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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development of an Attenuated Total Reflectance–Ultraviolet–Visible Probe for the Online Monitoring of Dark Solutions

Optical spectroscopy is a valuable tool for on-line monitoring of a variety of processes. Ultraviolet-visible (UV-vis) spectroscopy in particular, can monitor the concentration of analytes as well as identify speciation and oxidation state. However, it can be difficult to impossible to employ UV-vis based sensors on chemical systems that are very dark (i.e., high optical density) as exceedingly short pathlengths are required (for transmission approaches) or effective means of backscattering are needed (for reflectance approaches). Examples of processes that would benefit significantly from the use of optical sensors and encounter these challenges include used nuclear fuel recycling and molten salts with high concentrations of dissolved uranium. Utilizing an attenuated total reflectance (ATR) UV-vis approach can overcome these challenges and allow for the measurement of solutions orders of magnitude more concentrated than transmission UV-vis. However, determining ideal sensor specifications for varied processes can be time consuming and expensive. Here, in this study, we evaluate the ability for a novel ATR-UV-vis probe to measure very concentrated solutions of Co(II) and Ni(II) nitrate as well as organic dyes (methylene blue, acid red 1, and crystal violet). This sensor design provides a modular method for exploring possible “pathlengths” by altering the exposed ATR fiber length. Also studied were approaches to loading and measuring the sensor cell. These results are compared to a traditional 1 cm cuvette measured by transmission UV-vis. It was found that the ATR-UV-vis probe was capable of measuring solutions 600 times more concentrated than the 1 cm cuvette. Advanced data analysis in the form of multivariate curve resolution (MCR) was used to analyze the speciation of methylene blue over a large concentration range. The application of this novel ATR-UV-vis probe to the interrogation of dark solutions is a promising avenue for use in on-line monitoring of nuclear processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗
DOE OSTI2025

Studying the Durability of Molybdenum Carbide Aerogel as an Electrocatalyst Support for Proton Exchange Membrane Fuel Cells

Carbon-supported platinum nanoparticles (Pt/C) are currently the state-of-the-art catalyst in proton exchange membrane fuel cells (PEMFCs). Unfortunately, the carbon support lacks the ability to stabilize the metal catalyst, as platinum tends to dissolve and agglomerate, significantly compromising the durability. Herein, we synthesized a ceramic material, molybdenum carbide aerogel (MCAG), and utilized it as a Pt support for PEMFCs, as an alternative for conventional carbon supports. N 2 adsorption and XRD analysis showed that the MCAG possesses a combination of high porosity and a well-defined ceramic crystalline structure. The Pt/MCAG system was studied for its electrocatalytic activity toward ORR in a half-cell and demonstrated satisfactory reaction kinetics and electrochemical active surface area, comparable to the commercial Pt/C. For durability examination, an accelerated stress test (AST) in a single cell was conducted with the Pt/MCAG catalyst at the cathode, following the U.S. DOE electrocatalyst AST protocol. Intermediate polarization curves and cyclic voltammograms were recorded over the course of the AST to monitor the aging process. The results indicated that the MCAG support exhibited superior durability in long-term fuel cell operation compared to a carbon support. The Pt/MCAG system demonstrated stable behavior during the progressive stages of the AST, retaining more than 50% of its initial performance by the end of the test, whereas carbon-supported Pt preserved only 30%.

25 ENERGY STORAGE↗
DOE OSTI2024

Observation of molecular resonant double-core excitation driven by intense X-ray pulses

The ultrashort and intense pulses of X-rays produced at X-ray free electron lasers (XFELs) have enabled unique experiments on the atomic level structure and dynamics of matter, with time-resolved studies permitted in the femto- and attosecond regimes. To fully exploit them, it is paramount to obtain a comprehensive understanding of the complex nonlinear interactions that can occur at such extreme X-ray intensities. Herein, we report on the experimental observation of a resonant double-core excitation scheme in N 2 , where two 1σ core-level electrons are resonantly promoted to unoccupied 1π$^*_g$ molecular orbitals by a single few-femtosecond broad-bandwidth XFEL pulse. The production of these neutral two-site double core hole states is evidenced through their characteristic decay channels, which are observed in good agreement with high-level theoretical calculations. Such multi-core excitation schemes, benefiting from the high interaction cross sections and state- and site-selective nature of resonant X-ray interactions, should be generally accessible in XFEL irradiated molecules, and provide interesting opportunities for chemical analysis and for monitoring ultrafast dynamic processes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Chemical Recommender System: Replacement Suggestions for Small Molecules

The Chemical Recommender System (CRS) is an open-source, high-performance toolkit that enables real-time similarity searches across the complete PubChem database (over 50 million molecules) using commodity hardware. The CRS addresses critical limitations in existing chemical informatics platforms through a novel vector database infrastructure, extensible model integration capabilities, and complete algorithmic transparency. The system implements a vector database deployment with partitioned indexing that achieves a ~60x speedup over traditional approaches. A containerized model integration framework allows researchers to seamlessly incorporate custom predictive models into the full-scale search and scoring pipeline, while complete configurability of search parameters, filtering logic, and scoring functions provides capabilities not available in existing black-box solutions. Beyond structural similarity, the CRS integrates OPERA QSAR models for thermophysical and toxicity predictions, RDKit synthetic accessibility scoring, and user-defined models to compute weighted final replacement scores. The complete system is accessible through an interactive web application supporting real-time progress monitoring, post-processing score re-weighting, automated PDF reporting, and batch processing capabilities.

Nair, Parthiv Anand [Sandia National Laboratories ↗
DOE OSTI2024

A Scalable & Non-Destructive Characterization Strategy to Study Semiconductor/Dielectric Interfaces and Predict Wafer-Level Device Performance

The defect density present at the dielectric-semiconductor interface in an MOS structure directly influences the channel carrier characteristics in semiconductor devices, especially in wide bandgap material systems used in power devices. While these trap defects are typically quantified through electrical characterization of MOS-capacitor test structures, this treatment offers very little insight into the physical nature of interface defects. Such shortcomings demand a physical characterization strategy to guide fabrication optimization. X-ray photoelectron spectroscopy (XPS) is suggested as a viable technique to determine chemical data for dielectric interfaces formed using atomic layer deposition (ALD) on GaN substrates. Previously, 1-D XPS characterization has confirmed the presence of a Ga x O y interlayer between ALD dielectrics and the GaN substrate. In this work, XPS data is serially collected to form 2-D images of an ALD-Al 2 O 3 /GaN interface as a proof-of-concept experiment for in-situ XPS quality monitoring during ALD processing. The information provided by this work reveals some of the challenges for incorporating XPS characterization as an in-situ strategy during fabrication of GaN-based devices. Separately, electrical mapping of a 2-D array of ALD-Al 2 O 3 /GaN MOS-capacitor devices provide a means to quantify the spatial variations in interface quality across a single wafer. Physical characterization techniques, such as time-of-flight secondary ion mass spectroscopy, provide additional chemical information about the Al 2 O 3 /Ga x O y /GaN structure that complement the electrical mapping results. This analysis shows that a higher Ga x O y content correlates with higher interface state defects for trap energies deep in the band gap.

42 ENGINEERING↗
DOE OSTI2026

Stereo-vision thermal imaging system for tracking flying animals in wind farm areas (CRADA #763) Abstract

CRADA 763: abstract ThermalTracker-3D (TT3D) is a stereo vision thermal imaging system that provides 3D flight information on detected birds, bats, and other flying targets. The system was initially developed for use in the siting and monitoring of offshore wind projects to establish pre-construction and operation collision risk data but can be applied to terrestrial wind energy projects as well as national security monitoring. This technology will reduce monitoring cost, decrease processing time, and provide more accurate data for wind energy developers/operators and regulatory agencies. While the current technology is at a high level of readiness, Technology Readiness Level (TRL) 7, there remain several barriers to commercialization, particularly around ease-of-use, that result in a low Adoption Readiness Level (ARL). The proposed work will advance commercialization readiness by streamlining calibration methods for built systems. This work will:1. 1. develop a software package for factory and dynamic calibration processes 2. test that package with existing prototype TT3D systems, and 3. conduct outreach with industry end-users.

ThermalTracker↗
DOE OSTI2024

Simplified, Infiltration-free Ceramic Matrix Composite Manufacturing

Manufacturing of ceramic matrix composites (CMCs) with carbon fiber and carbon matrix includes a time- and labor-intensive ceramic infiltration step that is responsible for more than half of the total manufacturing cost. To make CMCs cost-competitive in price-sensitive markets, like concentrated solar power, it is essential to develop a CMC manufacturing process that skips the ceramic infiltration process. In this work, we will show how a high-char-yield preceramic resin can eliminate the infiltration step while maintaining material density and evaluate the technoeconomic impact of our CMC manufacturing process. Here, we monitor both morphology and porosity to determine the quality of CMCs made with different preceramic resins and evaluate the impact of polymer infiltration and pyrolyzing cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ACE-ENA: Fast Liquid Water Content

These data were collected during the Aerosol and Cloud Experiments in the Eastern North Atlantic field campaign as part of ARM Aerial Facility deployment (ACE-ENA, https://www.arm.gov/research/campaigns/aaf2017ace-ena). The ARM Aerial Facility Gulfstream-1 was deployed at Lajes Air Base (IATA: TER, ICAO: LPLA), on Terceira Island in the Azores, Portugal, for the two Intensive Observation Periods from June 20 through July 22, 2017 (IOP#1) and from January 11 through February 22, 2018 (IOP#2). The G-1 aircraft performed 20+19 research flights over the ARM Eastern North Atlantic (ENA) site and Atlantic Ocean to measure atmospheric turbulence, cloud water content and drop size distributions, aerosol precursor gases, aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50 Hz, 10 Hz, and 1 Hz.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CACTI: Fast Liquid Water Content

These data were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI; https://www.arm.gov/research/campaigns/amf2018cacti ) field campaign in the Sierras de Córdoba mountain range of north-central Argentina as part of the ARM Aerial Facility (AAF) deployment. The ARM Aerial Facility Gulfstream-1 was operated from Las Higueras Airport (IATA: RCU, ICAO: SAOC), Río Cuarto, Córdoba, Argentina, for the Intensive Observation Period (IOP) from Nov. 1 through Dec. 15, 2018. The G-1 aircraft performed 22 research flights over the first ARM Mobile Facility (AMF1) location in the Sierras de Córdoba mountain range to measure atmospheric state and turbulence, cloud water content and droplet size distributions, aerosol precursor gases, and aerosol chemical composition and size distributions. The current data set presents re-processed Particle Volume Monitor PVM-100A (aka Gerber probe) data: Liquid Water Content (LWC), Particle Surface Area (PSA), and the effective droplet radius (re) averaged to 50Hz, 10Hz, and 1Hz.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Silicon Carbide Passive Temperature Sensors at INL

Discuss the SiC passive thermometry methods currently performed at INL and ORNL, decide on best path forward for establishing an ASTM Standard for processing SiC temperature monitors, and expand this ASTM benchmarking efforts to international collaborators (Netherlands, Canada, and Poland).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2024

Autoregressive distributed lag-based dynamic uniformity modeling and monitoring approaches for superconductor manufacturing

High-temperature superconductors (HTS), known for their high efficiency and low energy loss, have found profound applications across various fields, driving the demand for long, uniformly performing tapes. However, ensuring uniform performance over extended lengths of HTS tapes, often characterized by the consistency of critical current, remains challenging due to fluctuations in growth conditions during manufacturing. Here, to elucidate the mechanisms underlying variations in tape uniformity and enable real-time monitoring of associated parameters, we propose an Autoregressive Distributed Lag (ADL)-based Dynamic Uniformity Modeling and Monitoring (ADUM2) approach. This method integrates uniformity measurement, the identification of critical process parameters and real-time monitoring within the manufacturing process. The ADUM2 approach is applied to the advanced metal organic chemical vapor deposition (A-MOCVD) process, a pilot-scale method for superconductor manufacturing. Our model demonstrates superior performance compared to benchmark methods, accounting for over 80% of the total variance in the data and identifying 13 key process parameters influencing the uniformity of HTS tapes. This study offers significant insights into the high-temperature superconductor manufacturing process and holds the potential to facilitate the production of cost-effective, uniformly performing long superconducting tapes in the future.

autoregressive distributed lag analysis↗
DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗
DOE OSTI2025

Application of Advanced Materials Processing to Enable Direct Production of Fast Reactor Fuel Alloys

Argonne National Laboratory (the Contractor), located in Lemont, IL, and Oklo, Inc. (the Participant), headquartered in Sunnyvale, CA, entered into a Cooperative Research and Development Agreement (CRADA) to integrate advanced electrorefining co-deposition and molten-salt monitoring technologies to produce a uranium-transuranic (U/TRU) alloy within a controlled composition range that can be used as fast reactor fuel for Oklo, Inc.’s advanced reactor technology. Argonne performed the integration of electrorefining and process monitoring technologies, determined operating parameters for producing U/TRU alloys, and developed optimized design and operating parameters for co-deposition of U/TRU alloys. Oklo, Inc. worked with Argonne and the Nuclear Regulatory Commission (NRC) to provide the necessary process documentation to begin implementing pyroprocessing technology in the fast reactor fuel production process. Outcomes of this project included a pilot-scale co-deposition cathode design and technical basis for the operation of the co-deposition electrorefiner to produce U/TRU alloys with controlled composition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2024

PMU Data Quality and Sensor Health Monitoring

Phasor Measurement Units (PMUs) play a critical role in the evolution of the electric power industry by providing high-precision, real-time monitoring of essential power system metrics. However, effectively detecting abnormalities and critical events from PMU data is a complex task, complicated by intricate temporal patterns, a scarcity of labeled data for training algo- rithms, and constraints on online computational power. In this study, we apply TranAD, an innovative algorithm that combines transformer architectures with the refinement of adversarial learning, to both synthetic and real-world PMU datasets for developing a data quality and sensor online health monitoring platform for utilities. Our findings reveal that TranAD not only provides efficient detection and localization but also enhances the detail with which abnormalities are detected, marking a a significant step forward in the field of clean data acquisition processes for power system monitoring

deep neural network, machine learning (ML)↗