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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Historic and modern nuclear graphite impurities: Pathways to improved waste strategies

Graphite has been used in large volumes as a structural material and neutron moderator since the earliest days of nuclear fission. However, no international consensus exists on the disposal of irradiated graphite, leaving much of the historic radioactive graphite inventory in interim vault or silo storage. With several new graphite-moderated reactors planned or under construction, the issue of graphite waste management is becoming increasingly urgent. This paper reviews and quantifies impurities in both historic and modern nuclear graphite, with emphasis on nitrogen—responsible for much of the 14 C inventory—and chlorine, which plays a critical role in repository performance and design. Modern graphites, benefitting from stringent quality-control measures developed for non-nuclear industries, meet or exceed the ASTM Ultra-High Purity nuclear standards, even without halide purification. Both chlorine and nitrogen concentrations have declined over time. For chlorine, identified as a key impurity influencing U.S. waste repository design, we propose a target of 0.1 appm in as-fabricated billets as a reasonable benchmark. Nitrogen sources are traced throughout the graphite production process, with surface and bulk concentrations characterized for all materials studied. Modern graphites commonly exhibit nitrogen levels below 5 appm, with values approaching 1 appm achievable. Using such reduced-nitrogen grades is critical to keeping graphite-induced radioactivity below the greater-than-Class-C waste threshold, thereby avoiding disposal cost penalties of nearly an order of magnitude.

Chlorine↗
DOE OSTI2024

Residual resistance ratio measurement system for Nb 3 Sn wires extracted from Rutherford cables

Residual resistance ratio (RRR) of superconducting strands is an important parameter for magnet electrical stability. RRR serves as a measure of the low-temperature electrical conductivity of the copper within a conductor that has a copper stabilization matrix. For Nb 3 Sn, due to the need of a reaction heat treatment, the technical requirements for high quality measurements of strands extracted from Rutherford cables are particularly demanding. Quality of wire, cabling deformation, heat treatment temperature, heat treatment atmosphere, sample handling, and measurement methods can all affect the RRR. Therefore, as an integral part of the electrical quality control (QC) of Nb 3 Sn Rutherford cables manufactured at the Lawrence Berkeley National Laboratory, it was prudent that we established a RRR measurement system that can isolate the assessment of cable-fabrication-related impacts from sample preparation and measurement factors. Here we describe a bespoke cryocooler-based measurement system, capable of measuring RRR of over 80 samples in a single cooldown. The samples are mounted on custom-designed printed circuit boards that accommodate the shape of strands extracted from a Rutherford cable without added deformation, which we will show is critical in ensuring that the measurements accurately represent the RRR values of the conductor within the cable. Using this sample mounting solution, we routinely measure the overall RRR of the strand as well as individual intra-strand sections corresponding to both cable edges and cable broad faces with high reproducibility. Such measurements provide valuable information on the variation of RRR along the length of the strands as well as across strand productions and cable runs over time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗
DOE OSTI2023

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. Here we explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

NASA GeneLab RNA-seq consensus pipeline: Standardized processing of short-read RNA-seq data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improved lipid analysis using a 2D-LC-MS system with a novel injection procedure

The aim of this study was to improve analysis of nonpolar lipidomics sample extracts using reversed phase (RP) chromatography. A 4/3/3 (v/v/v) mixture of methanol/methyl tert-butyl ether/chloroform (MeOH/MTBE/CHCl 3 , MMC) was chosen for sample extraction solvent based on its proven extraction capability for several lipid classes. To avoid carry over, loss of analytes and peak distortion the loops and all capillaries of the presented LC system were flushed and filled up with methanol until the analytical column. Here, the choice of methanol was due to its weak elution strength and being infinitely miscible with MMC and several other nonpolar solvents. This allowed injection of a 100 μl sample that was 20 μl nonpolar extraction solvent diluted fivefold with methanol. All lipids of 25 lipid classes were transferred quantitatively to the column head where the online dilution of methanol was carried out with aqueous eluent for focusing the lipid analytes. The weak elution strength of methanol prevented peak distortions. The consecutive reversed phase elution resulted in remarkably narrow peaks (full width at half maximum was 0.07–0.08 min typically) and enhanced sensitivity (limit of detection usually in sub nM region) because of increased sample injection volume and narrow peaks. Calibration and quality control samples made by diluting commercial lipid standards 200–50000 times confirmed the applicability of this approach both for targeted lipid quantification and for untargeted quantitative comparison of lipids from different sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

In-situ imaging of nucleation and growth of superlattices from nanoscale colloidal nanoparticles

Nanoparticle-based superlattices have attracted extensive research efforts due to their versatile composition and packing-dependent optical, magnetic, electronic, catalytic, mechanical properties. Yet the experimental understanding of the thermodynamics and pathways of the crystallization processes for these systems, which govern their ultimate morphology, size, and packing structure, has for long remained underexplored. Here, this minireview highlights recent integration of liquid-phase transmission electron microscopy, which allows for real-space, real-time direct imaging of the crystallization processes at the single-particle level, with simulation and the conceptual framework established by pioneers such as Dr. A. A. Chernov. Different aspects of superlattice formation were revealed by direct imaging for the first time, such as the existence of prenucleation precursor in nonclassical crystallization, surface energy-dependent growth, and coalescence, allowing for the charting of phase coordinates and thermodynamic quantities at the nanoscale. We discuss the similarities and differences of crystal growth behaviors in atomic and nanoparticle systems, as well as engineering opportunities to fashion, shape, and achieve quality control in superlattice formation from nanoparticles, for next-generation optical and mechanical metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A high-throughput approach for statistical process optimization in Laser Powder Bed Fusion

Process variability is inherent in metal additive manufacturing (AM). However, it is often overlooked in process optimization frameworks, constraining the understanding of process uncertainties and their influence on parameter selection. To address this, we present an integrated framework that combines high-throughput single-track experiments, GAN-based melt pool geometry extraction, robust statistical and machine learning modeling, and uncertainty-quantified process mapping. Process variability is characterized through single-track melt pool behaviors, and its influence on defect formation is systematically quantified to enable statistically guided process parameter optimization. This approach is demonstrated on Laser Powder Bed Fusion (L-PBF) of stainless steel 316L, effectively capturing the interplay between process parameters, melt pool variability, and defect probability. By integrating uncertainty quantification into process optimization, this study provides a structured methodology for addressing variability challenges in AM quality control, ultimately contributing to enhanced manufacturing reliability.

Laser Powder Bed Fusion↗
DOE OSTI2021

Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps

We report that local thermal history can significantly vary in parts during metal Additive Manufacturing (AM), leading to local defects. However, the sequential layer-by-layer nature of AM facilitates in-situ part voxelmetric observations that can be used to detect and correct these defects for part qualification and quality control. The challenge is to relate this local radiometric data with local defect information to estimate process error likelihood in future builds. This paper uses a Short-Wave Infrared (SWIR) camera to record the temperature history for parts manufactured with Laser Powder Bed Fusion (LPBF) processes. The porosity from a cylindrical specimen is measured by ex-situ micro-computed tomography (μCT). Specimen data from the SWIR camera, combined with the μCT data, are used to generate thermal feature-based porosity probability maps. The porosity predictions made by various SWIR thermal feature-porosity probability maps of a specimen with a complex geometry are scored against the true porosity obtained via μCT. The receiver operating characteristic curves constructed from the predictions for the complex sample demonstrate the porosity probability mapping methodology’s potential for in-situ based porosity detection.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Internal defect detection and characterization of samarium-cobalt sintered magnets by ultrasonic testing technique

Excessive quantities of samarium-cobalt (Sm-Co) magnet material are being scrapped needlessly due to a lack of understanding of inhomogeneity distribution and unacceptable internal defects. If there is a way to identify, locate, characterize and when needed separate the defective portions of magnet material, utilization can be increased and product quality improved. Further, the magnets’ magnetic and mechanical performance can be improved by reducing the occurrence of internal defects. This paper reports on a cost-effective and efficient nondestructive evaluation method based on an ultrasonic testing (UT) technique applied for detecting and characterizing internal defects in Sm-Co sintered magnets. Applying the UT technique will allow users to comprehensively analyze internal defects, such as inclusions, porosity, microcracks, and other structural irregularities, check for homogeneity and anomalous regions and give the locations of these internal anomalies and defects within the Sm-Co sintered magnets. The UT technique can also be applied to other rare-earth permanent magnets, such as sintered or die-upset neodymium-iron-boron (Nd-Fe-B) magnets. The UT technique can effectively guide quality control and acceptable product selection, in addition to optimizing the magnet alloy design and production processes. Therefore, it can facilitate the improvement of magnet manufacturing efficiency and machinability, reduce scrap, prolong service life, increase the use of what would be post-production waste, and enhance product reuse and recycling at end-of-life disposition.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analysis of phospholipids and triacylglycerols in intravenous lipid emulsions

Intravenous lipid emulsions (ILEs) are used for parenteral nutrition, providing a vital source of essential fatty acids and concentrated energy for patients who are unable to absorb nutrients via the digestive track. They are commonly used to treat local and non-local anesthetic toxicity, and lipophilic drug overdose. ILE are composed of natural lipids, and the composition of these natural lipids can be varied based on their source. The lipids are susceptible to hydrolytic degradation with time, resulting various lipid degradation products such as Lysophosphatidylcholines (LPs), affecting the actual composition of nutrients in the formulation. As a result, the identification and quantification of lipid components, including degradation products, in ILEs are crucial in quality control. In this study, lipids from different batches of ILE Intralipid® 20%, were separated and identified using a UHPLC-ESI-QTOF system and SimLipid® high throughput lipid identification software. Out of 47 lipids identified, 34 were phospholipids (PLs) and the others were triacylglycerols (TAGs). Most of the phospholipids detected were phosphatidylcholines (PC) and Lysophosphatidylcholines (LPC). A total of 9 LPCs, 18 PCs, 6 phosphoethanolamines (PEs), and 1 sphingomyelin (SM) were identified. The LPCs concentration changed with the manufacturing date and storage time. Furthermore, this UHPLC method enabled the identification and quantification of lipids and their decomposition products in complex ILE emulsion mixtures on a single 20-minute chromatographic run.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Towards underwater additive manufacturing via additive friction stir deposition

Given the challenges in feed material supply and quality control, metal additive manufacturing has rarely been implemented in austere environments, especially underwater. This paper explores the underwater operation potential of an emerging solid-state additive technology: additive friction stir deposition, wherein material feeding and bonding are enabled by mechanical forces with minimal influences from water. It is demonstrated that additive friction stir deposition of 304 stainless steel can be successfully performed with the print head and substrate immersed in water. High temperature is reached in the deposition zone (>60% melting temperature); the material deposition behavior is similar to that in typical open-air operation. The as-deposited material is fully-dense, having fewer annealing twins and a substantially smaller grain size than the feed material (4.98 μm vs. 31.44 μm ). Such microstructural changes stem from dynamic recrystallization caused by the large strain and high temperature introduced during deposition. In addition to grain refinement, small equiaxed dispersoids (~2–3 μm or less) are formed and evenly distributed in the austenite steel matrix. Rich in Cr, Mn, and O, these particles likely result from the reaction between the elements in stainless steel and water at elevated temperatures.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Strip sensor performance in prototype modules built for ATLAS ITk

ATLAS experiment is preparing an upgrade of its detector for High-Luminosity LHC (HL-LHC) operation. The upgrade involves installation of the new all-silicon Inner Tracker (ITk). In the context of the ITk preparations, more than 80 strip modules were built with prototype barrel sensors. They were tested with electrical readout on a per-channel basis. In general, an excellent performance was observed, consistent with previous ASIC-level and sensor-level tests. However, the lessons learned included two phenomena important for the future phases of the project. First was the need to store and test the modules in a dry environment due to humidity sensitivity of the sensors. The second was an observation of high noise regions for 2 modules. The high noise regions were tested further in several ways, including monitoring the performance as a function of time and bias voltage. Additionally, direct sensor-level tests were performed on the affected channels. The inter-strip resistance and bias resistance tests showed low values, indicating a temporary loss of the inter-strip isolation. A subsequent recovery of the noise performance was observed. Here we present the test details, an analysis of how the inter-strip isolation affects the module noise, and the relationship with sensor-level quality control tests.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

An automated QC station for the calibration of the Mu2e calorimeter readout units

The Mu2e calorimeter will employ Readout Units, each made of two Silicon Photomultipliers arrays and two Front End Electronics boards. To calibrate them, we have designed, assembled and put in operation an automated Quality Control (QC) station. Gain, collected charge and photon detection efficiency are evaluated for each unit. Here, in this paper, the QC Station is presented, in its hardware and software aspects, summarizing also the tests performed on the ROUs and the first measurement results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗