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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Freeze It or Leave It? Evaluating the Role of Cryo-Electron Microscopy in Battery Research

Cryogenic electron microscopy (cryo-EM) continues to gain prominence in materials science, particularly in battery research where it has enabled high-resolution, multimodal characterization of electrode materials and interfaces that otherwise degrade quickly under electron beam irradiation. But as anyone who has attempted cryo-EM techniques knows, freezing comes at a cost; cryo-EM experiments are time-consuming, highly sensitive, and carry an increased risk of artifacts due to issues such as frost contamination. Thus, when planning new characterization of battery materials or other beam-sensitive samples, it is critical to consider whether (and which) cryo-EM techniques are appropriate, based on study goals and an understanding of electron beam-sample interactions. Here we review such considerations for battery materials to elucidate the questions of when, why, and how to freeze to achieve high-quality characterization.

25 ENERGY STORAGE↗
DOE OSTI2021

LANL-UW Collaboration on LC-QTOF Datasets (Annual Report)

Prior to the application of non-targeted chemometric techniques (i.e., Fisher ratio analysis), pre-processing steps must be carried out on the data collected using liquid chromatography-quadrupole time of flight (LC-QTOF) mass spectrometry. Data compression is the most important pre-processing step due to the vast number of high-resolution mass channels (m/z; 0.0001 Da) collected. Our first approach was to bin the mass spectral dimension to 0.01 Da; however, this approach was computationally expensive and did not preserve the high-resolution data.

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

The 1996 Mid-Atlantic Winter Flood: Exploring Climate Risk through a Storyline Approach

Abstract This article explores the application of thermodynamic perturbations to a historical midlatitude, wintertime, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14-km variable-resolution simulations completed with the U.S. Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show that the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under preindustrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks from around +1 to +2 K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential nonlinearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Are All Post-starbursts Mergers? HST Reveals Hidden Disturbances in the Majority of PSBs

How do galaxies transform from blue, star-forming spirals to red, quiescent early-type galaxies? To answer this question, we analyzed a set of 26 gas-rich, shocked post-starburst galaxies with Hubble Space Telescope (HST) imaging in $B$, $I$, and $H$ bands and Sloan Digital Sky Survey (SDSS) $i$-band imaging of similar depth but lower resolution. We found that post-starbursts in our sample have intermediate morphologies between disk- and bulge dominated (Sérsic $n$ = ${1.7}_{-0.0}^{+0.3}$ ) and have red bulges, likely due to dust obscuration in the cores. A majority of galaxies in our sample are more morphologically disturbed than regular galaxies (88%, corresponding to >3$σ$ significance) when observed with HST, with asymmetry and Sérsic residual flux fraction being the most successful measures of disturbance. Most disturbances are undetected at the lower resolution of SDSS imaging. Although ~27% galaxies are clear merger remnants, we found that disturbances in another ~30% of the sample are internal, caused by small-scale perturbations or dust substructures rather than tidal features, and require high-resolution imaging to detect. We found 2.8$σ$ evidence that asymmetry features fade on timescales ~200 Myr, and may vanish entirely after ~750 Myr, so we do not rule out a possible merger origin of all post-starbursts given that asymmetric features may have already faded. This work highlights the importance of small-scale disturbances, detected only in high-resolution imaging, in understanding structural evolution of transitioning galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
DOE OSTI2021

Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

58 GEOSCIENCES↗
DOE OSTI2026

Magnetic flux emergence in binary neutron star remnants

Using high-resolution AthenaK simulations of a twisted toroidal flux tube, we study the flux emergence of magnetic structures in the shear layer of a hot massive neutron star typical of a binary neutron star remnant. High-resolution simulations demonstrate that magnetic buoyant instabilities allow for emergence only for extremely large magnetic fields significantly exceeding 10 17 G, and more typical fields around 10 16 G are instead dominated by hydrodynamic effects. Because merger remnants tend to be stable against hydrodynamic convection, our work places strong limitations on the mechanisms by which massive binary neutron star remnants can produce the magnetically-driven outflows needed to power jets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .

2D class averages↗
DOE OSTI2022

Assessing the Sensitivity of the Tropical Cyclone Boundary Layer to the Parameterization of Momentum Flux in the Community Earth System Model

Recent studies have demonstrated that high-resolution (~25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. Importantly, we demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Intergranular corrosion of Ni-30Cr in high-temperature hydrogenated water after removing surface passivating film

Abstract High-resolution transmission electron microscopy and atom probe tomography are used to characterize the initial passivation and subsequent intergranular corrosion of degraded grain boundaries in a model Ni-30Cr alloy exposed to 360 °C hydrogenated water. Upon initial exposure for 1000 h, the alloy surface directly above the grain boundary forms a thin passivating film of Cr 2 O 3 , protecting the underlying grain boundary from intergranular corrosion. However, the metal grain boundary experiences severe Cr depletion and grain boundary migration during this initial exposure. To understand how Cr depletion affects further corrosion, the local protective film was sputtered away using a glancing angle focused ion beam. Upon further exposure, the surface fails to repassivate, and intergranular corrosion is observed through the Cr-depleted region. Through this combination of high-resolution microscopy and localized passive film removal, we show that, although high-Cr alloys are resistant to intergranular attack and stress corrosion cracking, degradation-induced changes in the underlying metal at grain boundaries make the material more susceptible once the initial passive film is breached.

(S)TEM↗
DOE OSTI2024

GENESIS: Gamma Energy Neutron Energy Spectrometer for Inelastic Scattering

Improved neutron inelastic scattering cross section data are needed to inform integral benchmark studies and advance applications in a wide variety of areas including nuclear energy, stockpile stewardship, nonproliferation, and space exploration. Neutron inelastic scattering also serves as a non-selective probe of low-lying nuclear structure. To help meet these needs, the Gamma Energy Neutron Energy Spectrometer for Inelastic Scattering (GENESIS) was constructed at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. This array couples high-resolution γ-ray detectors and fast neutron detectors to achieve single and coincident n/γ detection over a broad energy range. The current configuration of the array includes 26 organic liquid scintillators and four high-purity germanium detectors (two single-crystal and two four-crystal CLOVER detectors with two-fold segmentation). The array was constructed with minimal supporting material and designed to cover a wide range of secondary particle angles and energies with limited inter-element scattering. Data acquisition is accomplished using Mesytec MDPP-16 multi-channel high-resolution digital pulse processing modules. The array characteristics, including γ-ray and neutron energy resolution, timing resolution, and detection efficiency were measured and used to validate a Geant4 model of the array. Furthermore, the primary sources of neutron background and the uncertainties in the determination of incident and secondary neutron energy were assessed. GENESIS provides a new capability to address nuclear data needs and facilitates the advancement of a wide range of nuclear applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Preliminary Results from ADRIANO2 Test Beams

A novel high-granularity, dual-readout calorimetric technique (ADRIANO2) is under development as part of the research program of T1604 Collaboration. (Talk Presented at the 19th International Conference on Calorimetry in Particle Physics (CALOR 2022), University of Sussex, Sussex, UK, 16–20 May 2022). The building block of such a calorimeter consists of a pair of optically isolated, small size tiles made of scintillating plastic and lead glass. The prompt Čerenkov light from the glass can be exploited to perform high resolution timing measurements, while the high granularity provides good resolution of the spatial components of the shower. Dual-readout compensation and particle flow techniques can be applied simultaneously to the scintillation and to the Čerenkov section, providing excellent energy resolution as well as PID particle identification. These characteristics make ADRIANO2 a 6-D detector, suited for High Energy as well as High Intensity experiments. A report on the status of the ADRIANO2 project, preliminary measurements of light yield, and current and future R&D plans by T1604 Collaboration are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Contributions to the BeEST sterile neutrino search

Sterile neutrinos are hypothetical right-handed leptons that have been postulated as extensions to the Standard Model to help explain the origin of neutrino masses and the matter-antimatter asymmetry in the Universe. Sterile neutrinos with keV-scale masses are also a promising candidate for dark matter. LLNL and the Colorado School of Mines are developing an experiment to search for sterile neutrinos by measuring the \Beryllium-7 Electron capture in Superconducting Tunnel junctions (STJs)" (the \BeEST" Experiment). When 7 Be implanted into STJ radiation detectors decays by electron capture, the neutrino escapes and the energy of the recoiling 7 Li nucleus can be measured with high accuracy. Using conservation of energy and momentum, the mass of the emitted neutrino can then be found. Sterile keV neutrinos would generate additional peaks in the spectrum, and the experiment uses high-resolution STJs to observe such a potential signal. During my internship, I contributed to different aspects of the BeEST experiment. I designed a UHV setup for X-ray spectroscopy analysis with STJs. It will allow high-resolution X-ray spectroscopy of different materials to examine the broadening in the BeEST spectra, the origins of which are unknown but may be related to material effects inside the STJ detectors. The design accounts for the specific demands of the BeEST project, but provides a degree of flexibility that makes it useful for other potential STJ projects. Additionally, to test the accuracy of the electronics and data analysis routine, an arbitrary waveform generator (AWG) will be used to generate simulated spectra that contain sterile neutrino signals of a specified mass and admixture. For this I programmed an AWG to output random STJ signals whose amplitudes follow the appropriate user-de ned probability distribution. The AWG signals can be input into the 32-channel preamplifier to simulate any spectrum of interest and test the uniformity of the amplifier chain, as well as the sensitivity of the data analysis.

42 ENGINEERING↗
DOE OSTI2021

Herbig Ae/Be Stars toward the Dark Cloud LDN 1667

We report the discovery of a new emission-line object, named SPH 4-South = (GAIA EDR3 5616553300192230272), toward the dark cloud LDN 1667. This object came to our attention after inspecting public images that show a faint diffuse nebula a few arcseconds south of SPH 4, an emission-line object previously classified as a T Tauri star. We present high-resolution spectra and analyzed JHK photometry of SPH 4 and SPH 4-South and new narrowband and archival broadband images of these objects. A comparison of the spectra of SPH 4 and SPH 4-South with high-resolution ones of DG Cir and R Mon strongly suggests that SPH 4 and SPH 4-South are Herbig Ae/Be stars. The classification of SPH 4-South is further supported by using a k-NN algorithm to its position in an H–K versus J–H color–color diagram. Both stars are detected in the four WISE bands and the WISE colors allow us to classify SPH 4 as a Class I and SPH 4-South as a Class II source. We also show that the faint nebula is most probably associated with SPH 4-South. Using published results on LDN 1667 and the Gaia Early Data Release 3, we conclude that SPH 4 is a member of LDN 1667. The case of SPH 4-South is not clear because the determination of its distance and proper motion could be affected by the nebulosity around the star, although membership of SPH 4-South to LDN 1667 cannot be ruled out.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth system models (ESMs) and ultra-high-resolution urban climate modeling, over large domains. Here, we present U-Surf, a first-of-its-kind 1 km resolution present-day (circa 2020) global continuous urban surface parameter dataset. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for satisfying dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet and canopy level. Generated using a systematically unified workflow, U-Surf ensures internal consistency among key parameters, making it the first globally coherent urban canopy surface dataset. U-Surf significantly improves the representation of the urban land heterogeneity both within and across cities globally; provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs; enables detailed city-to-city comparisons across the globe; and supports next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf can also be used as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to advance the research frontier of urban system science, climate-sensitive urban design, and coupled human–Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Cheng, Yifan [Univ. of Illinois at Urbana-Champaig↗
DOE OSTI2022

Identification of a quasi-liquid phase at solid–liquid interface

Abstract An understanding of solid–liquid interfaces is of great importance for fundamental research as well as industrial applications. However, it has been very challenging to directly image solid–liquid interfaces with high resolution, thus their structure and properties are often unknown. Here, we report a quasi-liquid phase between metal (In, Sn) nanoparticle surfaces and an aqueous solution observed using liquid cell transmission electron microscopy. Our real-time high-resolution imaging reveals a thin layer of liquid-like materials at the interfaces with the frequent appearance of small In nanoclusters. Such a quasi-liquid phase serves as an intermediate for the mass transport from the metal nanoparticle to the liquid. Density functional theory-molecular dynamics simulations demonstrate that the positive charges of In ions greatly contribute to the stabilization of the quasi-liquid phase on the metal surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗