Benzylamines are key intermediates in pharmaceuticals, agrochemicals, and polymers, but their conventional production relies on benzyl chloride - a petroleum-derived compound with high toxicity and energy demands. Lignin, accounting for up to 30% of plant biomass, is the largest renewable source of aromatic carbon on Earth. However, its highly complex and recalcitrant structure poses a major barrier to efficient conversion into high-value chemicals. Here, in this study, we developed a catalytic approach to convert commercial kraft lignin into phenolic benzylamines through selective depolymerization and subsequent functionalization. We systematically evaluated the effects of three alcohol solvents, formic acid (FA), and a ruthenium-on‑carbon (Ru/C) catalyst on monophenol yield and selectivity. Up to 6.5 wt% monophenol yield was achieved using methanol (MeOH), FA, and Ru/C at 300 °C for 2 h. Quantum thermodynamic simulations based on the COSMO-RS model confirmed the superior solvation and reactivity of the MeOH + FA system, rationalizing observed product yield. The purified monophenolic products, primarily guaiacol and alkyl guaiacols, were then converted into functionalized benzylamines with >90% yield via a multicomponent Mannich reaction under mild conditions. Techno economic analysis (TEA) and life cycle assessment (LCA) underscore the importance of improving lignin depolymerization yields and expanding biorefinery scale. Solvent-only configurations outperform other options in both cost and emissions, with the methanol-only case performing the best ($\$$105 /kg and 26 kg CO 2e /kg) at a large-scale facility. This study establishes a scalable, bio-based pathway for producing benzylamines from commercial kraft lignin, advancing lignin valorization and offering a sustainable alternative to produce petrochemical-based benzylamines.
Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.
Lewis acid zeolites are attractive catalysts for epoxidation and biomass valorization, as they are highly active and selective in the liquid phase and can operate at or near ambient conditions. While a rich experimental literature exists on liquid-phase Lewis acid zeolite catalysis, our understanding of the molecular organization and solvent dynamics in the vicinity of Lewis acid sites with differing metal site speciation remains limited. In this work, we investigate the molecular coordination and diffusion of two common solvents (methanol and water) around the closed and open Sn-BEA zeolite active sites using molecular dynamics simulations with a machine-learned interatomic potential trained on ab initio molecular dynamics trajectories. Molecular dynamics simulations reveal that introducing active sites significantly enhances local order in the first and second solvation shells compared to the pure silica case. For methanol, both closed and open active sites are singly coordinated, while more than two water molecules coordinate the open site. In contrast to methanol, we observed that water molecules dissociate, leading to the formation of additional Sn-OH and silanol groups away from the active site. The diffusion coefficients of water and methanol are functions of the solvent population in the pore. Here, our work provides insights into how active site speciation in Lewis acid zeolites affects solvent coordination, diffusion, and vibrational signature. This information is foundational for catalyst design and optimization of liquid-phase catalytic processes in zeolites. It also demonstrates the suitability of machine-learned interatomic potentials for modeling reactive systems, enabling sufficiently long trajectories for appropriate statistical averaging.