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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Membrane-Based Osmotic Heat Engine with Organic Solvent for Enhanced Power Generation from Low-Grade Heat

We present a hybrid osmotic heat engine (OHE) system that uses draw solutions with an organic solvent for enhanced thermal separation efficiency. The hybrid OHE system produces sustainable energy by combining pressure-retarded osmosis (PRO) as a power generation stage and membrane distillation (MD) utilizing low-grade heat as a separation stage. While previous OHE systems employed aqueous electrolyte draw solutions, using methanol as a solvent is advantageous because methanol is highly volatile and has a lower heat capacity and enthalpy of vaporization than water. Hence, the thermal separation efficiency of a draw solution with methanol would be higher than that of an aqueous draw solution. In this study, we evaluated the performance of LiCl-methanol as a potential draw solution for a PRO-MD hybrid OHE system. The membrane transport properties as well as performance with LiCl methanol draw solution were evaluated using thin-film composite (TFC) PRO membranes and compared to the results obtained with a LiCl water draw solution. Experimental PRO methanol flux and maximum projected power density of 47.1 L m(-2) h(-1) and 72.1 W m(-2), respectively, were achieved with a 3 M LiCl-methanol draw solution. The overall efficiency of the hybrid OHE system was modeled by coupling the mass and energy flows between the thermal separation (MD) and power generation (PRO) stages under conditions with and without heat recovery. The modeling results demonstrate higher ORE energy efficiency with the LiCl methanol draw solution compared to that with the LiCl water draw solution under practical operating conditions (i.e., heat recovery <90%). We discuss the implications of the results for converting low-grade heat to power.

Shaulsky, E↗
DOE OSTI2021

Fluoroarene Separations in Metal-Organic Frameworks with Two Proximal Mg 2+ Coordination Sites

Fluoroarenes which are widely used in medicinal, agricultural, and materials chemistry, and yet their production remains a critical challenge in organic synthesis. Indeed, the nearly identical physical properties of these vital building blocks hinders their purification by traditional methods, such as flash chromatography or distillation. As a result, the Balz-Schiemann reaction is currently employed to prepare fluoroarenes instead of more atom-economical C-H fluorination reactions, which produce inseparable mixtures of regioisomers. Herein, we propose an alternative solution to this problem: the purification of mixtures of fluoroarenes using metal-organic frameworks (MOFs). Specifically, we demonstrate that controlling the interaction of fluoroarenes with adjacent coordinatively unsaturated Mg 2+ centers within a MOF enables the separation of fluoroarene mixtures with unparalleled selectivities. Liquid-phase multicomponent equilibrium adsorption data and breakthrough measurements coupled with van der Waals-corrected density functional theory calculations reveal that the materials Mg 2 (dobdc) (dobdc 4- = 2,5-dioxidobenzene-1,4-dicarboxylate) and Mg 2 ( m -dobdc) ( m -dobdc 4- = 2,4-dioxidobenzene-1,5-dicarboxylate) are capable of separating the difluorobenzene isomers from one another. Additionally, these frameworks facilitate the separations of fluoroanisoles, fluorotoluenes, and fluorochlorobenzenes. In addition to enabling currently unfeasible separations for the production of fluoroarenes, our results suggest that carefully controlling the interaction of isomers with not one but two strong binding sites within a MOF provides a general strategy for achieving challenging liquid-phase separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimal Binding Affinity for Sieving Separation of Propylene from Propane in an Oxyfluoride Anion-Based Metal–Organic Framework

Highly efficient adsorptive separation of propylene from propane offers an ideal alternative method to replace the energy-intensive cryogenic distillation technology. Molecular sieving-type separation via high-performance adsorbents is targeted for superior selectivity, but the limit in adsorption capacity remains a great challenge. Here, we report an oxyfluoride-based ultramicroporous metal–organic framework UTSA-400, [Ni(WO 2 F 4 )(pyz) 2 ] (pyz = pyrazine), featuring one-dimensional pore channels that can accommodate the propylene molecules with optimal binding affinity while specifically excluding the propane molecules. The exposed oxide/fluoride pairs in UTSA-400 serve as strong functional sites for strengthened propylene–host interactions, accounting for a significantly enhanced propylene uptake, while the propane molecules are excluded due to the regulated host framework dynamics. The strong propylene binding enables near-saturation of propylene in the pore confinement at ambient conditions, leading to full utilization of pore space and superior packing density. Combined in situ infrared spectroscopy measurements and dispersion-corrected density functional theory calculations clearly unveil the nature of boosted host–guest binding. Direct production of polymer-grade (>99.5%) propylene with remarkable dynamic productivity is demonstrated by column breakthrough experiments. Furthermore, this work presents an example of pore engineering with atomic precision to break the trade-off in adsorptive separation through guest binding optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Adaptive Pore Opening to Form Tailored Adsorption Sites in a Cooperatively Flexible Framework Enables Record Inverse Propane/Propylene Separation

A proposed low-energy alternative to the separation of alkanes from alkenes by energy-intensive cryogenic distillation is separation by porous adsorbents. Unfortunately, most adsorbents preferentially take up the desired, high-value major component alkene, requiring frequent regeneration. Adsorbents with inverse selectivity for the minor component alkane would enable the direct production of purified, reagent-grade alkene, greatly reducing global energy consumption. However, such materials are exceedingly rare, especially for propane/propylene separation. Here, we report that through adaptive and spontaneous pore size and shape adaptation to optimize an ensemble of weak noncovalent interactions, the structurally responsive metal-organic framework CdIF-13 (sod-Cd(benzimidazolate) 2 ) exhibits inverse selectivity for propane over propylene with record-setting separation performance under industrially relevant temperature, pressure, and mixture conditions. Powder synchrotron X-ray diffraction measurements combined with first-principles calculations yield atomic-scale insight and reveal the induced fit mechanism of adsorbate-specific pore adaptation and ensemble interactions between ligands and adsorbates. Dynamic column breakthrough measurements confirm that CdIF-13 displays selectivity under mixed-component conditions of varying ratios, with a record measured selectivity factor of α ≈ 3 at 95:5 propylene:propane at 298 K and 1 bar. When sequenced with a low-cost rigid adsorbent, we demonstrated the direct purification of propylene under ambient conditions. In conclusion, this combined atomic-level structural characterization and performance testing firmly establishes how cooperatively flexible materials can be capable of unprecedented separation factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Kr/Xe Separation over a Chabazite Zeolite Membrane

Cryogenic distillation, the current conventional technology to separate Krypton and Xenon from air, and from nuclear reprocessing technologies, is an energy-intensive and expensive process. Membrane technology could potentially make this challenging industrial separation less energy intensive and economically viable. We demonstrate that chabazite zeolite SAPO-34 membranes effectively separated Kr/Xe gas mixtures at industrially relevant compositions. Control over membrane thickness and average crystal size led to industrial range permeances and high separation selectivities. Specifically, SAPO-34 membranes can separate Kr/Xe mixtures with Kr permeances as high as 361.4 GPU and separation selectivities of 34.8 for molar compositions close to typical concentrations of these two gases in air. In addition, SAPO-34 membranes separated Kr/Xe mixtures with Kr permeances as high as 525.7 GPU and separation selectivities up to 45.1 for molar compositions as might be encountered in nuclear reprocessing technologies. Molecular sieving and differences in diffusivities were identified as the dominant separation mechanisms.

Feng, Xuhui↗
DOE OSTI2018

Topological Insulators versus Topological Dirac Semimetals in Honeycomb Compounds

Intriguing physical properties of materials stem from their chemical constituents, whereas the connection between them is often not clear. Here, we uncover a general chemical classification for the two quantum phases in the honeycomb ABX structure—topological insulator (TI) and topological Dirac semimetal (TDSM). First, we find among the 816 (existing as well as hypothetical) calculated compounds, 160 TIs (none were noted before), 96 TDSMs, 282 normal insulators (NIs), and 278 metals. Second, based on this classification, we have distilled a simple chemical regularity based on compound formulas for the selectivity between TI and TDSM: the ABX compounds that are TDSM have B atoms (part of the BX honeycomb layers) that come from the periodic table columns XI (Cu, Ag, Au) or XII (Zn, Cd, Hg), or Mg (group II), whereas the ABX compounds whose B at-oms come from columns I (Li, Na, K, Rb, Cs) or II (Ca, Sr, Ba) are TIs. Third, focusing on the ABX bismide compounds that are thermodynamically stable, we find a structural motif that delivers topological insulation and stability at the same time. Furthermore, this study opens the way to simultaneously design new topological materials based on the compositional rules indicated here.

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

Tracking Physicochemical Conditions of Evaporite Deposition by Stable Magnesium Isotopes: A Case Study of Late Permian Langbeinites

Magnesium isotopic compositions of evaporite deposits may record information concerning brine evolution during deposition. We report Mg isotopic values (δ 26 MgDSM3) measured from an evaporite deposit of langbeinite (K 2 Mg 2 (SO 4 ) 3 ) found in the Permian Salado Formation. We used these data to model Mg isotope fractionation between langbeinite and its parent brine. In addition, both measured and theoretical results are used to estimate precipitation temperature and interpret depositional environment. The Salado langbeinite δ 26 Mg values are relatively low and fall within a relatively narrow range (–4.12 ± 0.03‰ to -3.81 ± 0.07‰). Equilibrium fractionation factors between langbeinite and aqueous Mg 2+ solutions were calculated using quantum chemical density functional theory. All computations were performed at the B3LYP/6-31 + G(d,p) level. Solvation effects were addressed using a solvent model (“water-droplet” approach) and mineral structures were investigated using volume variable cluster models (VVCM). The equilibrium Mg isotopic fractionation factors α between langbeinite and model brine solution we obtained are 1.0005, 1.0004, and 1.0003 (Δ 26 Mg langb-water ≈10 3 lnα = 0.473‰, 0.390‰, and 0.322‰) at 10°C, 25°C, and 40°C, respectively. These relatively large equilibrium fractionation factors indicate significant Mg isotope fractionation between langbeinite and its parent brine during precipitation, as langbeinite preferentially incorporates the heavier 26 Mg and 25 Mg isotopes. Rayleigh distillation modeling of the Salado langbeinite's relatively light Mg isotopic composition requires δ 26 Mg DSM3 values of -4‰ for the parent brine. Models favor a precipitation temperature as high as 40°C under equilibrium conditions. Finally, potential disequilibrium precipitation conditions suggested by Mg isotopic data also imply rapid deposition in a hot, arid sedimentary environment prevailing in the southwestern U.S. during the Late Permian.

58 GEOSCIENCES↗
DOE OSTI2018

Model evaluation guidelines for geomagnetic index predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near–Earth space into a single parameter. Most of the best–known indices are calculated from ground–based magnetometer data sets, such as Dst, SYM–H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root mean square error (RMSE) and mean absolute error (MAE) that are applied to a time–series comparison of model output and observations; and (2) event detection performance metrics such as Heidke Skill Score and probability of detection (POD) that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. Furthermore, a few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations

As the climate system warms, megafires have become more frequent with devastating effects. A byproduct of these events is the creation of smoke plumes that can rise into the stratosphere and spread across the globe where they reside for many months. To gain a deeper understanding of the plume dynamics, global climate simulations of a megafire were performed at a wide range of grid spacings from 2.0° down to 7 km, including a 7 km nonhydrostatic experiment. The analysis focuses on how the resolved dynamics affects the specification of the plume characteristics such as injection height and black carbon (BC) mass. Prior studies initialize the smoke plume at one or a few grid points and this is shown here to produce severely dissipative dynamics. In order to validate such simulations with observations, enhancements of the plume characteristics to offset the dissipation is necessary. Using a numerically converged simulation, sensitivity tests show that to approximate the observed stratospheric lifetime, a reduction in BC fraction by 50% is necessary for external mixtures. The vorticity dynamics of the plume is also analyzed with a Lagrangian budget to understand the mechanisms responsible for the evolution of a collocated anticyclonic vortex. The results can be distilled down into a simple conceptual model. As the plume rises, the air diverges at the top of the updraft where the largest concentrations of smoke are found. This divergence induces a dilution of the background cyclonic absolute vorticity producing an anticyclonic vortex. Vortex decay occurs from opposite arguments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scaling High-Resolution Soil Organic Matter Composition to Improve Predictions of Potential Soil Respiration Across the Continental United States

Despite the importance of microbial soil organic matter (SOM) respiration in regulating the flux of carbon between soils and the atmosphere, soil carbon cycling models remain primarily based on climate and soil properties, leading to large uncertainty in predictions. To address this knowledge gap, we analyzed high-resolution water-extractable SOM profiles from soil cores collected across the United States by the 1,000 Soils Pilot of the Molecular Observation Network. Our innovation lies in using machine learning to distill thousands of SOM formula into tractable units; and it enables integrating data from molecular measurements into soil respiration models. In surface soils, SOM chemistry provided better estimates of potential soil respiration than soil physicochemistry, and using them combined yielded the best prediction. Overall, we identify specific subsets of organic molecules that may improve predictions of global soil respiration and create a strong basis for developing new representations in process-based models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗
DOE OSTI2016

Metal–organic framework with optimally selective xenon adsorption and separation

Nuclear energy is considered among the most viable alternatives to our current fossil fuel based energy economy.1 The mass-deployment of nuclear energy as an emissions-free source requires the reprocessing of used nuclear fuel to mitigate the waste.2 One of the major concerns with reprocessing used nuclear fuel is the release of volatile radionuclides such as Xe and Kr. The most mature process for removing these radionuclides is energy- and capital-intensive cryogenic distillation. Alternatively, porous materials such as metal-organic frameworks (MOFs) have demonstrated the ability to selectively adsorb Xe and Kr at ambient conditions.3-8 High-throughput computational screening of large databases of porous materials has identified a calcium-based nanoporous MOF, SBMOF-1, as the most selective for Xe over Kr.9,10 Here, we affirm this prediction and report that SBMOF-1 exhibits by far the highest Xe adsorption capacity and a remarkable Xe/Kr selectivity under relevant nuclear reprocessing conditions. The exceptional selectivity of SBMOF-1 is attributed to its pore size tailored to Xe and its dense wall of atoms that constructs a binding site with a high affinity for Xe, as evident by single crystal X-ray diffraction and molecular simulation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Adsorption separation of heavier isotope gases in subnanometer carbon pores

Isotopes of heavier gases including carbon ( 13 C/ 14 C), nitrogen ( 13 N), and oxygen ( 18 O) are highly important because they can be substituted for naturally occurring atoms without significantly perturbing the biochemical properties of the radiolabelled parent molecules. These labelled molecules are employed in clinical radiopharmaceuticals, in studies of brain disease and as imaging probes for advanced medical imaging techniques such as positron-emission tomography (PET). Established distillation-based isotope gas separation methods have a separation factor ( S ) below 1.05 and incur very high operating costs due to high energy consumption and long processing times, highlighting the need for new separation technologies. Here, we show a rapid and highly selective adsorption-based separation of 18 O 2 from 16 O 2 with S above 60 using nanoporous adsorbents operating near the boiling point of methane (112 K), which is accessible through cryogenic liquefied-natural-gas technology. A collective-nuclear-quantum effect difference between the ordered 18 O 2 and 16 O 2 molecular assemblies confined in subnanometer pores can explain the observed equilibrium separation and is applicable to other isotopic gases.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantum wake dynamics in Heisenberg antiferromagnetic chains

Traditional spectroscopy, by its very nature, characterizes physical system properties in the momentum and frequency domains. However, the most interesting and potentially practically useful quantum many-body effects emerge from local, short-time correlations. Here, using inelastic neutron scattering and methods of integrability, we experimentally observe and theoretically describe a local, coherent, long-lived, quasiperiodically oscillating magnetic state emerging out of the distillation of propagating excitations following a local quantum quench in a Heisenberg antiferromagnetic chain. This “quantum wake” displays similarities to Floquet states, discrete time crystals and nonlinear Luttinger liquids. We also show how this technique reveals the non-commutativity of spin operators, and is thus a model-agnostic measure of a magnetic system’s “quantumness.”

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Complete separation of benzene-cyclohexene-cyclohexane mixtures via temperature-dependent molecular sieving by a flexible chain-like coordination polymer

Abstract The separation and purification of C6 cyclic hydrocarbons (benzene, cyclohexene, cyclohexane) represent a critically important but energy intensive process. Developing adsorptive separation technique to replace thermally driven distillation processes holds great promise to significantly reduce energy consumption. Here we report a flexible one-dimensional coordination polymer as an efficient adsorbent to discriminate ternary C6 cyclic hydrocarbons via an ideal molecular sieving mechanism. The compound undergoes fully reversible structural transformation associated with removal/re-coordination of water molecules and between activated and hydrocarbon-loaded forms. It exhibits distinct temperature- and adsorbate-dependent adsorption behavior which facilitates the complete separation of benzene, cyclohexene and cyclohexane from their binary and ternary mixtures, with the record-high uptake ratios for C 6 H 6 /C 6 H 12 and C 6 H 10 /C 6 H 12 in vapor phase and highest binary and ternary selectivities in liquid phase. In situ infrared spectroscopic analysis and ab initio calculations provide insight into the host-guest interactions and their effect on the preferential adsorption and structural transformation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Manifold learning of four-dimensional scanning transmission electron microscopy

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for straightforward visualization and exploration analysis of 4D-STEM datasets, distilling real-space neighboring effects on atomically resolved deflection patterns from single-layer graphene, with single dopant atoms, as recorded on a pixelated detector. These extracted patterns relate to both individual atom sites and sublattice structures, effectively discriminating single dopant anomalies via multi-mode views. We believe manifold learning analysis will accelerate physics discoveries coupled between data-rich imaging mechanisms and materials such as ferroelectric, topological spin, and van der Waals heterostructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Tracking materials science data lineage to manage millions of materials experiments and analyses

Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

Soedarmadji, Edwin↗