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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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Extract material signatures from buildings (façade if old construction, internal materials if under construction) using both satellite and drone platforms and using those extracted signatures to classify individual buildings by their structural composition and intended purpose.
It is noted that a knowledge of the thermal inertia of the earth's surface can be used in geologic mapping as a complement to surface reflectance data as provided by Landsat. Thermal inertia, which is a body property, cannot be determined directly but can be inferred from radiation temperature measurements made at various times in the diurnal heating cycle, combined with a model of the surface heating processes. A model of this type is developed and applied along with temperature measurements made in the field and by satellite to determine thermal properties of surface materials. An example from a test site in western Nevada is used to demonstrate the utility of this technique.
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Nondamaging process similar to electrochemical machining is useful in obtaining metal samples from places inaccessible to conventional sampling methods or where methods would be hazardous or contaminating to specimens. Process applies to industries where metals or metal alloys play a vital role.
In the pursuit of a higher fidelity deterministic simulation capability of the Advanced Test Reactor, it is important to have a fast yet accurate deterministic neutronics model. Here, to achieve this, we employed an advanced two-step method. The first step involves generating homogenized cross sections using OpenMC, a cutting-edge Monte Carlo neutron transport code. OpenMC offers excellent modular capabilities, allowing for easy component integration and flexibility in incorporating new designs into the model. The second step involves deterministic transport calculations, which are performed using Griffin, a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). To ensure the accurate spatial resolution and assignment of material cross sections, a Cubit-generated mesh for the Advanced Test Reactor is utilized as an intermediate step between the OpenMC and Griffin models; Griffin utilizes the mesh for its finite element solution, while OpenMC material identifications are written to the mesh file to be used in Griffin material assignments. Additionally, a Python-based script converts the cross sections generated by OpenMC into the ISOXML format required by Griffin. Initial comparisons using the Griffin diffusion solver indicated good agreement between the neutron multiplication factors obtained from the standalone OpenMC model and the Griffin model, with differences of less than 10 pcm in the 2D geometry configuration; it was later determined that this agreement was likely due to compensating effect and was more likely on the order of –700 pcm relative to the OpenMC solution. However, in three-dimensional calculations, an unacceptably large error (almost 8,000 pcm) was found in the Griffin solution with the diffusion solver. Subsequent calculations using Griffin’s discrete ordinates solver demonstrated substantially improved agreement, within 116 pcm of the OpenMC solution used to generate the cross sections for Griffin. Building on this capability, future work will seek to perform more detailed validation calculations. The ultimate goal is to evaluate both transient and multiphysics simulations of the reactor.
Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.
A long-range program in support of the aerospace industry aimed at reducing the use of strategic materials in gas turbine engines is discussed. The program, which is called COSAM (Conservation of Strategic Aerospace Materials), has three general objectives. The first objective is to contribute basic scientific understanding to the turbine engine technology bank so that our national security is not jeopardized if our strategic material supply lines are disrupted. The second objective is to help reduce the dependence of United States military and civilian gas turbine engines on worldwide supply and price fluctuations in regard to strategic materials. The third objective is, through research, to contribute to the United States position of preeminence in the world gas turbine engine markets by minimizing the acquisition costs and optimizing the performance of gas turbine engines. Three major research thrusts are planned: strategic element substitution; advanced processing concepts; and alternate material identification. Results from research and any required supporting technology will give industry the materials technology options it needs to make tradeoffs in material properties for critical components against the cost and availability impacts related to their strategic metal content.
This document describes results from the WAMOR Chesapeake Bay collection in October 2024, a data collection from the Arkeus HSOR hyperspectral sensor. The calibration array was staged at USCG Station Little Creek, Virginia. With the provided HSOR imagery and data, our analysis does not show a capability to effectively detect materials of interest from non-materials of interest as well as differentiate materials of interest from each other, the materials of interest being polypropylene, polyester, and cotton. The fundamental reasons are (1) the spectral bands of HSOR do not reside in specific wavelength locations to allow for material identification and (2) the provided imagery appears to not have consistent band-to-band relationships indicative of a lack of calibration (gain/offsets) or some other calibration issues. With regard to analyzing a subset of the delivered HSOR, HSOR can make general statements about materials not being water and have the capability to make color assessments.
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.
The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).
This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.
Bioelectrochemical energy conversion, discussing applications for toxic material identification, powering cardiac pacemakers and electric power generation
Fabrication of probes from a representative selection of materials used to make thermocouples identifies materials used in thermocouple junctions. Generating a thermoelectric electromotive between hot and cold junctions verifies whether or not the material in question is the same as the probe.
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Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS 2 -WS 2(1–x) Se 2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. In conclusion, this advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.
Effective inventory management can be overshadowed by conflicting priorities in organizational procedures, particularly in research-focused institutions such as national laboratories that handle expensive, delicate, and hazardous materials. Here, this study investigated the potential of radiofrequency identification (RFID) technology, currently used for hazardous chemical inventory, in applications with higher metal interference and absorption, specifically pressure release device (PRD) compliance and nuclear container management, at Lawrence Livermore National Laboratory (LLNL). This study was done to document best practices to enhance inventory identification speeds for inventory reconciliation and inventory recall and to explore optimal configurations for RFID implementation compared to traditional manual methods of equipment management. Tests were conducted to determine the ideal RFID tag orientation (read at angles of 0°, 90°, and 270°), various container layouts (linear, separated, curved, operational), and ID methods such as manual, barcode, and RFID performing three trials per method per orientation. Results indicated that 0° was the optimal read angle for minimizing metallic interference, and the operational and curved arrangements significantly outperformed the linear and separated configurations in read speed. 3D printed mounts were developed and tested, increasing the read range of the RFID reader by up to 235% in cases of high metallic interference. The RFID technology demonstrated an average speed increase of 65% over a simplified manual identification, which supports the conclusion that RFID is a more efficient method for large hazardous inventory management and equipment reconciliation. Additionally, capturing meta-data, such as location and date, can be used to query for inventory recall and automated updating of record information.
Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.
Identification of volatile contaminants of space cabin materials