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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI.

greenhouse ga emmission↗
DOE OSTI2025

Deciphering the local structure of Prussian blue analogue cathodes with Raman spectroscopy for sodium-ion batteries

Iron-based Prussian blue analogues (PBAs) have gained attention as low-cost, relatively higher-potential cathodes for sodium-ion batteries, due to their open 3D-framework structures. However, understanding the local structural changes is critical to unveiling the intercalation pathways and degradation mechanisms. We employ here operando Raman spectroscopy to probe the changes in the cyanide vibrational modes during cycling and degradation after cycling, which are not adequately resolved by X-ray diffraction due to the low structure factor and limited X-ray sensitivity of the cyanide groups. Vibrational spectroscopy has thus proven essential for deciphering these complex materials. Additionally, we implement pre-sodiation strategies to assess the impact of sodium inventory loss by pairing PBAs with a hard carbon anode in a full-cell configuration. Operando galvanostatic electrochemical impedance spectroscopy (EIS) and ex situ X-ray photoelectron spectroscopy (XPS) further elucidate the interface evolution and the role of water molecules in forming the cathode-electrolyte interphase (CEI) and solid-electrolyte interphase (SEI). The insights gained advance the understanding of PBAs and enhance their practical viability.

25 ENERGY STORAGE↗
DOE OSTI2026

Correlating Protein Dynamics and Catalytic Activity of a Model Hydrogenase Using Paramagnetic and Biological Nuclear Magnetic Resonance Spectroscopy

Rational catalyst design remains a significant challenge, with electronic structure, steric, and electrostatic effects known to contribute to activity. Recently, dynamics has been recognized as another factor that impacts catalysis, though identifying and predicting these effects has remained out of reach. Nickel-substituted rubredoxin (NiRd), a protein-based mimic of a hydrogenase enzyme, serves as a model catalytic system in which dynamics can be systematically investigated with respect to activity. While over 30 secondary-sphere mutants of NiRd have been shown to be catalytically active, no significant correlation was observed between the rates and catalytic overpotential or electronic structure, prompting questions about the protein-derived factors that modulate activity. Here, in this work, NMR spectroscopy was used to investigate the roles of substrate accessibility, protein dynamics, and protein stability in controlling catalysis. Significant paramagnetic effects from the nickel center (S = 1) isolate the methylene proton resonances of the metal-coordinating cysteine residues. The sensitivity of resonance positions and linewidths to local environment offers an opportunity to study dynamical molecular changes around the metal center with high resolution. Machine learning algorithms were employed to identify correlations between the catalytic activity and the paramagnetic NMR spectra. These analyses revealed spectroscopic features of specific cysteine protons that report on catalytic overpotential and increased turnover rates, which are further supported by the results obtained using high-field NMR techniques. Collectively, these studies indicate the potential for multifrequency NMR techniques to resolve key contributors to catalytic activity and highlight the importance of local and outer-sphere dynamics.

Protein Engineering↗
DOE OSTI2025

Impact of Halogen Groups on the Properties of PEA–Based 2D Pb–Sn Halide Perovskites

Tuning broad emission in 2D Pb–Sn halide perovskites (HPs) is essential for advancing optoelectronic applications, particularly for color-tunable and white-light-emitting devices. This broad emission is linked to structural factors, such as defects and phase segregation of the Pb component within the Pb–Sn system, which are strongly influenced by the molecular structure and chemical properties of spacer cations. Atomic tuning of the spacers via halogenation opens up a new way to fine-tune the molecular properties, enabling further augmentations of HP functionalities. Nevertheless, the distinct broad emission's sensitivity to spacer chemistry remains underexplored. Here, halogenation's influence is systematically investigated on 2D HP emission characteristics using a high-throughput workflow. These findings reveal that the F-containing phenethylammonium (4F-PEA) spacer narrows the broadband PL, whereas Cl broadens it. Through a correlative study, it is found that 4F-PEA reduces not only the local phase segregation but also the defect levels and microstrains in 2D HPs. This is likely attributed to the manifestation of less lattice distortion via stronger surface coordination of the dipole-augmented 4F-PEA. Furthermore, these results highlight halogenation as a key factor in modulating phase segregation and defect density in 2D Pb–Sn HPs, offering a promising pathway to tune the emission for enhanced optoelectronic performance.

2D Pb-Sn halide perovskites↗
DOE OSTI2023

Ferroelectricity in hafnia controlled via surface electrochemical state

Ferroelectricity in binary oxides including hafnia and zirconia has riveted the attention of the scientific community due to the highly unconventional physical mechanisms and the potential for the integration of these materials into semiconductor workflows. Over the last decade, it has been argued that behaviours such as wake-up phenomena and an extreme sensitivity to electrode and processing conditions suggest that ferroelectricity in these materials is strongly influenced by other factors, including electrochemical boundary conditions and strain. Here we argue that the properties of these materials emerge due to the interplay between the bulk competition between ferroelectric and structural instabilities, similar to that in classical antiferroelectrics, coupled with non-local screening mediated by the finite density of states at surfaces and internal interfaces. Via the decoupling of electrochemical and electrostatic controls, realized via environmental and ultra-high vacuum piezoresponse force microscopy, we show that these materials demonstrate a rich spectrum of ferroic behaviours including partial-pressure-induced and temperature-induced transitions between ferroelectric and antiferroelectric behaviours. In conclusion, these behaviours are consistent with an antiferroionic model and suggest strategies for hafnia-based device optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Magnetic field sensors for detection of trapped flux in superconducting radio frequency cavities

Superconducting radio-frequency (SRF) cavities are fundamental building block of modern particle accelerators. They operate at liquid helium temperatures 2-4 K to achieve very high quality factors 1e10-1e11. Trapping of magnetic flux within the superconductor is a significant contribution to the residual RF losses which limit the achievable quality factor. Suitable diagnostic tools are in high demand to understand the mechanisms of flux trapping in technical superconductors and the fundamental components of such diagnostic tool are magnetic field sensors. We have studied the performance of commercially available Hall probes, anisotropic magnetoresistive sensors and flux-gate magnetometers with respect to their sensitivity and capability to detect localized, low magnetic flux amplitudes, of the order of few tens of magnetic flux quantum at liquid helium temperature. Although Hall probes have the lowest magnetic field sensitivity (~96$ nV$/microT at 2 K), their physical dimensions are such that they have the ability to detect the lowest number of trapped vortices among the three type of sensors. Hall probes and anisotropic magnetoresistive sensors have been selected to be used in a setup to map regions of trapped flux on the surface of a single-cell SRF cavity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Effects of negative triangularity on microinstabilities in a low-recycling lithium-wall spherical tokamak

In this work, we present a linear gyrokinetic study of the impact of negative triangularity (NT) on microinstabilities in the Lithium Tokamak eXperiment-β (LTX-β), a low-recycling spherical tokamak with liquid lithium plasma-facing components that produce flat electron-temperature profiles [Elliott et al., IEEE Trans. Plasma Sci. 48, 1382 (2020)]. While NT is widely recognized as a stabilizing mechanism and often associated with improved confinement in conventional tokamaks, this study reveals that its effect is not universally stabilizing in the parameter regime of LTX-β and is shown to be highly sensitive to local equilibria. Using local linear simulations with the GS2 code [Kotschenreuther et al., Comput. Phys. Commun. 88, 128 (1995)] at ρ=0.3, 0.5, and 0.8 for two representative discharges (#103955 and #109355), and employing the Miller equilibrium model to isolate shaping effects, we find that NT can transition from stabilizing to destabilizing depending on magnetic shear, safety factor, and electron-temperature gradient. In shot #103955, NT reduces growth rates across radii, with strongest stabilization at the edge, whereas in shot #109355, it is stabilizing in the core but destabilizing at mid-radius and edge under experimental conditions. Parametric scans show that flattening the electron-temperature profile, increasing magnetic shear, and reducing the safety factor recover NT stabilization. These results demonstrate that NT stabilization is tunable rather than intrinsic and requires coordinated control of magnetic geometry and gradient drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗
DOE OSTI2021

Environment-sensitivity functions for gross primary productivity in light use efficiency models

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Thioredoxin VdTrx1, an unconventional secreted protein, is a virulence factor in Verticillium dahliae

Understanding how plant pathogenic fungi adapt to their hosts is of critical importance to securing optimal crop productivity. In response to pathogenic attack, plants produce reactive oxygen species (ROS) as part of a multipronged defense response. Pathogens, in turn, have evolved ROS scavenging mechanisms to undermine host defense. Thioredoxins (Trx) are highly conserved oxidoreductase enzymes with a dithiol-disulfide active site, and function as antioxidants to protect cells against free radicals, such as ROS. However, the roles of thioredoxins in Verticillium dahliae , an important vascular pathogen, are not clear. Through proteomics analyses, we identified a putative thioredoxin (VdTrx1) lacking a signal peptide. VdTrx1 was present in the exoproteome of V. dahliae cultured in the presence of host tissues, a finding that suggested that it plays a role in host-pathogen interactions. We constructed a VdTrx1 deletion mutant Δ VdTrx1 that exhibited significantly higher sensitivity to ROS stress, H 2 O 2 , and tert -butyl hydroperoxide ( t -BOOH). In vivo assays by live-cell imaging and in vitro assays by western blotting revealed that while VdTrx1 lacking the signal peptide can be localized within V. dahliae cells, VdTrx1 can also be secreted unconventionally depending on VdVps36, a member of the ESCRT-II protein complex. The Δ VdTrx1 strain was unable to scavenge host-generated extracellular ROS fully during host invasion. Deletion of VdTrx1 resulted in higher intracellular ROS levels of V. dahliae mycelium, displayed impaired conidial production, and showed significantly reduced virulence on Gossypium hirsutum , and model plants, Arabidopsis thaliana and Nicotiana benthamiana . Thus, we conclude that VdTrx1 acts as a virulence factor in V. dahliae .

Tian, Li↗
DOE OSTI2021

Influence of elongation and triangularity on plasma response to resonant magnetic perturbations

Abstract Systematic toroidal modeling of the plasma response to the n = 1–4 ( n is the toroidal mode number) resonant magnetic perturbation (RMP) field is carried out in order to understand the plasma-shaping effect on controlling the type-I edge-localized modes (ELMs) in tokamak experiments. Considered are large variations of the plasma elongation and triangularity at a fixed edge safety factor q a , for limiter plasmas with both single-null (SN) and double-null (DN) divertor-like boundary shapes. Numerical results assuming conformal 3D RMP coils show that (i) the optimum coil phasing between the upper and lower rows for ELM control becomes increasingly sensitive to the plasma elongation with higher- n toroidal spectra, (ii) the optimum coil phasing is however essentially independent of the plasma triangularity for all n = 1–4 RMP fields, (iii) with the same coil current and the optimum coil phasing, high elongation generally favors ELM control but it may be more challenging for plasmas with intermediate elongation and with lower- n ( n = 1–2) RMPs, and (iv) higher triangularity is generally always better for ELM control with all n = 1–4 fields for both DN and SN divertor-like plasma boundary shapes.

Physics↗
DOE OSTI2024

Characterizing gravitational wave detector networks: from A # to cosmic explorer

Gravitational-wave observations by the laser interferometer gravitational-wave observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A # , given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped cosmic explorer (CE) observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein telescope with 10 km arms in Europe. We use a set of science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A # observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy (MMA) and precision measurement of the Hubble parameter. Two CE observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming MMA. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two CE observatories is critical for accomplishing all the identified science metrics including the nuclear equation of state, cosmological parameters, the growth of black holes through cosmic history, but also make new discoveries such as the presence of dark matter within or around neutron stars and black holes, continuous gravitational waves from rotating neutron stars, transient signals from supernovae, and the production of stellar-mass black holes in the early Universe. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A # observatories.

Einstein telescope↗
DOE OSTI2024

Strongly coupled edge states in a graphene quantum Hall interferometer

Electronic interferometers using the chiral, one-dimensional (1D) edge channels of the quantum Hall effect (QHE) can demonstrate a wealth of fundamental phenomena. The recent observation of phase jumps in a Fabry-Pérot (FP) interferometer revealed anyonic quasiparticle exchange statistics in the fractional QHE. When multiple integer edge channels are involved, FP interferometers have exhibited anomalous Aharonov-Bohm (AB) interference frequency doubling, suggesting putative pairing of electrons into 2e quasiparticles. Here, we use a highly tunable graphene-based QHE FP interferometer to observe the connection between interference phase jumps and AB frequency doubling, unveiling how strong repulsive interaction between edge channels leads to the apparent pairing phenomena. By tuning electron density in-situ from filling factor v < 2 to v > 7, we tune the interaction strength and observe periodic interference phase jumps leading to AB frequency doubling. Our observations demonstrate that the combination of repulsive interaction between the spin-split v = 2 edge channels and charge quantization is sufficient to explain the frequency doubling, through a near-perfect charge screening between the localized and extended edge channels. Our results show that interferometers are sensitive probes of microscopic interactions and enable future experiments studying correlated electrons in 1D channels using density-tunable graphene.

42 ENGINEERING↗
DOE OSTI2022

Inferring small-sample-reactivity worth from oscillation experiment with auto-corrected local flux

To estimate reactivity changes as a function of time from the signal of detectors count rates, one usually uses point kinetic equations. However, when detectors are located inside the core, it is often necessary to compute a correction factor to account for the flux-shape deformation (i.e. the change of detector efficiency that may have been induced by neutron absorbers movements). Various simulation methods have been developed depending on transients observed. They range from the resolution of static equations up to full-time-dependent neutron transport in 3D. However, those methods require high computational-cost calculations of local reaction rates that rely on nuclear data evaluations. Therefore, a 'measured' reactivity is never determined through pure measurements. Taking advantage of the known oscillation movement of a small sample at the center of the MINERVE reactor, the innovative algorithm presented in this paper derives the correction factor needed to get no time drift on reactivity steps. Derived correction factors are in good agreement with those obtained independently with TRIPOLI-4 through sensitivity analysis. Low sensitivity to the sample composition and moderate sensitivity to the detectors position reinforce the reliability of those results. This application paves the way to almost pure nuclear data reactivity measurements. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Modeling Diurnal and Annual Ethylene Generation from Solar-Driven Electrochemical CO 2 Reduction Devices

Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards achieving net-negative carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Here, using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimental CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. We simulated diurnal product outputs with and without the effects of ambient temperature to determine gaseous product temperature sensitivity. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne/yr at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.

Yap, Kyra M. K.↗
DOE OSTI2026

Mass radius and D-term of atomic nuclei in relativistic mean field theory

Based on relativistic mean field theory for atomic nuclei, we compute the mass radius and other radii associated with the energy momentum tensor for dozens of spin-0 nuclei across the nuclear chart. We also compute the D-term of these nuclei, the forward limit of the gravitational form factor 𝐷⁡(𝑡=0)=𝐷. The dependence on the neutron number 𝑁 is systematically studied for calcium (Ca), nickel (Ni), zirconium (Zr), tin (Sn), and lead (Pb) isotopes. Remarkably, |𝐷| does not monotonically increase with 𝑁. Instead, it exhibits local maxima and minima when 𝑁 equals a magic number and even a submagic number. This results in characteristic kinks in the mass, scalar, tensor, and shear radii of these isotopes. Our work for the first time elucidates the strong sensitivity of the various mechanical properties of nuclei to the nuclear shell structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Improving Predictions of Spin-Crossover Complex Properties through DFT Calculations with a Local Hybrid Functional

We conducted a study on the performance of the local hybrid exchange-correlation functional PBE0r for a set of 95 experimentally-characterized iron spin crossover (SCO) complexes. The PBE0r functional is a variant of PBE0 where the exchange correction is restricted to on-site terms formulated within the basis of local orbitals. We determine the free parameters of the PBE0r functional against experimental data and other hybrid functionals. With a Hartree-Fock (HF) exchange factor of 4%, the PBE0r functional accurately reproduces the electronic and free energy trends predicted in prior DFT studies for these 95 complexes using the B3LYP functional. Larger values of HF exchange stabilize high-spin states. The PBE0r-predicted bond lengths tend to exceed the experimental bond lengths, and bond lengths are less sensitive to HF exchange. The predicted SCO transition temperatures T 1/2 from PBE0r correlate moderately with the experimental transition temperatures, showing a slight improvement compared to the previous modB3LYP-predicted T 1/2 . Furthermore, this study suggests the PBE0r functional as computationally cost-effective and offers the possibility of simulating larger complexes with accuracy comparable to other global hybrid functionals, provided the HF exchange parameter is carefully optimized.

25 ENERGY STORAGE↗
DOE OSTI2024

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗