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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.
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Flammability Limits: A Comprehensive Review of Theory, Experiments, and Estimation Methods
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Properties That Potentially Limit High-Level Blends of Biomass-Based Diesel Fuel
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Calcium-Uranyl-Carbonato Species Kinetically Limit U(VI) Reduction by Fe(II) and Lead to U(V)-Bearing Ferrihydrite
Reaction conditions and mechanisms promoting or inhibiting U reduction exert a central control on U solubility and, therefore, U transport and its associated risks. Here, we vary and track common aqueous uranium species to show that a kinetic restriction inhibits homogeneous reduction of the calcium-uranyl-carbonato species (CaUO$_2$(CO$_3$)$_3$$^{2–}$ and Ca$_2$UO$_2$(CO$_3$)$_3$) by Fe(II)$_{(aq)}$, while ferrihydrite surface-catalyzed reduction of all aqueous uranyl by Fe(II) proceeds. Using U L$_3$ high energy resolution fluorescence detection (HERFD) X-ray absorption near edge structure (XANES) spectroscopy, U L$_3$ extended X-ray absorption fine structure (EXAFS) spectroscopy, and transmission electron microscopy (TEM), we also show that U(V) is generated and incorporated into ferrihydrite formed from homogeneous U(VI) reduction by Fe(II)(aq). Through elucidation of the mechanisms that inhibit reduction of the calcium-uranyl-carbonato species and promote stabilization of U(V), we advance our understanding of the controls on U solubility and thus improve prediction of U transport in surface and subsurface systems.
Nanoscale Mg-Depleted Layers Slow Carbonation of Forsterite (Mg 2 SiO 4 ) When Water Is Limited
Passivation of silicate surfaces by accumulated reaction products is an obstacle to efficient CO 2 mineralization. In this study, we investigate a unique passivation effect during the carbonation of the basalt mineral forsterite (Mg 2 SiO 4 ) in humid supercritical CO 2 (50 °C, 90 bar). Using in situ high-pressure infrared spectroscopy, we demonstrate that dissolution of forsterite into a thin water film slows significantly after reaction for ˜24 h, even under far-from-equilibrium conditions. Further, 29 Si magic angle spinning nuclear magnetic resonance spectroscopy detects a highly polymerized amorphous silica at this stage. On the basis of transmission electron microscopy and energy dispersive X-ray spectroscopy, we show that the silica is present as a Mg-depleted layer that is just 2–3 nm thick on the reacted forsterite particles. The decrease in the level of forsterite dissolution in the presence of an extraordinarily thin Mg-depleted layer can be strongly linked to properties of the thin fluid film at the surface, highlighting the importance of water during mineral carbonation. This study furthers our understanding of silicate mineral carbonation under select low-water, humidified fluid conditions relevant to basaltic geologic reservoirs, recovery of critical elements by carbonation of mafic ores, and sequestration of atmospheric CO 2 by enhanced rock weathering.
Exploring the Limits of Dative Boratrane Bonding: Iron as a Strong Lewis Base in Low-Valent Non-Heme Iron-Nitrosyl Complexes
We previously reported the synthesis and preliminary characterization of a unique series of low-spin (ls) {FeNO} 8–10 complexes supported by an ambiphilic trisphosphineborane ligand, [Fe(TPB)(NO)] +/0/– . In this study, we use advanced spectroscopic techniques and density functional theory (DFT) calculations to extract detailed information as to how the bonding changes across the redox series. We find that, in spite of the highly reduced nature of these complexes, they feature an NO + ligand throughout with strong Fe–NO π-backbonding and essentially closed-shell electronic structures of their FeNO units. This is enabled by an Fe–B interaction that is present throughout the series. In particular, the most reduced [Fe(TPB)(NO)] – complex, an example of a ls-{FeNO} 10 species, features a true reverse dative Fe → B bond where the Fe center acts as a strong Lewis-base. Hence, this complex is in fact electronically similar to the ls-{FeNO} 8 system, with two additional electrons “stored” on site in an Fe–B single bond. The outlier in this series is the ls-{FeNO} 9 complex, due to spin polarization (quantified by pulse EPR spectroscopy), which weakens the Fe–NO bond. These data are further contextualized by comparison with a related N 2 complex, [Fe(TPB)(N 2 )] – , which is a key intermediate in Fe(TPB)-catalyzed N 2 fixation. Our present study finds that the Fe → B interaction is key for storing the electrons needed to achieve a highly reduced state in these systems, and highlights the pitfalls associated with using geometric parameters to try to evaluate reverse dative interactions, a finding with broader implications to the study of transition metal complexes with boratrane and related ligands.
A Computational Assessment of Actinide Dioxide Cations AnO 2 2+ for An = U to Lr: The Limited Stability Range of the Hexavalent Actinyl Moiety, [O=An=O] 2+
The isolated gas-phase actinide dioxide dications, AnO 2 2+ , were evaluated by DFT and coupled cluster CCSD(T) calculations for 12 actinides, An = U–Lr. CASSCF calculations were used to define the orbitals for the CCSD(T) calculations. The characteristic linear [O$=$An$=$O] 2+ hexavalent actinyl(VI) was found to be the lowest energy structure for An = U, Np, and Pu, which also form stable actinyl(VI) species in solution and possibly for Am when spin–orbit effects are included. For Am, there is a divalent [An II (O 2 )] 2+ structure where the dioxygen is an end-on physisorbed η 1 - 3 O 2 2 kcal/mol above the actinyl when spin–orbit effects are included which lower the energy of the actinyl structure. For An = Cm, Bk, and Lr, the lowest energy structure is trivalent [An III (O 2 – )] 2+ where the dioxygen is a side-on superoxide, η 2 -O 2 – . For Cm, the actinyl is close in energy to the ground state when spin–orbit effects are included. For An = Cf, Es, Fm, Md, and No, the lowest energy structure is divalent [An II (O 2 )] 2+ where the dioxygen is an end-on physisorbed η 1 - 3 O 2 . The relative energies suggest that curyl(VI) and berkelyl(VI), like well-known americyl(VI), might be stabilized by coordinating ligands in condensed phases. The results further indicate that for californyl and beyond, the actinyl(VI) moieties will probably be elusive even using strong donor ligands. The prevalence of low oxidation states (OSs) An(II) and An(III) for transplutonium actinides reflects stabilization of the 5f orbitals and validates established trends, including the remarkably high stability of divalent No. Finally, bond distances and other parameters suggest maximum bond covalency around plutonyl(VI), with a particularly substantial decrease in bond strength between americyl(VI) and curyl(VI).
Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning
Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. As a result, the test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset.
Multiresolution Quantum Chemistry: Nonlinear Response Properties at the Basis Set Limit
We benchmark the accuracy of Dunning correlation-consistent Gaussian basis sets for computing frequencydependent second-order hyperpolarizabilities relevant to second-harmonic generation (SHG), using multiresolution analysis (MRA) as a reference. Basis set errors are analyzed using a unit-sphere representation of the effective hyperpolarizability vector, enabling direct assessment of directional error structure. We introduce a relative RMS total error metric that integrates directional deviations over the unit sphere and complement it with signed projection errors that distinguish over- and underestimation. Unsupervised clustering based on these signed directional metrics reveals four distinct convergence behaviors across a set of 68 molecules. Unitsphere visualizations of representative systems show that basis set errors are often highly anisotropic and localized along specific bond directions, even when global error measures appear small. Doubly augmented basis sets consistently outperform singly augmented ones, and core-polarization functions are required for uniform convergence in second-row systems. Overall, this work demonstrates that directional analysis combined with clustering provides a robust framework for understanding basis set convergence in nonlinear optical response properties.
X-ray Spectroscopy of a Rare-Earth Molecular System Measured at the Single Atom Limit at Room Temperature
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Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data
Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.
Inaccessible Polarization-Induced Phase Transitions in a Block Copolymer Electrolyte: An Unconventional Mechanism for the Limiting Current
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Electrochemical Characterization of PEO/LiTFSI Electrolytes Near the Solubility Limit
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