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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media

Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, in this study, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Identifying the Gate-Opening Mechanism in the Flexible Metal–Organic Framework UTSA-300

Atomic-level understanding of the gate-opening phenomenon in flexible porous materials is an important step toward learning how to control, design, and engineer them for applications such as the separation of gases from complex mixtures. Here, we report such mechanistic insight through an in-depth study of the pressure-induced gate-opening phenomenon in our earlier reported metal–organic framework (MOF) Zn(dps) 2 (SiF 6 ) (dps = 4,4'-dipyridylsulfide), also called UTSA-300, using isotherm and calorimetry measurements, in situ infrared spectroscopy, and ab initio simulations. UTSA-300 is shown to selectively adsorb acetylene (C 2 H 2 ) over ethylene (C 2 H 4 ) and ethane (C 2 H 6 ) and undergoes an abrupt gate-opening phenomenon, making this framework a highly selective gas separator of this complex mixture. The selective adsorption is confirmed by pressure-dependent in situ infrared spectroscopy, which, for the first time, shows the presence of multiple C 2 H 2 species with varying strengths of bonding. A rare energetic feature at the gate-opening condition of the flexible MOF is observed in our differential heat energies, directly measured by calorimetry, showcasing the importance of this tool in adsorption property exploration of flexible frameworks and offering an energetic benchmark for further energy-based fundamental studies. Based on the agreement of this feature with ab initio-based adsorption energies of C 2 H 2 in the closed-pore structure UTSA-300a (“a” refers to the activated form), this feature is assigned to the weakening of the H-bond C–H···F formed between C 2 H 2 and fluorine of the MOF. Furthermore, our analysis identifies the weakening of this H-bond, the expansion of the closed-pore MOF upon successive C 2 H 2 coadsorption until its volume is close to that of the open-pore MOF, and the spontaneous gate opening to energetically favor C 2 H 2 adsorption in the open-pore structure as crucial steps in the gate-opening mechanism in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Stochastic microstructural analysis of ceramic matrix composites using a high-fidelity multiscale framework

This paper presents a high-fidelity multiscale framework to simulate the mechanical behavior of ceramic matrix composites (CMCs), accounting for the complex micromorphology captured using detailed material characterization. First, high-resolution micrographs are obtained for the specific carbon fiber silicon-carbide-nitride matrix (C/SiNC) CMC to characterize the variability of the architectural features and manufacturing-induced defects at the microscale. An image processing algorithm is then used to precisely estimate the size and distribution of all subscale features and defects from the micrographs. The information is then used to generate a three-dimensional stochastic representative volume element (SRVE) to reconstruct microscale constituents accounting for the variability. As a result, the generated SRVEs are simulated using the high-fidelity generalized method of cells (HFGMC) micromechanics theory to investigate the effects of defects on the elastic properties of C/SiNC CMCs.

Ceramic Matrix Composites↗
DOE OSTI2020

Nanoporous Catalysts for Biomass Conversion

Transforming plant biomass to biofuel and chemicals has become a global effort as results of increasing fuel demand but diminishing fossil-based energy preservation and rising concerns of climate and environmental impact. Various conversion routes involving thermochemical and biological conversions have been established and well-studied. Heterogeneous catalysis is playing a critical role in the biomass conversion and, with some unique catalytic properties, nanoporous catalysts have been extensively utilized in catalytic process of biomass conversion and exhibited exciting catalytic performance. This chapter will be devoted to nanoporous catalysts used in biomass conversion. It will start with a brief introduction of current biomass conversion technologies and then a detailed review of the application of various nanoporous materials in a large diversity of catalytic reactions in biomass conversion. The nanoporous materials include zeolites and zeotypes, metal-organic frameworks (MOFs), nanoporous carbons and carbon nitrides, nanoporous oxides, hydroxides and complex oxide forms, porous organic polymers (POPs), and porous metals.

Shi, Hui↗
DOE OSTI2021

Ambient-Temperature Hydrogen Storage via Vanadium(II)-Dihydrogen Complexation in a Metal-Organic Framework

In this report, the widespread implementation of H 2 as a fuel is currently hindered by the high pressures or cryogenic temperatures required to achieve reasonable storage densities. In contrast, the realization of materials that strongly and reversibly adsorb hydrogen at ambient temperatures and moderate pressures could transform the transportation sector and expand adoption of fuel cells in other applications. To date, however, no adsorbent has been identified that exhibits a binding enthalpy within the optimal range of -15 to -25 kJ/mol for ambient-temperature hydrogen storage. Here, we report the hydrogen adsorption properties of the metal-organic framework (MOF) V 2 Cl 2.8 (btdd) (H 2 btdd, bis(1 H -1,2,3-triazolo[4,5- b ],[4',5'- i ])dibenzo[1,4]dioxin), which features exposed vanadium(II) sites capable of backbonding with weak π acids. Significantly, gas adsorption data reveal that this material binds H 2 with an enthalpy of -21 kJ/mol. This binding energy enables usable hydrogen capacities that exceed that of compressed storage under the same operating conditions. The Kubas-type vanadium(II)-dihydrogen complexation is characterized by a combination of techniques. From powder neutron diffraction data, a V-D 2 (centroid) distance of 1.966(8) Å is obtained, the shortest yet reported for a MOF. Using in situ infrared spectroscopy, the H-H stretch was identified, and it displays a red shift of 242 cm -1 . Electronic structure calculations show that a main contribution to bonding stems from the interaction between the vanadium d π and H 2 σ* orbital. Ultimately, the pursuit of MOFs containing high densities of weakly π-basic metal sites may enable storage capacities under ambient conditions that far surpass those accessible with compressed gas storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Compressing Hamiltonians with ab initio downfolding for simulating strongly-correlated materials on quantum computers

The accurate first-principles description of strongly correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here, we demonstrate that using quantum algorithms to obtain the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate Ca 2 Cu O 3 , the excitonic ground state of monolayer W Te 2 , and the charge-ordered state of correlated metal Sr VO 3 . Numerical simulations using a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to 54 qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical preoptimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms.

Alvertis, Antonios M. [NASA, Ames; LBNL, Berkeley]↗
DOE OSTI2020

Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals

Industrial separations of near-azeotropic chemicals, species with very similar boiling points, are energy- and capital-intensive. Adsorption-based processes can energy-efficiently separate near-azeotropic mixtures provided suitable adsorbent materials can be found. Among the full diversity of industry-relevant molecules, millions of these mixtures exist, meaning that discovery of mixture-specific adsorbents by direct experiment is infeasible. We show that vast numbers of adsorbents and adsorbing molecules can be explored in a powerful way by coupling atomistic simulations with machine learning. This concept is demonstrated by describing the adsorption of ~54000 industry-relevant chemicals in an experimentally derived set of thousands of metal–organic framework materials. Here, our results identify thousands of near-azeotropic mixtures that can be efficiently separated using adsorption and open possibilities for creating adsorption processes for complex mixtures with many components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Building [U IV 70 (OH) 36 (O) 64 ] 4– oxocluster frameworks with sulfate, transition metals, and U V

Uranium (IV) oxide clusters, colloids, and materials are designed and studied for 1) nuclear materials applications, 2) understanding environmental fate and transport of actinides, and 3) exploring the complex bonding behavior of open-shell f-elements. U IV -oxyhydroxsulfate clusters are particularly relevant in industrial processes and in nature. Recent studies have shown that counter-cations to these polynuclear anions differentiate rich structural topologies in the solid-state. Here we present nine different structures with wheel-shaped [U 70 (OH) 36 (O) 64 (SO 4 ) 60 ] 4- (U 70 ) linked into 1-dimensional and 2-dimensional frameworks with sulfate, divalent transition metals (Cr II , Fe II , Co II , Ni II ) and U V . Here, the small-angle X-ray scattering of these phases dissolved in butylamine reveals differing supramolecular assembly of U 70 clusters, controlled primarily by sulfates. However, observed trends in transition metal linking guide future design of U 70 -materials with different topologies. Finally, U 70 linking via U IV -O-U V -O-U IV bridges presents a rare example of mixed oxidation state uranium oxides without disorder.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Reverse Micelle Based Synthesis of Microporous Materials in Microgravity

Microporous materials include a large group of solids of varying chemical composition as well as porosity. These materials are characterized by channels and cavities of molecular dimensions. The framework structure is made up of interconnecting T-O-T' bonds, where T and T' can be Si, Al, P, Ga, Fe, Co, Zn, B and a host of other elements. Materials with Si-O-Al bonding in the framework are called zeolites and are extensively used in many applications. Ion-exchange properties of these materials are exploited in the consumer and environmental industries. Chemical and petroleum industries use zeolites as catalysts in hydrocarbon transform ations. Synthesis of new microporous frameworks has led to the development of new technologies, and thus considerable effort worldwide is expended in their discovery. Microporous materials are typically made under hydrothermal conditions. Influence of nature of starting reactants, structure directing agents, pH, temperature, and aging all have profound influence on the synthesis process. This is primarily because the most interesting open frameworks are not necessarily the stable structures in the reaction medium. Thus, the discovery of new frameworks is often tied to finding the right composition and synthesis conditions that allow for kinetic stabilization of the structure. This complexity of the synthesis process and limited understanding of it has made it difficult to develop directed is of microporous materials and most advances in this field have been made by trial and error. The basic issues in crystal growth of these materials include: (1) Nature of the nucleation process; (2) Molecular structure and assembly of nuclei; (3) Growth of nuclei into crystals; (4) Morphology control; and (5) Transformation of frameworks into other structures. The NASA-funded research described in this paper focuses on all the above issues and has been described in several publications. We present the highlights of our program, especially with the focus on possible experiments in microgravity.

Dutta, Prabir K.↗
DOE OSTI2025

Development of an ERT‐Based Framework for Bentonite Buffers Monitoring From Laboratory Tests: 2. Quantitative Moisture Dynamics Estimation Model

Abstract The long‐term containment of high‐level radioactive waste in geological disposal repositories relies on Engineered Barrier Systems (EBS), with bentonite clay emerging as a candidate material due to its unique properties. Understanding moisture dynamics within bentonite buffers is crucial for EBS performance, as it directly influences the material's swelling capacity, thermal and hydraulic conductivity, mechanical properties, and long‐term evolution under complex thermal‐hydrological‐mechanical (THM) processes. This study develops an advanced Electrical Resistivity Tomography (ERT)‐based framework to quantitatively monitor moisture dynamics under THM conditions. Our framework extends the Waxman‐Smits model to incorporate the coupled effects of temperature, water content, fluid chemistry, and mechanical changes on bentonite's electrical properties. Utilizing HotBENT‐Lab data from our companion paper, which includes electrical conductivity, CT density, and thermocouple measurements, this study offers a novel methodological framework bridging different scales of the model. Our results show that the extended model can estimate water content from ERT data, capturing spatial and temporal variations in moisture distribution within bentonite columns. However, the model tends to overestimate water content compared to CT density‐derived measurements. We address this discrepancy by incorporating a simplified swelling effect model, which improves agreement between ERT and CT density‐based water content estimates. We also discuss model limitations, including simplified treatment of swelling and micropore effects, and propose a conceptual framework for transitioning from laboratory to field applications, addressing challenges such as parameter scalability, field validation methods, and integration of diverse data sources. This ERT‐based framework can potentially advance real‐world moisture monitoring of bentonite‐based EBS in nuclear waste repositories. Plain Language Summary Safely containing high‐level radioactive waste depends on barriers made from materials like bentonite clay, which is effective because it swells and seals in the waste. To ensure these barriers work well over time, it's important to understand how moisture moves through the clay. Our study developed a new method using ERT to monitor moisture levels in bentonite under conditions that mimic those in actual storage sites, including changes in temperature, water content, and mechanical stress. This study improved an existing model to better account for how these factors affect the clay, allowing us to create more accurate moisture maps. Initially, the proposed model overestimated the amount of water in the clay, but its accuracy was improved by factoring in how the clay swells when wet. This study also identified some limitations of the model and suggested ways to adapt it for use in real‐world waste storage sites. This new approach could lead to better monitoring and safety checks for nuclear waste storage systems, helping to ensure long‐term containment. Key Points This work develops an ERT‐based framework extending the Waxman‐Smits model to monitor bentonite moisture dynamics during coupled THM processes The extended model accurately estimates water content from Electrical Resistivity Tomography data, incorporating swelling effects to improve precision This work proposes a conceptual framework for transitioning from laboratory to field applications, advancing EBS monitoring in nuclear waste repositories

Chen, Hang↗
DOE OSTI2022

Metallic and complex hydride-based electrochemical storage of energy

Abstract The development of efficient storage systems is one of the keys to the success of the energy transition. There are many ways to store energy, but among them, electrochemical storage is particularly valuable because it can store electrons produced by renewable energies with a very good efficiency. However, the solutions currently available on the market remain unsuitable in terms of storage capacity, recharging kinetics, durability, and cost. Technological breakthroughs are therefore expected to meet the growing need for energy storage. Within the framework of the Hydrogen Technology Collaboration Program—H 2 TCP Task-40, IEA’s expert researchers have developed innovative materials based on hydrides (metallic or complex) offering new solutions in the field of solid electrolytes and anodes for alkaline and ionic batteries. This review presents the state of the art of research in this field, from the most fundamental aspects to the applications in battery prototypes.

Cuevas, Fermin (ORCID:0000000290555880)↗
DOE OSTI2020

Encoding Multilayer Complexity in Anti‐Counterfeiting Heterometallic MOF‐Based Optical Tags

Abstract Optical tags provide a way to quickly and unambiguously identify valuable assets. Current tag fluorophore options lack the tunability to allow combined methods of encoding in a single material. Herein we report a design strategy to encode multilayer complexity in a family of heterometallic rare‐earth metal–organic frameworks based on highly connected nonanuclear clusters. To impart both intricacy and security, a synergistic approach was implemented resulting in both overt (visible) and covert (near‐infrared, NIR) properties, with concomitant multi‐emissive spectra and tunable luminescence lifetimes. Tag authentication is validated with a variety of orthogonal detection methodologies. Importantly, the effect induced by subtle compositional changes on intermetallic energy transfer, and thus on the resulting photophysical properties, is demonstrated. This strategy can be widely implemented to create a large library of highly complex, difficult‐to‐counterfeit optical tags.

Deneff, Jacob I.↗
DOE OSTI2022

Sulfated Zirconium Metal–Organic Frameworks as Well-Defined Supports for Enhancing Organometallic Catalysis

Understanding heterogeneous catalysts is a challenging pursuit due to surface site nonuniformity and aperiodicity in traditionally-used materials. One example is sulfated metal oxides, which function as highly-active catalysts and as supports for organometallic complexes. These applications are due to traits such as acidity, ability to act as a weakly coordinating ligand, and aptitude for promoting transformations via radical cation intermediates. Research is ongoing about the structural features of sulfated metal oxides that imbue the aforementioned properties, such as sulfate geometry and coordination. To better understand these materials, metal–organic frameworks (MOFs) have been targeted as structurally-defined analogs. Composed of inorganic nodes and organic linkers, MOFs possess features such as high porosity and crystallinity, which make them attractive for mechanistic studies of heterogeneous catalysts. In this work, a Zr 6 -based MOF NU-1000 is sulfated and characterized using atomically-precise techniques such as single crystal X-ray diffraction (SCXRD) in addition to diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). Here, the dynamic nature of the sulfate binding motif is found to transition from monodentate, to bidentate, to tridentate depending on the degree of hydration, as supported by density functional theory (DFT) calculations. Heightened Brønsted acidity compared to the parent MOF was observed upon sulfation, and probed through trimethylphosphine oxide (TMPO) physisorption, ammonia sorption, in-situ ammonia DRIFTS, and DFT studies. With the support structure benchmarked, an organoiridium complex was chemisorbed onto the sulfated MOF node and the efficacy of this supported catalyst was demonstrated for stoichiometric and catalytic activation of benzene-d6 and toluene with structure-activity relationships derived.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Time- and rate-dependent material functions in non-Newtonian fluids in response to different deformation fields pose a challenge in integrating different constitutive models into conventional computational fluid dynamic platforms. Considering their relevance in many industrial and natural settings alike, robust data-driven frameworks that enable accurate modeling of these complex fluids are of great interest. The main goal is to solve the coupled Partial Differential Equations (PDEs) consisting of the constitutive equations that relate the shear stress to the deformation and fully capture the behavior of the fluid under various flow protocols with different boundary conditions. Here, in this work, we present non-Newtonian physics-informed neural networks (nn-PINNs) for solving systems of coupled PDEs adopted for complex fluid flow modeling. The proposed nn-PINN method is employed to solve the constitutive models in conjunction with conservation of mass and momentum by benefiting from Automatic Differentiation (AD) in neural networks, hence avoiding the mesh generation step. nn-PINNs are tested for a number of different complex fluids with different constitutive models and for several flow protocols. These include a range of Generalized Newtonian Fluid (GNF) empirical constitutive models, as well as some phenomenological models with memory effects and thixotropic timescales. nn-PINNs are found to obtain the correct solution of complex fluids in spatiotemporal domains with good accuracy compared to the ground truth solution. We also present applications of nn-PINNs for complex fluid modeling problems with unknown boundary conditions on the surface, and show that our approach can successfully recover the velocity and stress fields across the domain, including the boundaries, given some sparse velocity measurements.

42 ENGINEERING↗
DOE OSTI2023

A customized MOF-polymer composite for rapid gold extraction from water matrices

With the fast-growing accumulation of electronic waste and rising demand for rare metals, it is compelling to develop technologies that can promotionally recover targeted metals, like gold, from waste, a process referred to as urban mining. Thus, there is increasing interest in the design of materials to achieve rapid, selective gold capture while maintaining high adsorption capacity, especially in complex aqueous-based matrices. Here, a highly porous metal-organic framework (MOF)–polymer composite, BUT-33–poly(para-phenylenediamine) (PpPD), is assessed for gold extraction from several matrices including river water, seawater, and leaching solutions from CPUs. BUT-33–PpPD exhibits a record-breaking extraction rate, with high Au3+ removal efficiency (>99%) within seconds (less than 45 s), a competitive capacity (1600 mg/g), high selectivity, long-term stability, and recycling ability. Furthermore, the high porosity and redox adsorption mechanism were shown to be underlying reasons for the material’s excellent performance. Given the accumulation of recovered metallic gold nanoparticles inside, the material was also efficiently applied as a catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

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.

Charge transfer↗
DOE OSTI2019

Competing stripe and magnetic phases in the cuprates from first principles

Realistic description of competing phases in complex quantum materials has proven extremely challenging. For example, much of the existing density-functional-theory-based first-principles framework fails in the cuprate superconductors. Various many-body approaches involve generic model Hamiltonians and do not account for the interplay between the spin, charge, and lattice degrees of freedom. Here, by deploying the recently constructed strongly constrained and appropriately normed (SCAN) density functional, we show how the landscape of competing stripe and magnetic phases can be addressed on a first-principles basis both in the parent insulator YBa 2 Cu 3 O 6 and the near-optimally doped YBa 2 Cu 3 O 7 as archetype cuprate compounds. In YBa 2 Cu 3 O 7 , we find many stripe phases that are nearly degenerate with the ground state and may give rise to the pseudogap state from which the high-temperature superconducting state emerges. We invoke no free parameters such as the Hubbard U , which has been the basis of much of the existing cuprate literature. Lattice degrees of freedom are found to be crucially important in stabilizing the various phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗