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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy

The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparisons between climate models with SLAB and fully-dynamic (FULL) ocean components are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy resolving pre-industrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. Here, the stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated, suggesting a role for ocean dynamics beyond entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dataset: Breaking the barrier of human-annotated training data for machine-learning-aided plant research using aerial imagery

This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.

generative and adversarial learning↗
NASA NTRS2021

LET and Range Characteristics of Proton Recoil Ions in Gallium Nitride (GaN)

A better understanding of the linear energy transfer and the range of proton recoil ions in gallium nitride is necessary to properly evaluate GaN device radiation tolerance. By analyzing the linear energy transfer (LET) and range of recoil heavy ions in GaN we can begin to reproduce the body of knowledge that exists for Si-based devices for this upcoming technology. Although the previous data on older technology has impressive depth and breadth we must be diligent and cautious in the application of these institutional intuitions when applied to emerging technologies such as GaN. As ever increasing materials science advances emerge, a sound methodology for evaluating new technologies must be established in order to apply what we know towards the effort of ensuring radiation tolerance.

gallium nitride (GaN)↗
DOE OSTI2020

Trace-Level, Multi-Gas Detection for Food Quality Assessment Based on Decorated Silicon Transistor Arrays

Multiplexed gas detection at room temperature is critical for practical applications, such as for tracking the complex chemical environments associated with food decomposition and spoilage. Here, an integrated array of multiple silicon-based, chemical-sensitive field effect transistors (CSFETs) is presented to realize selective, sensitive, and simultaneous measurement of gases typically associated with food spoilage. CSFETs decorated with sensing materials based on ruthenium, silver, and silicon oxide are used to obtain stable room-temperature responses to ammonia (NH 3 ), hydrogen sulfide (H 2 S), and humidity, respectively. For example, one multi-CSFET sensor signal changes from its baseline by 13.34 in response to 1 ppm of NH 3 , 724.45 under 1 ppm H 2 S, and 23.46 under 80% relative humidity, with sensitive detection down to 10 ppb of NH 3 and H 2 S. To demonstrate this sensor for practical applications, the CSFET sensor array is combined with a custom-printed circuit board into a compact, fully integrated, and portable system to conduct real-time monitoring of gases generated by decomposing food. By using existing silicon-based manufacturing methodologies, this room-temperature gas sensing array can be fabricated reproducibly and at low cost, making it an attractive platform for ambient gas measurement needed in food safety applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Towards a quantitative understanding of bonding in supersonic single particle impacts: A three-dimensional FIB-SEM exploration

Particle bonding is crucial to coating quality in cold spray, but it has been a challenge to accurately quantify bonding even in single particle impacts. This paper uses FIB-SEM to explicitly map the particle-substrate interface for Cu-on-Cu single microparticle impacts in a full 3D rendering that spans a wide range of impact velocities. This approach permits a detailed quantification of the total bonding area and all of its associated components. In addition to revealing why prior 2D characterization efforts have missed important details about impact bonding, these data quantitatively reveal the evolution of bonding from its onset at the “critical velocity” V cr (where bonding is generally poor, ∼6 %) to its peak at around 1.3‧V cr (where almost 90 % of the particle bonds). Further increase in the velocity to 1.5‧V cr and beyond finds the onset of hydrodynamic penetration and a decrease in bonding. These data then support the development of a simple analytical model based on oxide rarefication and extrusion of bare metal through gaps in the oxide layer as driving the development of bonding. As a result, the model reproduces the experiments and provides guidance on optimization of bonding as a function of material and process parameters.

Analytical methods↗
DOE OSTI2024

Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements

Abstract Atmospheric CO 2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO 2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Abstract As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Response of Graphite to Dynamic Loading and Hypervelocity Jet Impacts

The compressive strengths of three varieties of high purity graphite, PCEA, NBG-18, and NBG-25, as well as the depth of penetration of small-scale charges into these materials was experimentally determined. These grades are similar in density, ranging from 1.80 – 1.85 g/cc, and nominal apparent porosity, ranging from 18% to 20%, but provide a wide range in maximum grain or particle size from 10s to 1000s of µm. Two very different manufacturing methods are also represented; PCEA is extruded while NBG-18 and NBG-25 are iso-molded. The quasistatic and dynamic strengths of each grade were determined on a load frame and split-Hopkinson pressure bar, respectively. The depth of penetration (DOP) of two small-scale shaped charges, the Teledyne RP-1 and RP-4, was determined against graphite. The global response of the RP-4 impacts was markedly different as the PCEA samples remained intact while all the NBG-25 samples split into 2 or 3 pieces after the jet penetration had completed. However, for all tests, the trusted DOPs fell within 2 cm. Preliminary hydrocode modeling of the penetration events used existing models that were not designed for graphite. The results can be tuned to reasonably reproduce the DOP, but the wound channel geometry is not reproduced well. A model designed for graphite would need to represent graphite’s non-linear and energy dissipation characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dataset for Role of electron correlation on the adenine dimer interaction for non-equilibrium geometries: a benchmark Quantum Monte Carlo study

Datasets for the calculations reported in "Role of electron correlation on the adenine dimer interaction for non-equilibrium geometries: A benchmark Quantum Monte Carlo study" by L. Washburn, A. Sedova, P. R. C. Kent. J. Chem. Phys. (2026) 165 (5): 054118. https://doi.org/10.1063/5.0332651. Includes the molecular geometries, QMCPACK, PySCF, and ORCA inputs and outputs, analysis scripts and files needed to reproduce all the figures and tables.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Taguchi method of experimental design in materials education

Some of the advantages and disadvantages of the Taguchi Method of experimental design as applied to Materials Science will be discussed. This is a fractional factorial method that employs the minimum number of experimental trials for the information obtained. The analysis is also very simple to use and teach, which is quite advantageous in the classroom. In addition, the Taguchi loss function can be easily incorporated to emphasize that improvements in reproducibility are often at least as important as optimization of the response. The disadvantages of the Taguchi Method include the fact that factor interactions are normally not accounted for, there are zero degrees of freedom if all of the possible factors are used, and randomization is normally not used to prevent environmental biasing. In spite of these disadvantages it is felt that the Taguchi Method is extremely useful for both teaching experimental design and as a research tool, as will be shown with a number of brief examples.

Weiser, Martin W.↗
NASA NTRS2003

Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters

Near-infrared spectroscopy (NIRS) can be used to noninvasively measure changes in the concentrations of oxy- and deoxyhemoglobin in tissue. We have previously shown that while global changes can be reliably measured, focal changes can produce erroneous estimates of concentration changes (NeuroImage 13 (2001), 76). Here, we describe four separate sources for systematic error in the calculation of focal hemoglobin changes from NIRS data and use experimental methods and Monte Carlo simulations to examine the importance and mitigation methods of each. The sources of error are: (1). the absolute magnitudes and relative differences in pathlength factors as a function of wavelength, (2). the location and spatial extent of the absorption change with respect to the optical probe, (3). possible differences in the spatial distribution of hemoglobin species, and (4). the potential for simultaneous monitoring of multiple regions of activation. We found wavelength selection and optode placement to be important variables in minimizing such errors, and our findings indicate that appropriate experimental procedures could reduce each of these errors to a small fraction (<10%) of the observed concentration changes.

Non-NASA Center↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom Probe Tomography (APT) is a powerful technique for visualizing the atomic-scale distribution of solutes in materials, but quantitative cluster analysis of APT datasets remains a challenge due to the need for subjective parameter selection in clustering algorithms. While distance-based and density-based methods such as HDBSCAN are widely used, their performance is highly sensitive to user-defined parameters, which undermines reproducibility and accuracy. This study proposes an image-based, deep learning-aided workflow for automating parameter selection and cluster detection in APT data analysis. By projecting 3D APT point clouds onto 2D planes, we leverage pretrained convolutional neural networks (ConvNeXt-Tiny and ResNet-50) through transfer learning to predict the number of clusters present in synthetic datasets. The output is used to guide K-means clustering and estimate HDBSCAN parameters, specifically minimum cluster size and minimum sample points. This approach reduces reliance on manual parameter tuning, improving consistency and scalability. The methodology demonstrates the feasibility of using image-based deep learning for interpreting complex spatial patterns in APT data, enabling faster and more objective analysis. The complete workflow and code are made publicly available to support reproducibility and future research.

Density-based clustering↗
DOE OSTI2021

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. Here, the variations in the resulting analyses are explored due to the influence of new and more varied data.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Preliminary Evaluation of GEOS-5 Aerosol and CO Distributions Forecast During TC4

The NASA Tropical Composition, Cloud, and Climate Coupling (TC4) Mission was based out San Jose, Costa Rica during July and August 2007. During TC4 the NASA Global Modeling and Assimilation Office (GMAO) ran twice-daily 0.5 x 0.666 global 5-day forecasts of the Goddard Earth Observing System atmospheric general circulation model and data assimilation system (GEOS-5). This implementation of GEOS-5 contained an aerosol and carbon monoxide (CO) model to provide online forecast tropospheric distributions of dust, sea salt, sulfate, and carbonaceous aerosols and CO for both the planning of flights and for science. Here we provide a description of the aerosol and CO modeling system and give a preliminary evaluation of forecast tracer distributions. Our comparisons to satellite observations of aerosol and CO show qualitatively similar simulated distributions of tracers to those observed. During TC4 copious amounts of dust were observed in the Caribbean. The model generally reproduced the observations of the timing of dust events and the vertical structure in the lower atmosphere. However, the model simulations had too much aerosol at high altitudes relative to airborne Cloud Physics Lidar observations. The results were similar for biomass burning aerosol and CO tracers, where the model showed higher simulated concentrations of these tracers at aircraft flight altitude than observations.

Colarco, Peter R.↗
DOE OSTI2024

Anthropogenic aerosols mask increases in US rainfall by greenhouse gases

A comprehensive understanding of human-induced changes to rainfall is essential for water resource management and infrastructure design. However, at regional scales, existing detection and attribution studies are rarely able to conclusively identify human influence on precipitation. Here we show that anthropogenic aerosol and greenhouse gas (GHG) emissions are the primary drivers of precipitation change over the United States. GHG emissions increase mean and extreme precipitation from rain gauge measurements across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions further offset GHG increases in the winter and spring but enhance rainfall during the summer and fall. Our results show that the conflicting literature on historical precipitation trends can be explained by offsetting aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes but cannot confidently determine whether a given anthropogenic agent has increased or decreased rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Uncoupling differential water usage from drought resistance in a dwarf Arabidopsis mutant

Abstract Understanding the molecular and physiological mechanisms of how plants respond to drought is paramount to breeding more drought-resistant crops. Certain mutations or allelic variations result in plants with altered water-use requirements. To correctly identify genetic differences which confer a drought phenotype, plants with different genotypes must be subjected to equal levels of drought stress. Many reports of advantageous mutations conferring drought resistance do not control for soil water content (SWC) variations across genotypes and may therefore need to be re-examined. Here, we reassessed the drought phenotype of the Arabidopsis (Arabidopsis thaliana) dwarf mutant, chiquita1-1 (chiq1-1, also called constitutively stressed 1 (cost1)), by growing mutant seedlings together with the wild-type to ensure uniform soil water availability across genotypes. Our results demonstrate that the dwarf phenotype conferred by loss of CHIQ1 function results in constitutively lower water usage per plant, but not increased drought resistance. Our study provides an easily reproducible, low-cost method to measure and control for SWC and to compare drought-resistant genotypes more accurately.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Verification of RESRAD-OFFSITE Code (V.4)

This report documents the verification of RESRAD-OFFSITE Version 4.0 and describes, where necessary, the verification of the following: • The data comprising the standard dose and risk coefficient libraries in the RESRAD database files Master_dcf_ICRP07.mdb and Master_dcf_2k.mdb. • The extraction and transfer of the data from the selected database file to the computational code by the RESRAD-OFFSITE 4.0 interface, ResOWin.exe. • The different processes that are modeled by the main computational code in RESRAD OFFSITE 4.0, ResOMain.exe. • The data displayed in the graphical and text reports. Many verifications were performed as part of the quality assurance quality control program associated with the development and release of RESRAD-OFFSITE 4.0, namely: • developer testing, • internal independent testing, and • release testing. Some were also performed in response to questions from users regarding the performance of the code. The main text of the report focuses on summarizing a subset of those tests, both independent and developer tests that verified the computations performed by the code. The verifications included in this report served as the basis for the development of the release tests of the computational executables and provided the quantitative results to be compared with the code output. The input and output interfaces and the data transfers between the various executables of the code were tested while performing the verification testing. They were tested intentionally during release testing. This report also provides some basic information to help in understanding the activities that were verified. The report: • outlines the components of RESRAD-OFFSITE 4.0 and the interconnections between these components, • outlines the processes modeled by the computational code, • provides summary figures and tables to offer confirmation of the verification of the computational components of the code, • reproduces the verifiers’ reports, if available, in individual appendices, • refers to the previous verification report (Yu et al. 2011) for more details about some of the verifications, and • reproduces the test cases and the testers’ reports from the release testing in individual appendices, when possible.

54 ENVIRONMENTAL SCIENCES↗