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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

An Integrated Modeling Framework for Sediment Dynamics During Urban Flooding: Application to Hurricane Harvey in Houston

Floodwater can mobilize and redistribute large volumes of sediment from upland to downstream urban areas, threatening infrastructure, water quality, and ecosystem health. However, existing modeling approaches often fail to capture sediment dynamics in urban floodplains due to the lack of integration between upland hydrological processes and riverine sediment transport. This study presents the first integrated modeling framework that couples the Energy Exascale Earth System Model (E3SM) land component, which simulates runoff and hillslope erosion, with TELEMAC-GAIA, a two-dimensional hydrodynamic and sediment transport model. This framework enables the fully distributed, process-based simulation of high-resolution (as fine as 30 m) sediment dynamics from hillslopes to floodplains. Applied to a highly urbanized watershed in Houston during Hurricane Harvey, this framework reproduced observed water levels at 16 USGS gauges (median R 2 = 0.83 and KGE = 0.78), key sediment dynamics such as sediment transport and deposition processes, and reproduced spatial deposition patterns consistent with LiDAR-derived data. Based on the simulation, we estimate 8.0 million m 3 of event-scale sediment deposition, including 5.7 million m 3 trapped in the flood-control reservoirs and 2.3 million m 3 deposited along major channels and floodplains. Using a representative unit removal cost, this corresponds to an estimated dredging cost of $581 million for total deposition. These results provide a first-order, physically based quantification of Harvey-scale sediment impacts. This study provides a valuable tool for the holistic analysis of sediment dynamics triggered by extreme urban flooding, supporting flood-resilience planning. More broadly, it highlights the importance of integrating physically based hydrological processes for urban flooding and sediment research.

Hurricane Harvey↗
DOE OSTI2017

Tunable thermal expansion in framework materials through redox intercalation

Thermal expansion properties of solids are of fundamental interest and control of thermal expansion is important for practical applications but can be difficult to achieve. Many framework type materials show negative thermal expansion when internal cages are empty but positive thermal expansion when additional atoms or molecules fill internal voids present, offering a potential route for control. Here we show that redox intercalation offers an effective method to control thermal expansion from positive to zero to negative by insertion of Li ions into the simple negative thermal expansion framework material ScF 3 , doped with 10% Fe to enable reduction. The small concentration of intercalated Li ions has a strong influence through steric hindrance of transverse fluoride ion vibrations, which directly controls the thermal expansion. As a result, redox intercalation of guest ions is thus likely to be a general and effective method for controlling thermal expansion in the many known framework materials with phonon-driven negative thermal expansion.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Liquid metal–organic frameworks

Metal–organic frameworks (MOFs) are a family of chemically diverse materials, with applications in a wide range of fields, covering engineering, physics, chemistry, biology and medicine. Until recently, research has focused almost entirely on crystalline structures, yet now a clear trend is emerging, shifting the emphasis onto disordered states, including ‘defective by design’ crystals, as well as amorphous phases such as glasses and gels. Here we introduce a strongly associated MOF liquid, obtained by melting a zeolitic imidazolate framework. We combine in situ variable temperature X-ray, ex situ neutron pair distribution function experiments, and first-principles molecular dynamics simulations to study the melting phenomenon and the nature of the liquid obtained. We demonstrate from structural, dynamical, and thermodynamical information that the chemical configuration, coordinative bonding, and porosity of the parent crystalline framework survive upon formation of the MOF liquid.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Redox-switchable breathing behavior in tetrathiafulvalene-based metal–organic frameworks

Metal–organic frameworks (MOFs) that respond to external stimuli such as guest molecules, temperature, or redox conditions are highly desirable. Herein, we coupled redox-switchable properties with breathing behavior induced by guest molecules in a single framework. Guided by topology, two flexible isomeric MOFs, compounds 1 and 2, with a formula of In(Me 2 NH 2 ) (TTFTB), were constructed via a combination of [In(COO) 4 ]- metal nodes and tetratopic tetrathiafulvalene-based linkers (TTFTB). The two compounds show different breathing behaviors upon the introduction of N 2 . Single-crystal X-ray diffraction, accompanied by molecular simulations, reveals that the breathing mechanism of 1 involves the bending of metal–ligand bonds and the sliding of interpenetrated frameworks, while 2 undergoes simple distortion of linkers. Reversible oxidation and reduction of TTF moieties changes the linker flexibility, which in turn switches the breathing behavior of 2. The redox-switchable breathing behavior can potentially be applied to the design of stimuli-responsive MOFs.

carbon capture↗
DOE OSTI2020

Radiation-resistant metal-organic framework enables efficient separation of krypton fission gas from spent nuclear fuel

Capture and storage of volatile radionuclides that result from processing of used nuclear fuel is a major challenge. Solid adsorbents, in particular ultra-microporous metal-organic frameworks, could be effective in capturing these volatile radionuclides, including 85 Kr. However, metal-organic frameworks are found to have higher affinity for xenon than for krypton, and have comparable affinity for Kr and N 2 . Also, the adsorbent needs to have high radiation stability. To address these challenges, here we evaluate a series of ultra-microporous metal-organic frameworks, SIFSIX-3-M (M = Zn, Cu, Ni, Co, or Fe) for their capability in 85 Kr separation and storage using a two-bed breakthrough method. These materials were found to have higher Kr/N 2 selectivity than current benchmark materials, which leads to a notable decrease in the nuclear waste volume. The materials were systematically studied for gamma and beta irradiation stability, and SIFSIX-3-Cu is found to be the most radiation resistant.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2023

A framework to centre justice in energy transition innovations

The important role of justice in energy transition technologies has been a topic of increasing interest in recent years. However, key questions remain about how inequities influence energy transition innovations (ETIs) from their design to their widespread use, which ETIs receive more funding, and who controls ETI research, prototyping and deployment. Here, in this work, we propose a framework to centre justice in energy transition innovations (CJI) and examine how three tenets of justice (recognition, procedural and distributional justice) influence each level of ETI, including niche, regime and landscape levels. We examine wind energy in Mexico and multiple ETIs in Los Angeles as use cases to show how our CJI framework can help reveal the specific inequities undermining just energy transitions at crucial analytical levels of ETI in practice. Our CJI framework offers a path for promoters, practitioners and underserved communities to target the problems these groups face and create ETIs that better address their specific aspirations, needs and circumstances.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation

Observation of dynamic processes by transmission electron microscopy (TEM) is an attractive technique to experimentally analyze materials’ nanoscale phenomena and understand the microstructure-properties relationships in nanoscale. Even if spatial and temporal resolutions of real-time TEM increase significantly, it is still difficult to say that the researchers quantitatively evaluate the dynamic behavior of defects. Images in TEM video are a two-dimensional projection of three-dimensional space phenomena, thus missing information must be existed that makes image’s uniquely accurate interpretation challenging. Therefore, even though they are still a clustering high-dimensional data and can be compressed to two-dimensional, conventional statistical methods for analyzing images may not be powerful enough to track nanoscale behavior by removing various artifacts associated with experiment; and automated and unbiased processing tools for such big-data are becoming mission-critical to discover knowledge about unforeseen behavior. We have developed a method to quantitative image analysis framework to resolve these problems, in which machine learning and particle filter estimation are uniquely combined. The quantitative and automated measurement of the dislocation velocity in an Fe-31Mn-3Al-3Si autunitic steel subjected to the tensile deformation was performed to validate the framework, and an intermittent motion of the dislocations was quantitatively analyzed. The framework is successfully classifying, identifying and tracking nanoscale objects; these are not able to be accurately implemented by the conventional mean-path based analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A p -orbital honeycomb-Kagome lattice realized in a two-dimensional metal-organic framework

The experimental realization of p-orbital systems is desirable because p-orbital lattices have been proposed theoretically to host strongly correlated electrons that exhibit exotic quantum phases. Here, we synthesize a two-dimensional Fe-coordinated bimolecular metal-organic framework which constitutes a honeycomb lattice of 1,4,5,8,9,12-hexaazatriphenylene molecules and a Kagome lattice of 5,15-di(4-pyridyl)-10,20-diphenylporphyrin molecules on a Au(111) substrate. Density-functional theory calculations show that the framework features multiple well-separated spin-polarized Kagome bands, namely Dirac cone bands and Chern flat bands, near the Fermi level. Using tight-binding modelling, we reveal that these bands are originated from two effects: the low-lying molecular orbitals that exhibit p-orbital characteristics and the honeycomb-Kagome lattice. This study demonstrates that p-orbital Kagome bands can be realized in metal-organic frameworks by using molecules with molecular orbitals of p-orbital like symmetry.

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

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks

We employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) for learning a forward and an inverse solution operator of partial differential equations (PDEs). We focus on steady-state solutions of coupled hydromechanical processes in heterogeneous porous media and present the parameterization of the spatially heterogeneous coefficients, which is exceedingly difficult using standard reduced-order modeling techniques. We show that our framework provides a speed-up of at least 2,000 times compared to a finite-element solver and achieves a relative root-mean-square error (r.m.s.e.) of less than 2% for forward modeling. For inverse modeling, the framework estimates the heterogeneous coefficients, given an input of pressure and/or displacement fields, with a relative r.m.s.e. of less than 7%, even for cases where the input data are incomplete and contaminated by noise. The framework also provides a speed-up of 120,000 times compared to a Gaussian prior-based inverse modeling approach while also delivering more accurate results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Evaluating metal-organic frameworks for natural gas storage

Metal-organic frameworks have received significant attention as a new class of adsorbents for natural gas storage; however, inconsistencies in reporting high-pressure adsorption data and a lack of comparative studies have made it challenging to evaluate both new and existing materials. Here, we briefly discuss high-pressure adsorption measurements and review efforts to develop metal-organic frameworks with high methane storage capacities. To illustrate the most important properties for evaluating adsorbents for natural gas storage and for designing a next generation of improved materials, six metal-organic frameworks and an activated carbon, with a range of surface areas, pore structures, and surface chemistries representative of the most promising adsorbents for methane storage, are evaluated in detail. High-pressure methane adsorption isotherms are used to compare gravimetric and volumetric capacities, isosteric heats of adsorption, and usable storage capacities. Additionally, the relative importance of increasing volumetric capacity, rather than gravimetric capacity, for extending the driving range of natural gas vehicles is highlighted. Other important systems-level factors, such as thermal management, mechanical properties, and the effects of impurities, are also considered, and potential materials synthesis contributions to improving performance in a complete adsorbed natural gas system are discussed.

Mason, JA↗
DOE OSTI2014

Ionothermal synthesis of open-framework metal phosphates with a Kagome lattice network exhibiting canted anti-ferromagnetism

Four open-framework transition-metal phosphates; (NH4)2Co3(HPO4)2F4 (1), (NH4)Co3(HPO4)2(H2PO4)F2 (2), KCo3(HPO4)2(H2PO4)F2 (3), and KFe3(HPO4)2(H2PO4)F2 (4); are prepared by ionothermal synthesis using pyridinium hexafluorophosphate as the ionic liquid. Single-crystal X-ray diffraction analyses reveal that the four compounds contain cobalt/iron–oxygen/fluoride layers with Kagomé topology composed of interlinked face-sharing MO3F3/MO4F2 octahedra. PO3OH pseudo-tetrahedral groups augment the [M3O6F4] (1)/[M3O8F2] layers on both sides to give M3(HPO4)2F4 (1) and M3(HPO4)2F2 (2–4) layers. These layers are stacked along the a axis in a sequence AA…, resulting in the formation of a layer structure for (NH4)2Co3(HPO4)2F4(1). In NH4Co3(HPO4)2(H2PO4)F2 and KM3(HPO4)2(H2PO4)F2, the M3(HPO4)2F2 layers are stacked along the a axis in a sequence AAi… and are connected by [PO3(OH)] tetrahedra, giving rise to a 3-D open framework structure with 10-ring channels along the [001] direction. The negative charges of the inorganic framework are balanced by K+/NH4+ ions located within the channels. The magnetic transition metal cations themselves form layers with stair-case Kagomé topology. Magnetic susceptibility and magnetization measurements reveal that all four compounds exhibit a canted anti-ferromagnetic ground state (Tc = 10 or 13 K for Co and Tc = 27 K for Fe) with different canting angles. The full orbital moment is observed for both Co2+ and Fe2+.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Deformation behavior of an amorphous zeolitic imidazolate framework – from a supersoft material to a complex organometallic alloy

Zeolitic imidazolate frameworks (ZIFs)—a subset of metal–organic frameworks (MOFs)—have recently attracted immense attention. Many crystalline ZIFs (c-ZIFs) have highly porous zeolite structures that are ideal for molecular encapsulation. Recently emerging non-crystalline or amorphous ZIFs (a-ZIFs) with a similar short-range order are of interest because they can be converted from c-ZIFs for large-scale production. Here, we present a computational study of the deformation behavior of a unique a-ZIF model by simulating step-wise compression and expansion with strains between –0.389 and +0.376. An insulator-to-metal transition is observed at 51 GPa leading to a multicomponent light amorphous alloy of only 3.68 g (cm) –3 . A high-density amorphous-to-amorphous phase transition is observed due to the sudden formation of N–N bond pairs. Here, the systematic expansion of the a-ZIF retains the framework softness until it fractures at high strain. Based on the expansion data, we propose an empirical formula for super-soft materials, which is in line with available experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Effect of Topology in Lewis Pair Functionalized Metal Organic Frameworks on CO 2 Adsorption and Hydrogenation

We have used density functional theory and classical grand canonical Monte Carlo simulations to identify two functionalized metal organic frameworks (MOFs) that have the potential to be used for both CO 2 capture from flue gas and catalytic conversion of CO 2 to valuable chemicals. These new materials based on MIL-140B and MIL-140C functionalized with Lewis pair (acid and base) moieties, which are integrated into the framework linkers. In this work, we show that the Lewis pair functional groups are capable of catalyzing heterolytic dissociation of H 2 and subsequent hydrogenation of CO 2 through concerted 2-H addition. We have examined the effect of pore size and framework topology on the competitive binding of H 2 and CO 2 . We show that the small pore size of functionalized MIL-140B stabilizes the formation of a pre-activated CO 2 species and that this pre-activated CO 2 has a lower overall reaction barrier for hydrogenation of CO 2 to formic acid than a competing pathway in the same material that does not go through a pre-activated complex. We demonstrate that steric hindrance can potentially break energy scaling relationships, which limit the ability to optimize traditional heterogeneous catalysts, by independently changing one part of the CO 2 hydrogenation pathway, without negatively impacting other parts of the pathway. Specifically, we show that steric effects can reduce the CO 2 hydrogenation barrier without impacting the H 2 dissociation barrier or binding energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Insights into CO 2 adsorption and chemical fixation properties of VPI-100 metal–organic frameworks

Here, metal–organic frameworks (MOFs) have shown great promise as efficient CO 2 adsorbents, as well as an emerging class of heterogeneous catalysts for conversion of CO 2 to other useful chemicals. In this work, we synthesized and characterized two isostructural hafnium-based MOFs, denoted as Hf-VPI-100 (Cu) and Hf-VPI-100 (Ni). Both frameworks demonstrated high catalytic efficiency for cycloaddition of CO 2 to epoxides under ambient pressure. In situ PXRD, QCM and DRIFTs have been used to probe the interaction between the guest molecules (CO 2 /epoxide) and Hf-VPI-100. The crystal structures of these frameworks were preserved during the exposure to CO 2 atmosphere up to 20 bar. The epoxide uptake per unit cell of VPI-100 and diffusion coefficients have been calculated by QCM analysis. Comparison of catalytic efficiency between Hf-VPI-100 and the previously reported Zr-based VPI-100, aided by electronic structure calculations revealed that the open metal centers in the metallocyclam act as the primary Lewis acid sites to facilitate the catalytic conversion of CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Metal-hydrogen-pi-bonded organic frameworks

We report the synthesis and characterization of a new series of permanently porous, three-dimensional metal–organic frameworks (MOFs), M-HAF-2 (M = Fe, Ga, or In), constructed from tetratopic, hydroxamate-based, chelating linkers. Here, the structure of M-HAF-2 was determined by three-dimensional electron diffraction (3D ED), revealing a unique interpenetrated hcb-a net topology. This unusual topology is enabled by the presence of free hydroxamic acid groups, which lead to the formation of a diverse network of cooperative interactions comprising metal–hydroxamate coordination interactions at single metal nodes, staggered π–π interactions between linkers, and H-bonding interactions between metal-coordinated and free hydroxamate groups. Such extensive, multimodal interconnectivity is reminiscent of the complex, noncovalent interaction networks of proteins and endows M-HAF-2 frameworks with high thermal and chemical stability and allows them to readily undergo postsynthetic metal ion exchange (PSE) between trivalent metal ions. We demonstrate that M-HAF-2 can serve as versatile porous materials for ionic separations, aided by one-dimensional channels lined by continuously π-stacked aromatic groups and H-bonding hydroxamate functionalities. As an addition to the small group of hydroxamic acid-based MOFs, M-HAF-2 represents a structural merger between MOFs and hydrogen-bonded organic frameworks (HOFs) and illustrates the utility of non-canonical metal-coordinating functionalities in the discovery of new bonding and topological patterns in reticular materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗