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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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At least 127 records · Page 7

Competitive Binding of Ethylene, Water, and Carbon Monoxide in Metal–Organic Framework Materials with Open Cu Sites

Typically, metal–organic frameworks (MOFs) with open metal sites (OMS) are understood to have selectivity in olefin/paraffin separations because of π–π interactions between olefin double bonds and OMS. One concern associated with these separations is that other species that potentially bind to OMS may also be present in feed streams, causing competition for these sites. We used density functional theory (DFT) to assess the binding energy of ethylene, water, and carbon monoxide on a set of more than 60 MOFs with open Cu sites in the form of Cu dimers. A useful observation from our results is that the relative binding energies of pairs of molecules (e.g., ethylene and water) can be calculated accurately from calculations that hold the MOF structure rigid and only relax the positions of the adsorbing molecules. These kinds of calculations are far more numerically efficient than calculations that relax all degrees of freedom in the system, so this observation will be useful in future efforts to screen larger collections of materials. A second observation is that the binding energies of each molecule in the 60 MOFs are quite similar to the binding energies in CuBTC, an exemplar MOF with open Cu sites in the form of Cu dimers. Analysis of the variations that do exist in the binding energies among materials points to possible avenues for controlling either the absolute binding energies or the relative binding energies of species associated with OMS in these materials. The third observation is that two unusual MOFs can bind ethylene more strongly than water because of a dual-site binding mechanism in which an ethylene molecule can interact simultaneously with both dimers while the smaller water molecule interacts primarily with a single OMS. This observation suggests a possible avenue for developing other MOFs in which the binding energy of ethylene is higher than that of water.

36 MATERIALS SCIENCE↗

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Strongly Correlated States of Transition Metal Spin Defects: The Case of an Iron Impurity in Aluminum Nitride

We investigate the electronic properties of an exemplar transition metal impurity in an insulator, with the goal of accurately describing strongly correlated defect states. Here, we consider iron in aluminum nitride, a material of interest for hybrid quantum technologies, and we carry out calculations with quantum embedding methods, density matrix embedding theory (DMET) and quantum defect embedding theory (QDET), and with spin-flip time-dependent density functional theory (TDDFT). We show that both DMET and QDET accurately describe the ground state and low-lying excited states of the defect and that TDDFT yields photoluminescence spectra in agreement with experiments. In addition, we provide a detailed discussion of the convergence of our results as a function of the active space used in the embedding methods, thus defining a protocol to obtain converged data directly comparable with experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Teaching an Old Reagent New Tricks: Synthesis, Unusual Reactivity, and Solution Dynamics of Borohydride Grignard Compounds

Grignard reagents of general formula RMgX (X = Cl - , Br - , I - ) have been utilized in various chemistries for over 100 years. Here, we report that replacing the halide in a Grignard reagent with a reactive borohydride anion adds a new synthetic dimension for these influential compounds. Here, we synthesized the series RMgBH 4 (R=Et, n-Bu, Ph, Bn) and characterized the reactivity toward both organic and inorganic molecules. Using butylmagnesium borohydride (BuMgBH 4 ) as an exemplar, we demonstrate that these compounds possess unique reactivity due to the presence of reducing borohydride groups, resulting in tandem reactivity with organic amides/esters to generate secondary and primary alcohols. Molecular dynamics simulations indicate the stability of BuMgBH 4 is comparable to that of Mg(BH 4 ) 2 + MgBu 2 , validating the Schlenk equilibrium in borohydride Grignard compounds. Metadynamics simulations confirm that the equilibrium is kinetically accessible through solvent-mediated processes. BuMgBH 4 also reacts with CO 2 and NH 3 , revealing potential uses for CO 2 utilization and as a mixed-anion metal borohydride/amide precursor.

36 MATERIALS SCIENCE↗

Influence of Ether Solvent and Anion Coordination on Electrochemical Behavior in Calcium Battery Electrolytes

The emergence of magnesium and calcium batteries as potential beyond Li ion energy storage technologies has generated significant interest into the fundamental aspects of alkaline earth metal cation coordination in multivalent electrolytes and the impact of coordination on application-critical electrolyte properties such as solubility, transport, and electrochemical stability. Understanding these details in calcium electrolytes is of immediate importance due to recent, unprecedented demonstrations of reversible calcium metal electrodeposition in a limited number of ethereal solvent-based systems. In this work, we provide insight connecting Ca 2+ coordination tendencies to important calcium battery electrolyte properties. Our results demonstrate a clear solvent:Ca 2+ coordination strength trend across a series of cyclic ether and linear glyme solvents that controls the extent of ion association in solutions of “weakly” coordinating salts. We apply understanding gained from these results to rationalize relative anion:Ca 2+ coordination tendencies and attendant Ca 2+ coordination structures using two oxidatively stable anions of particular interest for current battery electrolytes. Armed with this understanding of solvent and anion interactions with Ca 2+ , we demonstrate and interpret differences in electrochemical calcium deposition behavior across several electrolyte exemplars with varying solvent and anion coordination strengths. Our findings demonstrate that solvents exhibiting especially strong coordination to Ca 2+ , such as triglyme, can inhibit reversible calcium deposition despite effective elimination of anion:Ca 2+ coordination while solvents exhibiting more modest coordination strength, such as 1,2-dimethoxyethane, may enable deposition provided anion:Ca 2+ coordination is substantially limited. These results reveal that the strength of coordination of both anion and solvent should be considered in the design of electrolytes for calcium batteries.

25 ENERGY STORAGE↗

Effect of Salt Concentration on the Interfacial Solvation Structure and Early Stage of Solid–Electrolyte Interphase Formation in Ca(BH 4 ) 2 /THF for Ca Batteries

The Ca 2+ solvation structure at the electrolyte/electrode interface is of central importance to understand electroreduction stability and solid–electrolyte interphase (SEI) formation for the novel multivalent Ca battery systems. Here, using an exemplar electrolyte, the concentration-dependent solvation structure of Ca(BH 4 ) 2 -tetrahydrofuran on a gold model electrode has been investigated with various electrolyte concentrations via electrochemical quartz crystal microbalance with dissipation (EQCM-D) and X-ray photoelectron spectroscopy (XPS). For the first time, in situ EQCM-D results prove that the prevalent species adsorbed at the interface is CaBH 4 + across all concentrations. As the salt concentration increases, the number of BH 4 – anions associated with Ca 2+ increases, and much larger solvated complexes such as CaBH 4 + ·4THF or Ca(BH 4 ) 3 – ·4THF form at the interface at high concentrations prior to Ca plating. Different interfacial chemistries lead to the formation of SEIs with different components demonstrated by XPS. High electrolyte concentrations reduce the solvent decomposition and promote the formation of thick, uniform, and inorganic-rich (i.e., CaO) SEI layers, which contribute to improved Ca plating efficiency and current density in electrochemical measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exceptional Electrical Detection of Trace NO 2 via Mixed Metal MOF-on-MOF Film-Based Sensors

The tunability of metal–organic frameworks (MOFs) makes them exceptional materials for the development of highly selective, low-power sensors for toxic gas detection. Herein, we demonstrate enhanced detection of NO 2 gas by a MOF-based electrical impedance sensor made using a unique mixed metal MOF-on-MOF synthesis. For this work, a combined experimental and computational study was performed using the exemplar Ni x Mg 1–x -MOF-74 to understand the fundamental structure–property relationships behind metal mixing and MOF film synthesis methods on sensor performance. Density functional theory results indicated that the presence of Ni in Mg-MOF-74 increased framework stability and increased the electron density of states at lower energies near the HOMO, as well as enhanced the NO 2 –Mg adsorption interaction. Impedance data of the Ni x Mg 1–x -MOF-74 films with larger Ni contents showed greater impedance change after exposure to 1 ppm of NO 2 gas. Furthermore, when synthesized through either a drop-cast or direct solvothermal film growth approach, the monometallic Ni-based sensors had the best performance. However, the mixed metal Ni x Mg 1–x -MOF-74 sensors synthesized through a MOF-on-MOF approach resulted in the highest impedance change, outperforming all monometallic Ni-based sensors. In particular, the mixed metal Ni-on-Mg-MOF-74 film was the best-performing sensor with an impedance change of 309 upon trace NO 2 exposure. Change in impedance response after NO 2 exposure was improved by 52% compared to the best monometallic Ni-on-Ni-MOF-74 sensor. Structural analysis of the Ni-on-Mg film showed that the first Mg-MOF-74 layer acts as a structural template controlling the structural features of the final film after metal exchange with Ni. This led to improved film quality, evidenced by the greater crystallinity and larger MOF grain sizes, and resulted in enhanced sensor performance which was not achievable through other metal mixing methods. Altogether, this study identifies structure–property relationships and synthetic templating methods that inform MOF-based sensor design, allowing for improved detection of toxic compounds.

36 MATERIALS SCIENCE↗

Vertical Strain-Driven Antiferromagnetic to Ferromagnetic Phase Transition in EuTiO 3 Nanocomposite Thin Films

Three-dimensional (3D) strain induced in self-assembled vertically aligned nanocomposite (VAN) epitaxial films provides an unrivaled method to induce very large strains in thin films. Here, by growing VAN films of EuTiO 3 (ETO)–Eu 2 O 3 (EO) with different EO fractions, the vertical strain was systematically increased in ETO, up to 3.15%, and the Eu–Ti–Eu bond angle along $\langle$111$\rangle$ decreased by up to 1°, leading to a weakening of the antiferromagnetic interactions and switching from antiferromagnetic to ferromagnetic behavior. Our work has shown for the first time that Eu–Ti–Eu superexchange interactions play a key role in determining the magnetic ground state of ETO. Broadly, our work serves as an exemplar to show that multifunctionalities in strong spin–lattice coupling perovskite oxides can be uniquely tuned at the atomic scale using simple VAN structures.

36 MATERIALS SCIENCE↗

Reversing the Irreversible: Thermodynamic Stabilization of LiAlH 4 Nanoconfined Within a Nitrogen-Doped Carbon Host

A general problem when designing functional nanomaterials for energy storage is the lack of control over the stability and reactivity of metastable phases. Using the high-capacity hydrogen storage candidate LiAlH4 as an exemplar, we demonstrate an alternative approach to the thermodynamic stabilization of metastable metal hydrides by coordination to nitrogen binding sites within the nanopores of N-doped CMK-3 carbon (NCMK-3). The resulting LiAlH 4 @NCMK-3 material releases H 2 at temperatures as low as 126 °C with full decomposition below 240 °C, bypassing the usual Li 3 AlH 6 intermediate observed in bulk. Moreover, >80% of LiAlH 4 can be regenerated under 100 MPa H 2 , a feat previously thought to be impossible. Nitrogen sites are critical to these improvements, as no reversibility is observed with undoped CMK-3. Density functional theory predicts a drastically reduced Al–H bond dissociation energy and supports the observed change in the reaction pathway. Finally, the calculations also provide a rationale for the solid-state reversibility, which derives from the combined effects of nanoconfinement, Li adatom formation, and charge redistribution between the metal hydride and the host.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Chemical Redox Agent-Driven Noncorrosive Formation of Nanoporous Mg Structures for Advanced Hydrogen Storage

Light metal-based nanomaterials are widely used for energy storage due to their high energy density and surface-to-volume ratio. However, their high reactivity is paradoxically both the source of advantageous properties and a hurdle to the fabrication of stable nanostructures. Here, in this study, we demonstrate the formation of nanoporous Mg via chemical redox agent-driven dealloying, which ensures minimized surface passivation and results in fine nanostructures with <50 nm of interconnected metallic ligament despite the labile chemical properties of Mg. The thin passivation layer protects the metallic ligaments from severe coarsening by suppressing surface diffusion. The hydrogen storage performance of nanoporous Mg is investigated as an exemplar for energy applications, and the hydrogen ab/desorption kinetics is substantially enhanced compared to other nano-Mg with similar dimensions. Mesoscale simulations highlight the significance of the bicontinuous structure compared to the particle-like counterpart. This work offers valuable insights into the unexplored realm of reactive metal-based nanoporous structures, highlighting their potential for sustainable energy storage and carrier media.

08 HYDROGEN↗

Sequence-Dependent Structure/Function Relationships of Catalytic Peptide-Enabled Gold Nanoparticles Generated under Ambient Synthetic Conditions

Peptide-enabled nanoparticle (NP) synthesis routes can create and/or assemble functional nanomaterials under environmentally friendly conditions, with properties dictated by complex interactions at the biotic/abiotic interface. Manipulation of this interface through sequence modification can provide the capability for material properties to be tailored to create enhanced materials for energy, catalysis, and sensing applications. Fully realizing the potential of these materials requires a comprehensive understanding of sequence-dependent structure/function relationships that is presently lacking. In this work, the atomic-scale structures of a series of peptide-capped Au NPs are determined using a combination of atomic pair distribution function analysis of high-energy X-ray diffraction data and advanced molecular dynamics (MD) simulations. The Au NPs produced with different peptide sequences exhibit varying degrees of catalytic activity for the exemplar reaction 4-nitrophenol reduction. The experimentally derived atomic-scale NP configurations reveal sequence-dependent differences in structural order at the NP surface. Replica exchange with solute-tempering MD simulations are then used to predict the morphology of the peptide overlayer on these Au NPs and identify factors determining the structure/catalytic properties relationship. We show that the amount of exposed Au surface, the underlying surface structural disorder, and the interaction strength of the peptide with the Au surface all influence catalytic performance. A simplified computational prediction of catalytic performance is developed that can potentially serve as a screening tool for future studies. Our approach provides a platform for broadening the analysis of catalytic peptide-enabled metallic NP systems, potentially allowing for the development of rational design rules for property enhancement.

Bedford, Nicholas M.↗

Mineral Protection and Resource Limitations Combine to Explain Profile-Scale Soil Carbon Persistence

The fate of soil carbon (C) is largely controlled by microbial oxidation of organic matter (OM), which is constrained by a variety of mechanisms. OM association with soil minerals provides pronounced protection against microbial decomposition. However, factors such as climate, occlusion, and resource limitations also contribute to OM preservation. Here, we explore the factors explaining C distribution and age within an upland rainforest soil in Hawai'i, a site with abundant preferential flow paths (PFPs) and high short-range order (SRO) mineral content. We characterized lateral and vertical changes in Δ 14 C, SRO mineral content, C-functional group chemistry, and microbial community composition to elucidate the contributions of multiple protection mechanisms to OM preservation. Consistent with our expectation, SRO mineral content and Δ 14 C were strongly correlated (R 2 = 0.95), indicating strong mineral protection of OM throughout the profile. Surprisingly, distance from PFP was also a significant predictor of Δ 14 C and improved model fit, particularly in the shallow horizons (R 2 = 0.97). Elevated C/N ratios, decreased microbial abundance, and greater SRO mineral content suggest nitrogen limitations and enhanced mineral protection constrain OM turnover with distance from PFPs in deep, subsurface mineral horizons. Steady microbial abundance, increasing putative anaerobe abundance, and changes in C-functional group chemistry indicate oxygen limitations constrain OM turnover in the matrix of shallow mineral horizons. Given that oxygen and nutrient limitations contribute to OM preservation in this high SRO system—an exemplar of mineral protection—resource limitations may play an even more important role in OM preservation in other well-structured soils.

59 BASIC BIOLOGICAL SCIENCES↗

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Abstract Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec’s ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.

97 MATHEMATICS AND COMPUTING↗

Machine learning potential assisted exploration of complex defect potential energy surfaces

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO 2 ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

36 MATERIALS SCIENCE↗

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

36 MATERIALS SCIENCE↗

Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ( $${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.

59 BASIC BIOLOGICAL SCIENCES↗

Carbon chain shape selectivity by the mouse olfactory receptor OR-I7

The rodent OR-I7 is an olfactory receptor exemplar activated by aliphatic aldehydes such as octanal. Normal alkanals shorter than heptanal bind OR-I7 without activating it and hence function as antagonists in vitro. We report a series of aldehydes designed to probe the structural requirements for aliphatic ligand chains too short to meet the minimum approximate 6.9 Å length requirement for receptor activation. Experiments using recombinant mouse OR-I7 expressed in heterologous cells show that in the context of short aldehyde antagonists, OR-I7 prefers binding aliphatic chains without branches, though a single methyl on carbon-3 is permitted. The receptor can accommodate a surprisingly large number of carbons (e.g. ten in adamantyl) as long as the carbons are part of a conformationally constrained ring system. A rhodopsin-based homology model of mouse OR-I7 docked with the new antagonists suggests that small alkyl branches on the alkyl chain sterically interfere with the hydrophobic residues lining the binding site, but branch carbons can be accommodated when tied back into a compact ring system like the adamantyl and bicyclo[2.2.2]octyl systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗