Search NASA⌕ Search

SEARCH · Search NASA

Results for “cover complexity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Wide‐Field Bond Quality Evaluation Using Frequency Domain Thermoreflectance with Deep Neural Network Feature Reconstruction

Heterogeneous integration of microelectronic components provides a pathway to improve circuit/component performance; however, this comes with assembly challenges, in particular due to complex interfaces via subsurface bump bonds. The ability of these bonds to transmit electrical signals and conduct heat to the carrier substrate limits component performance. In this work, hyperspectral frequency‐domain thermoreflectance (FDTR) imaging is demonstrated as a robust technique for evaluating the quality of subsurface indium bump bonds in a surrogate microelectronic sample. By performing microscale FDTR imaging with coarse motion image stitching, thermal phase maps that cover a 4 mm by 4 mm field‐of‐view with subsurface feature sensitivity at depths greater than 50 µm are obtained. The resulting FDTR hyperspectral data contains more than three million pixels and reveal the quality of subsurface microbump arrays. Wide‐field analysis of bonded versus gap regions is enabled by deep neural network feature reconstruction, that after training, rapidly provides an interpretable representation of bond quality. Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics.

FDTR↗
DOE OSTI2020

SuperCam Calibration Targets: Design and Development

Abstract SuperCam is a highly integrated remote-sensing instrumental suite for NASA’s Mars 2020 mission. It consists of a co-aligned combination of Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), Visible and Infrared Spectroscopy (VISIR), together with sound recording (MIC) and high-magnification imaging techniques (RMI). They provide information on the mineralogy, geochemistry and mineral context around the Perseverance Rover. The calibration of this complex suite is a major challenge. Not only does each technique require its own standards or references, their combination also introduces new requirements to obtain optimal scientific output. Elemental composition, molecular vibrational features, fluorescence, morphology and texture provide a full picture of the sample with spectral information that needs to be co-aligned, correlated, and individually calibrated. The resulting hardware includes different kinds of targets, each one covering different needs of the instrument. Standards for imaging calibration, geological samples for mineral identification and chemometric calculations or spectral references to calibrate and evaluate the health of the instrument, are all included in the SuperCam Calibration Target (SCCT). The system also includes a specifically designed assembly in which the samples are mounted. This hardware allows the targets to survive the harsh environmental conditions of the launch, cruise, landing and operation on Mars during the whole mission. Here we summarize the design, development, integration, verification and functional testing of the SCCT. This work includes some key results obtained to verify the scientific outcome of the SuperCam system.

Manrique, J. A. (ORCID:0000000220532819)↗
DOE OSTI2023

Single-size and cluster dynamics modeling of intra-granular fission gas bubbles in UO 2

For this work, we perform simulations of intra-granular fission gas bubble evolution in UO 2 using both a relatively simple, computationally inexpensive single-size model and a detailed cluster dynamics model. Simulations encompass 36 experimental cases from 4 different databases, covering various temperature and burnup levels. We systematically compare results from the two models to each other and to post-irradiation experimental data of bubble average size and number density. Overall, the model-to-model comparisons reveal an excellent agreement across the set of simulations. This outcome indicates that, in spite of the underlying assumptions, the single-size model provides a good approximation of the complex physical behavior that is more rigorously described by the cluster dynamics model. Qualitatively, both models reproduce the trends of the experimental data with temperature and burnup correctly. Quantitatively, calculated results are either in good agreement with the data or within errors that appear consistent with the inherent uncertainties. Moreover, for the single-size model, we demonstrate and assess a multiscale approach whereby values for the fission gas atom diffusion coefficient from separate atomistic calculations are used. Systematic comparisons to experimental data point out a credible accuracy of the multiscale model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

An atomic cluster expansion (ACE) potential for water under extreme conditions

We present a machine learning interatomic potential for water designed to capture its complex multiphase behavior, including both molecular and superionic ice phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to enable high-fidelity molecular dynamics simulations of water under extreme conditions, for pressures up to 100 GPa and for temperatures between 500 and 6000 K. A diverse range of configurations was generated through ab initio molecular dynamics (AI-MD) simulations, covering insulating and superionic ice phases, liquid water, and dissociated plasma phase. We demonstrate that the H 2 O ACE potential accurately reproduces experimental and DFT predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of water, including the transition from molecular fluid to the appropriate solid ice phases, and the superionic ice phases. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of water under extreme thermodynamic conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

EM Physics

Geant4 provides a comprehensive set of electromagnetic (EM) processes and models for electron/positron, gamma and long-lived charged particles, spanning energies from 100 eV to 100 TeV. Covering diverse energy regions often requires multiple models, which can be constructed using pre-packaged or user-defined EM physics constructors. Geant4 also supports detailed low-energy EM physics through models like Livermore, Penelope, and ICRU73, offering extensive data for elements across a wide energy range (250 eV–100 GeV). Application domains include space, medical, and radiobiology simulations. Additionally, Geant4 provides robust options for simulating complex optical photon production and transportation processes, enhancing its versatility in physics research and applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Measurement of Neutron Multiplicity in Charged Current Neutrino Interactions on Oxygen

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-doped water Cherenkov detector located 100~meters downstream in the Booster Neutrino Beam (BNB) at the Fermi National Accelerator Laboratory (Fermilab). Its primary goals are to (1) measure the neutron yield from $\nu_\mu$ interactions as a function of momentum transfer $Q^2$ so that neutrino-nucleus interaction models can be better constrained, and (2) demonstrate the power of novel, fast-timing detectors with the first deployment of Large Area Picosecond PhotoDetectors (LAPPDs). Current knowledge of neutrino-nucleus interactions fall short in modeling the topologies of such interactions, leading to inaccurate reconstruction of event kinematics such as particle energy, direction, and vertex. As a consequence, the accuracy of cross section measurements is impacted, which is necessary for precise physics measurements. Neutrons are an indication of inelasticity and affect the determination of the energy of the parent neutrino. Quantifying the neutron yield is a step towards reducing the associated uncertainties, and thus improving our understanding of these complex interactions and benefiting the next generation of long-baseline neutrino experiments. ANNIE will make use of LAPPDs to measure neutron multiplicity of CC-0$\pi$ $\nu_\mu$ interactions on oxygen, making it the first experiment to deploy an array of these photodetectors. Because the LAPPD is a novel photodetection technology, much customization is required to integrate it into existing electronics. The first half of this thesis covers the significant progress made towards the first deployment of the LAPPD system. From its test stand at Fermilab, the LAPPD system was systematically tested and put together until deployment readiness was achieved. I present my contributions to the design, fabrication, and testing of the waterproof housing and cables, and the commissioning of the LVHV board that powers the LAPPD and its readout electronics. These efforts brought the LAPPD system significantly closer to deployment, and eventually first data. The second half of this thesis presents the vertex and energy reconstruction algorithms developed to analysis the beam data with PMT-only information. While much progress has been made towards the deployment of LAPPDs, with several in the detector tank, efforts to integrate the LAPPD datastream are in progress. Thus, I developed a ring edge detection technique using PMT data to fit the muon vertex and determine its energy. The analysis in this thesis finds average neutron yields of $\Bar{n}_{data} = 0.452 \pm 0.039 (\text{stat}) \pm 0.27 (\text{sys})$ for a selection of muon neutrino candidates in the fiducial volume of ANNIE and corrected with an averaged neutron detection efficiency. An equivalent analysis for simulated beam data results in an average neutron yield of $\Bar{n}_{MC} = 0.582 \pm 0.018 (\text{stat}) \pm 0.25 (\text{sys})$. Future work includes application of efficiency corrections at a positional level, quantification of all systematic uncertainties, and neutron multiplicity measurements with other event topologies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Digital camera imagery for vegetation phenology, Seward Peninsula, Alaska, 2021-2022

Timelapse camera images from Council Mile Marker (MM) 71, Kougarok MM 64, Kougarok Fire Complex (KFC, also referred to as the Garfield Fire Site), and Teller MM 27 NGEE-Arctic field sites on the Seward Peninsula, Alaska, captured from August 2021 to July 2022. Ten Wingscape Timelapse Pro cameras, and twenty seven Power-interval Camera Automation Modules (PiCAMs) designed by Brookhaven National Laboratory's Terrestrial Ecosystem Science and Technology (TEST) group were deployed targeting patches of low and tall shrubs (including Alnus sp. and Salix sp.) and general vegetation and landscape views. Images from Wingscape cameras were recorded at hourly intervals from 11 AM to 2 PM, and images from PiCAMs were recorded at 5 hourly intervals from 12 AM to 8 PM, continuously for 12 months and capture vegetation phenology, snow accumulation and snow melt events. This data package includes images (*.jpg), organized by site and camera ID, and metadata with details of the cameras used, number of images recorded, start and end dates, GPS locations and example fields of view. Additional information available in *.pdf and *.csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Revealing an Intermediate Cu–O/OH Superstructure on Cu(110)

Identifying the atomic structure and formation mechanism of intermediates during chemical transformations is challenging because of their short-lived nature. With a combination of microscopic and spectroscopic measurements and first-principles calculations, herein we report the formation of a metastable intermediate Cu–O/OH superstructure during the reaction of hydrogen with oxygen-covered Cu(110). This superstructure resembles the parent c(6 × 2)-O phase and can be termed as c(6 × 2)-(4O+2OH) with OH groups occupying the missing Cu sites between isolated Cu atoms. Using atomistic calculations, we elucidate the reaction pathways leading to the c(6 × 2)-(4O+2OH) formation via both molecular and dissociative H 2 adsorption. In this work, the results demonstrate the complex surface dynamics resulting from the parallel reaction pathways and may open up the possibility of directing the reaction dynamics by deliberately manipulating transient surface structure and composition.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting phase behavior in high entropy and chemically complex alloys

The interest in high entropy alloys and other metallic compounds with four or more elements at near-equiatomic ratios has drawn attention to the ability to rapidly predict phase behavior of these complex materials, particularly where existing thermodynamic data are lacking. In this paper, we discuss aspects of this from the point of view of predicting without utilizing (or fitting) experimental data. Of particular interest are heuristic approaches that provide prediction of single-phase compositions, more rigorous approaches that tackle the thermodynamics from a more fundamental point of view, and simulation approaches that provide further insight into the behaviors. This paper covers cases of all three of these, in order to examine the strengths and weaknesses of each approach, and to indicate directions where these may be utilized and improved upon. Of particular interest is moving beyond “which composition may form a solid solution,” to recognizing the importance of underlying thermodynamic realities that affect the temperature- and composition-dependent transformations of these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure-Based Analysis of Semisynthetic Anti-TB Rufomycin Analogues

This study employed structural information from cocrystals of rufomycin 4 (1a) and caseinolytic protein C1 (ClpC1)-NTD-wt to guide design and semisynthesis of rufomycin analogues, evaluate their antituberculosis (TB) biological profiles, and establish structure–activity relationships (SAR). Covering three regions of interest (ROIs, A–C) as modification sites, 14 of the 30 semisynthetic analogues (2–31) showed similar or improved MICs relative to the main natural precursors, rufomycins 4/6 (1a/b). Compounds 5 and 27 exhibited up to 10-fold enhanced potency against Mycobacterium tuberculosis (Mtb) in vitro, with MIC values of 1.9 and 1.4 nM, respectively. Evaluation of ClpC1-binding properties used existing ClpC1-NTD complexes with rufomycin 4 (PDB: 6cn8) and ecumicin (PDB: 6pbs) as references. The newly reported X-ray ClpC1-NTD cocrystal structure of 11 (syn. But4-Cl) revealed significant conformational effects involving the side chains of certain amino acids of the heptapeptide and confirmed the importance of ROIs A–C for medicinal chemistry efforts. Observed interactions of the N-terminal tail of ClpC1 with the rufomycin analogues vs ecumicin explains their different modes of inactivating the ClpC1/P1/P2 homeostatic machinery. Collectively, the observations inform further SAR optimization strategies for the rufomycin class of antibiotics and complement our understanding of their mode of action.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Accurate and efficient parameterization of an atomic cluster expansion (ACE) potential for ammonia under extreme conditions

We present a machine learning interatomic potential for ammonia designed to capture its complex multiphase behavior, including both molecular and superionic phases. The potential is based on the atomic cluster expansion (ACE) formulation and has been parameterized to facilitate high-fidelity molecular dynamics simulations of ammonia under extreme conditions, for pressures up to 100 GPa and for temperatures above 500 K and up to 6000 K. A diverse range of configurations was generated through high-quality ab initio molecular dynamics simulations, covering insulating and superionic ice phases, liquid ammonia, molecular nitrogen (N 2 ) and hydrogen (H 2 ), and metastable compounds that form upon dissociation, including $NH^{+}_{4}$, $H^{+}_{3}$, N 2 H 4 , and N 3 H. We demonstrate that the ammonia ACE potential accurately reproduces experimental and density functional theory predicted isotherms and Hugoniots. Crucially, the potential is able to capture the intricate phase behavior of ammonia, including the transition from insulating molecular fluid to the superionic phase. This work provides a robust interatomic potential that can be used for large-scale, accurate simulations of ammonia under extreme thermodynamic conditions, offering a powerful tool for investigating its behavior in various phases and applications.

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

Present and future of JLab CLAS12 physics program

The CLAS12 detector at Jefferson Lab produced the first results in SIDIS and DI-HADRON reactions. Making use of the CEBAF high energy (up to 11 GeV) and highly longitudinal polartized (up to 90%) electron beam will cover unexplored territories in electron-scattering physics. Exclusive reactions on nuclons and nuclei will be measured with high precision in high luminosity (up to 10e35 cm-2s-1) experiments. Mapping out a new class of structure functions, GPDs and TMDs, detecting and reconstructing exotic meson and baryon states and accessing nucleons correlations in nuclei, in the next decade, the CLAS12 rich physics program will provide insight in the complex dynamics of the QCD paving the road to the EIC physics. In this talk, the CLAS12 detector and JLab Hall-B physics program will be described reporting some preliminary results for flag-ship reactions and plans for future upgrades of the detector.

Battaglieri, Marco↗
DOE OSTI2025

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Citizen science↗
DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗
DOE OSTI2024

The first ensemble of kilometer-scale simulations of a hydrological year over the third pole

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Tutorial review on structure – dendrite growth relations in metal battery anode supports

This tutorial review explains the emerging understanding of the surface and bulk chemistry – electrochemical performance relations in anode supports (aka secondary current collectors, substrates, templates, hosts) for lithium, sodium and potassium metal batteries (LMBs, SMBs or NMBs, and KMBs or PMBs). In relation to each section, the possible future research directions that may yield both new insight and improved cycling behavior are explored. Representative case studies from Li, Na and K metal anode literature are discussed. The tutorial starts with an overview of the solid electrolyte interphase (SEI), covering both the “classic” understanding of the SEI structure and the “modern” insights obtained by site-specific cryogenic stage TEM analysis. Next, the multiple roles of supports in promoting cycling stability are detailed. Without an optimized support architecture, the metal–electrolyte interface becomes geometrically unstable at a lower current density and cycle number. Taking into consideration the available literature on LMBs, SMBs and KMBs, it is concluded that effective architectures are geometrically complex and electrochemically lithiophilic, sodiophilic or potassiophilic, so as to promote conformal electrochemical wetting of the metal during plating/stripping. One way that philicity is achieved is through support oxygen surface chemistry, which yields a reversibly reactive metal–support interface. Examples of this include the well-known oxygen–carbon moieties in reduced graphene oxide (rGO), as well as classic ion battery reversible conversion reaction oxides such as SnO 2 . Unreactive surfaces lead to dewetted island growth of the metal, which is a precursor to dendrites, and possibly to non-uniform dissolution. Surveying the literature on various Li, Na and K metal supports, it is concluded that the key bulk thermodynamic property that will predict electrochemical wetting behavior is the enthalpy of infinite solution (Δ sol H ∞ ) of the metal (solute) into the support (solvent). Large and negative Δ sol H ∞ promotes uniform metal wetting on the support surface, corresponding to relatively low plating overpotential. Positive Δ sol H ∞ promotes dewetted islands and a relatively high overpotential. This simple rule explains a broad range of studies on Li, Na and K metal – support interactions, including the previously reported correlation between mutual solubility and wetting.

25 ENERGY STORAGE↗
DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Probe-Enabled Approaches for Function-Dependent Cell Sorting and Characterization of Microbiome Subpopulations

Understanding the roles that individual species or communities play within a microbiome is a significant challenge. The complexity and heterogeneity of microbiomes presents a challenge to researchers looking to unravel the function that microbiomes serve within larger environments. While identification of the species and proteins present in a microbiome can be accomplished through genomics approaches, strategies that report on enzyme activity are limited. In this chapter, we describe the application of small molecule chemical probes in the isolation and subsequent characterization of microbiome subpopulations based on enzymatic function. We will cover protocols for labeling microbes with appropriate probes, microbiome sample preparation, and using fluorescence-activated cell sorting to isolate subpopulations based on function. We hope that the strategies outlined here will serve as a resource for researchers studying the functional role that microbiomes play in the gut and soil.

Steiger, Andrea K.↗