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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tuning the reactivity of carbon surfaces with oxygen-containing functional groups

Oxygen-containing carbons are promising supports and metal-free catalysts for many reactions. However, distinguishing the role of various oxygen functional groups and quantifying and tuning each functionality is still difficult. Here we investigate the role of Brønsted acidic oxygen-containing functional groups by synthesizing a diverse library of materials. By combining acid-catalyzed elimination probe chemistry, comprehensive surface characterizations, 15N isotopically labeled acetonitrile adsorption coupled with magic-angle spinning nuclear magnetic resonance, machine learning, and density-functional theory calculations, we demonstrate that phenolic is the main acid site in gas-phase chemistries and unexpectedly carboxylic groups are much less acidic than phenolic groups in the graphitized mesoporous carbon due to electron density delocalization induced by the aromatic rings of graphitic carbon. The methodology can identify acidic sites in oxygenated carbon materials in solid acid catalyst-driven chemistry.

Zhou, Jiahua↗
DOE OSTI2025

Magnesium‐Mediated Electrochemical Synthesis of Ammonia

Metal-mediated electrochemical synthesis of ammonia (NH3) is a promising method to activate N2 at room temperature. While a Li-mediated approach has been optimized to produce NH3 at high current density and selectivity, Li's scarcity and its highly negative plating potential limit scalability and energy efficiency. Alternative mediators have been proposed, but only Ca has shown some promise, achieving ≈50% Faradaic efficiency (FE), though requiring voltages beyond -3 V. Here, we report a Mg-mediated nitrogen reduction reaction (Mg-NRR), where N2 is activated on Mg to form Mg3N2, followed by protolysis to release NH3 and regenerate Mg. A notable NH3 FE of 25.28 ± 3.80% is achieved at a current density of -45 mA cm-2, corresponding to an NH3 partial current density of -11.30 ± 1.77 mA cm-2 under 6 bar N2. Isotope-labeled experiments confirm that NH3 originates from N2, with similar FE (25.15 ± 1.01%). Importantly, NH3 production is demonstrated at a total cell potential as low as -3 V. This Li-free Mg-NRR system offers key advantages, including lower energy input and use of earth-abundant materials, making it a scalable route for sustainable NH3 synthesis.

Goyal, Ishita↗
DOE OSTI2021

Proton-detected 15 N- 1 H dipolar coupling/ 1 H chemical shift correlation experiment for the measurement of NH distances in biological solids under fast MAS solid-state NMR

Measurement of distances from dipolar couplings is essential for structural characterization, refinement and validation using the solid-state nuclear magnetic resonance (ssNMR) spectroscopy. Particularly, knowledge about NH dipolar interactions in biological solids is important for understanding the hydrogen (H)-bonding interactions, molecular geometry and spin dynamics. In this regard, we have proposed a proton-detected two-dimensional (2D) 15 N- 1 H dipolar coupling/ 1 H chemical shift correlation experiment using the C-symmetry based windowless recoupling of chemical shift anisotropy (ROCSA) in combination with the DIPSHIFT pulse-based method for the measurement of short NH distances in the isotopically labeled and naturally abundant biological solids at fast magic angle spinning (MAS) rates (40–70 kHz). Our proposed method results in undistorted recoupled 15 N- 1 H dipolar coupling powder lineshapes that are free from the recoupled 1 H CSA contributions under the 15 N evolution, a feature that is essential for the measurement of NH distances with improved accuracy (± 500 Hz in terms of the NH dipolar couplings). The pulse sequence developed in the present study is also insensitive to the 1H–1H homonuclear dipolar interactions, relaxation effects owing to its constant-time implementation, and t1-noise from the fluctuations in the MAS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Selective semihydrogenation of acetylene in ethylene using defect-rich boron nitride catalyst from flux reconstruction

Efficient removal of trace acetylene from ethylene streams is essential for producing polymer-grade ethylene, yet achieving highly selective semihydrogenation without over-hydrogenation remains a long-standing challenge. A key barrier is the lack of a simple, low-cost catalyst that can activate hydrogen effectively while preventing ethylene from reacting further. Here we show that defect-rich boron nitride, prepared through a straightforward flux reconstruction method, serves as a highly selective and metal-free catalyst for acetylene semihydrogenation. The catalyst contains abundant open boron and nitrogen sites that enable efficient hydrogen activation and rapid release of ethylene, thereby avoiding over-hydrogenation. Experiments combined with isotope labeling and theoretical analysis reveal that these defects lower the energy barrier for hydrogen activation while accelerating product desorption. Our findings demonstrate a scalable strategy for defect engineering in boron nitride and highlight its potential as a robust, sustainable alternative to metal-based catalysts in industrial ethylene purification.

Wang, Tao [Oak Ridge National Laboratory (ORNL), O↗
DOE OSTI2023

Highly active, ultra-low loading single-atom iron catalysts for catalytic transfer hydrogenation

Highly effective and selective noble metal-free catalysts attract significant attention. Here, a single-atom iron catalyst is fabricated by saturated adsorption of trace iron onto zeolitic imidazolate framework-8 (ZIF-8) followed by pyrolysis. Its performance toward catalytic transfer hydrogenation of furfural is comparable to state-of-the-art catalysts and up to four orders higher than other Fe catalysts. Isotopic labeling experiments demonstrate an intermolecular hydride transfer mechanism. First principles simulations, spectroscopic calculations and experiments, and kinetic correlations reveal that the synthesis creates pyrrolic Fe(II)-plN 3 as the active center whose flexibility manifested by being pulled out of the plane, enabled by defects, is crucial for collocating the reagents and allowing the chemistry to proceed. The catalyst catalyzes chemoselectively several substrates and possesses a unique trait whereby the chemistry is hindered for more acidic substrates than the hydrogen donors. This work paves the way toward noble-metal free single-atom catalysts for important chemical reactions.

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

LCLS RF Station Anomaly Candidates, SLAC, Nov 2020 to Dec 2020, Version 1

The data consists of radio-frequency (RF) station process variables (amplitude and amplitude-mean-out-of-tolerance bit) and beam position monitor (BPM) values for about a month of Linac Coherent Light Source (LCLS) time. The data is all from normal conducting hard X-ray operation. The dataset contains roughly 5000 anomaly “candidates.” For each candidate, we include 3.5 minutes of data from the 82 RF stations and ~20 seconds of data from 4 BPMs. We also include several CSV files with additional information and labels for each candidate.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

High-Brightness Self-seeded X-ray Free Electron Laser to Precisely Map Macromolecular Structure

Two datasets were uploaded. Run numbers #r0101~#r0112 were collected from self-seeded XFEL mode. Run numbers #r0079~#r0088 and #r0090 were collected from SASE mode. The run number is shown in the file name as "ue_191027_SFX_runnumber-c00.cxi.". To understand the effect of self-seeded XFEL source, we used the datasets from run numbers #r0102 to #r0112. Datasets used were randomly chosen. - SS1 is from #r0102~#r0112. - SS2 is from #r0102~#r0105 and #r0109. - SS3 is from #r0104 and #r0105. To compare self-seeded mode and SASE mode, we also used corresponding three SASE datasets labeled as SASE1, SASE2, and SASE3 from run numbers #r0079 to #r0089. Datasets used were randomly chosen. - SASE1 is from #r0079~#r0089. - SASE2 is from #r0080, #r0081, #r0085, #r0087. - SASE3 is composed of #r0079, #r0080.

Lysozyme↗
DOE OSTI2021

FtsZ-mediated fission of a cuboid bacterial symbiont

Less than a handful of cuboid and squared cells have been described in nature, which makes them a rarity. Here, we show how Candidatus Thiosymbion cuboideus, a cube-like gammaproteobacterium, reproduces on the surface of marine free-living nematodes. Immunostaining of symbiont cells with an anti-fimbriae antibody revealed that they are host-polarized, as these appendages exclusively localized at the host-proximal (animal-attached) pole. Moreover, by applying a fluorescently labeled metabolic probe to track new cell wall insertion in vivo, we observed that the host-attached pole started septation before the distal one. Similarly, Ca. T. cuboideus cells immunostained with an anti-FtsZ antibody revealed a proximal-to-distal localization pattern of this tubulin homolog. Although FtsZ has been shown to arrange into squares in synthetically remodeled cuboid cells, here we show that FtsZ may also mediate the division of naturally occurring ones. This implies that, even in natural settings, membrane roundness is not required for FtsZ function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source-receiver observation geometry. Here, we develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.

58 GEOSCIENCES↗
DOE OSTI2024

Coastal bacteria and protists assimilate viral carbon and nitrogen

Abstract Free viruses are the most abundant type of biological particles in the biosphere, but the lack of quantitative knowledge about their consumption by heterotrophic protists and bacterial degradation has hindered the inclusion of virovory in biogeochemical models. Using isotope-labeled viruses added to three independent microcosm experiments with natural microbial communities followed by isotope measurements with single-cell resolution and flow cytometry, we quantified the flux of viral C and N into virovorous protists and bacteria and compared the loss of viruses due to abiotic vs biotic factors. We found that some protists can obtain most of their C and N requirements from viral particles and that viral C and N get incorporated into bacterial biomass. We found that bacteria and protists were responsible for increasing the daily removal rate of viruses by 33% to 85%, respectively, compared to abiotic processes alone. Our laboratory incubation experiments showed that abiotic processes removed roughly 50% of the viruses within a week, and adding biotic processes led to a removal of 83% to 91%. Our data provide direct evidence for the transfer of viral C and N back into the microbial loop through protist grazing and bacterial breakdown, representing a globally significant flux that needs to be investigated further to better understand and predictably model the C and N cycles of the hydrosphere.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗
DOE OSTI2021

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Monomer-dimer tensor-network basis for qubit-regularized lattice gauge theories

Traditional SU⁡(𝑁) lattice gauge theories (LGTs) can be formulated using an orthonormal basis constructed from the irreducible representations (irreps) 𝑉 𝜆 of the SU⁡(𝑁) gauge symmetry. On a lattice, the elements of this basis are tensor networks comprising dimer tensors on the links labeled by a set of irreps {𝜆 ℓ } and monomer tensors on sites labeled by {𝜆 𝑠 }. These tensors naturally define a local site Hilbert space, ℋ$^𝑔_𝑠$, on which gauge transformations act. Gauss’s law introduces an additional index 𝛼 𝑠 =1,2,…,𝒟⁡(ℋ$^𝑔_𝑠$) that labels an orthonormal basis of the gauge-invariant subspace of ℋ$^𝑔_𝑠$. This monomer-dimer tensor-network (MDTN) basis, |{𝜆 𝑠 },{𝜆 ℓ },{𝛼 𝑠 }⟩, of the physical Hilbert space enables the construction of new qubit-regularized SU⁡(𝑁) gauge theories that are free of sign problems while preserving key features of traditional LGTs. Here, we investigate finite-temperature confinement-deconfinement transitions in a simple qubit-regularized SU(2) and SU(3) gauge theory in 𝑑 =2 and 𝑑 =3 spatial dimensions, formulated using the MDTN basis, and show that they reproduce the universal results of traditional LGTs at these transitions. Additionally, in 𝑑 =1, we demonstrate using a plaquette chain that the string tension at zero temperature can be continuously tuned to zero by adjusting a model parameter that plays the role of the gauge coupling in traditional LGTs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Skopi: a simulation package for diffractive imaging of noncrystalline biomolecules

X-ray free-electron lasers (XFELs) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL experiments involving aerosolized or solvated biomolecular samples offer new challenges in terms of both experimental design and data processing. Skopi is a simulation package that can generate single-hit diffraction images for reconstruction algorithms, multi-hit diffraction images of aggregated particles for training machine learning classifiers using labeled data, diffraction images of randomly distributed particles for fluctuation X-ray scattering algorithms, and diffraction images of reference and target particles for holographic reconstruction algorithms. Skopi is a resource to aid feasibility studies and advance the development of algorithms for noncrystalline experiments at XFEL facilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Influence of root- vs needle-derived litter on soil C and N stabilization in a California coniferous forest from 2001 to 2011

How plant inputs from above- versus below-ground affect long term carbon (C) and nitrogen (N) retention and stabilization in soils is not well known. We present results of a decade-long field study that traced the decomposition of 13C- and 15N-labeled Pinus ponderosa needle and fine root litter placed in O or A soil horizons of a sandy Alfisol under a coniferous forest. We measured the retention of litter-derived C and N in particulate (>2 mm) and bulk soil (<2 mm) fractions, as well as in density-separated free light and three mineral-associated fractions. After 10 years, the influence of slower initial mineralization of root litter compared to needle litter was still evident: almost twice as much root litter (44% of C) was retained than needle litter (22–28% of C). After 10 years, the O horizon retained more litter in coarse particulate matter implying the crucial comminution step was slower than in the A horizon, while the A horizon retained more litter in the finer bulk soil, where it was recovered in organo-mineral associations. Retention in these A horizon mineral-associated fractions was similar for roots and needles. Nearly 5% of the applied litter C (and almost 15% of the applied N) was in organo-mineral associations, which had centennial residence times and potential for long-term stabilization. Vertical movement of litter-derived C was minimal after a decade, but N was significantly more mobile. Overall, the legacy of initial litter quality influences total SOM retention; however, the potential for and mechanisms of long-term SOM stabilization are influenced not by litter type but by soil horizon.This dataset comprises (1) litter recovery for each of 5 timepoints in >2mm, <2mm, and density fractions, (2) density fractionation results for the 10-year timepoint, and (3) vertical movement of applied litter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Engineering Forisome Scaffolds: Elucidating Spatial Self-Assembly Patterning of Bio-inorganic Complexes

Organisms can synthesize biomaterials incorporating an array of naturally occurring elements while overcoming challenges and insults. Although, it is known that most cellular biomaterials are synthesized in specialized cellular compartments, there are knowledge gaps about how organic/inorganic biomaterial synthesis is orchestrated inside cells. In addition, there is great potential in understanding how individual monomers can self-assembly into organized patterns to form responsive biomaterials. Forisomes are a natural responsive biomaterial found in legume plants that serve as a plug sieve element in the plant phloem that undergo anisotropic conformational changes by rapid (<1 s) ATP-independent from condensed spindle to plug-like form, triggered by the influx of Ca 2+ . Addressing principles of forisome synthesis and assembly will determine how biomaterials containing inorganic elements self-assemble and conduct chemical modification to produce biomaterials or undergo biomineralization. We employ transcription and translation (TXTL) using cell-free expression systems for forisome monomer expression, self-assembly, and pattern probing. We conducted experiments to precisely control forisome proteins synthesis of various monomers SEO1, SEO2, SEO3, and SEO4 to explore self- assembly. We demonstrate forisome self-assembly of the SEO monomers is possible and indicate unique monomer fluorescent labeling patterns that require additional analysis. We investigated locations and linkers for adding tetracysteine tag fluorophore probes to determine impacts of self-assembly and anisotropic conformational changes.

42 ENGINEERING↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2024

Warm boundary-layer cloud properties and cloud susceptibilities product at the ARM ENA observatory

Cloud liquid water path, albedo, and cloud fraction susceptibilities for marine warm boundary-layer clouds are quantified using the Meteosat retrieved cloud microphysical properties at the ARM Eastern North Atlantic (ENA) observatory in the Azores. The Meteosat cloud retrievals are based on the methods applied by the Clouds and the Earth’s Radiant Energy System (CERES) project, which are specifically tailored to support the ARM user facility over the ARM ground-based observation sites (enavisstpx2dm11minnisX1.c1). Cloud susceptibility is quantified as the slope between cloud properties and Nd within a 1-degree-by-1-degree grid box at each satellite time step using a least-square regression. To reduce uncertainties associated with the adiabatic assumption in the Nd retrieval, for each cloud object, we exclude cloudy pixels at the cloud edge, defined as those adjacent to cloud-free pixels. To minimize influence of cloud heterogeneity and co-variability on the retrieved cloud susceptibility, cloud properties are averaged to 0.25-degree resolution using the 3-km pixel-level retrieval, and the 1-degree cloud susceptibilities are estimated using the smoothed data. Cloud type classification is applied to distinct cloud object, labeling all contiguous cloudy pixels. Marine warm boundary-layer clouds are defined as clouds with 90% of cloud tops below 3km, and Meteosat retrieved cloud phase as liquid.

54 ENVIRONMENTAL SCIENCES↗