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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nanopore Activity Assays for Detection of Biomarker Protease Activity: Design and Testing of Substrates for Both Nanopore Sequencing and PCR-Based Detection Methods

The work performed in this project has demonstrated the ability to construct proteolytic enzyme substrates that are PCR and sequencing-readable reporter molecules. Specifically, the goal was to detect those reporter molecules via PCR and Oxford Nanopore Technologies MinION sequencing methods following exposure to the biomarker protease thrombin. The assay development focused on binding the constructed peptide-oligonucleotide chimera to immobilized streptavidin. The action of thrombin on the peptide portion of the molecule released the oligonucleotide for detection. Detection of protease activity was demonstrated in a concentration-dependent manner using MALDI-MS, RT-PCR and DNA sequencing. Additional steps to remove background release of reporter molecules during the assay was used to improve the difference in detected oligonucleotide reporter following protease activity. Additional steps in assay development will be to (1) test the assay in an appropriate matrix, (2) investigate detection using additional DNA sequencing platforms and (3) demonstrate multiplexed detection of multiple protease markers in a single reaction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Nondestructive Damage Detection of Concrete With Alkali-Silica Reactions Using Coda Wave and Anomaly Detection

An anomaly detection model for early damage detection for concrete structures undergoing alkali-silica reaction (ASR) is presented. It is difficult to detect ASR initiation and early damage without a reference expansion measurement. Coda waves, or the multiply scattered portion of ultrasonic waves, have been found to be indicative of small changes in complex material such as concrete. The relationship between concrete damage and relative velocity change and decorrelation of coda waves has been studied, but a generalized model which detects when damage occurs in a concrete structure is still lacking. The presented method uses features extracted from coda waves to detect early damage in concrete structures. The model uses unsupervised learning and only requires data from undamaged structures for training. During the training process, the reconstruction error of the training data is minimized. When the data collected from damaged concrete structures is used as an input of the model, it returns high reconstruction errors that indicate the occurrence of damage in the structures. The performance of the model is validated using experimental studies and has been shown to generalize across two different ASR specimens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Sensitivity and Detection Limit of Spectroscopic-Grade Perovskite CsPbBr 3 Crystal for Hard X-Ray Detection

We report spectroscopic-grade single crystal detectors can register the energies of individual X-ray interactions enabling photon-counting systems with superior resolution over traditional photoconductive X-ray detection systems. Current technical challenges have limited the preparation of perovskite semiconductors for energy-discrimination X-ray photon-counting detection. Here, this work reports the deployment of a spectroscopic-grade CsPbBr 3 Schottky detector under reverse bias for continuum hard X-ray detection in both the photocurrent and spectroscopic schemes. High surface barriers of approximate to ≈ 1 eV are formed by depositing solid bismuth and gold contacts. The spectroscopic response under a hard X-ray source is assessed in resolving the characteristic X-ray peak. The methodology in enhancing X-ray sensitivity by controlling the X-ray energies and flux, and voltage, is described. The X-ray sensitivity varies between a few tens to over 8000 μC Gy air -1 cm -2 . The detectable dose rate of the CsPbBr 3 detectors is as low as 0.02 nGy air s -1 in the energy discrimination configuration. Finally, the unbiased CsPbBr 3 device forms a spontaneous contact potential difference of about 0.7 V enabling high quality of the CsPbBr 3 single crystals to operate in "passive" self-powered X-ray detection mode and the X-ray sensitivity is estimated as 14 μC Gy air -1 cm -2 . The great potential of spectroscopic-grade CsPbBr 3 devices for X-ray photon-counting systems is anticipated in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗
DOE OSTI2022

Improving Non-Destructive Detection Technology Through SAVY Feature Detection

Surveillance of special nuclear material (SNM) storage containers is required by the DOE to assess their integrity across the Complex. This work aims to improve the task of container inspections by leveraging automation through machine learning (ML) tools to reduce the human-intensive effort and expert-level knowledge needed to assess container status. A field-deployable, non-destructive technology was designed using off-the-shelf components to collect multiple images from different perspectives of containers in storage to detect both spatial features of interest and anomalies of concern. Nine ML models were generated using unique training datasets and parameters. Learned features include SAVY surface regions including the body side wall, collar, lid, filter, and printed/etched information. Average Precision (AP) is used to calculate detection performance when both viewing previously seen environments and previously unseen environments. The application of image transformations and resolution scaling while training greatly improved the detection performance in unseen environments, and significantly increasing the number of computation iterations improved detection performance on previously seen environments. Additional capabilities were developed including the novel detection of procedural non-compliance and the ability to localize anomalies relative to SAVY surface features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Demonstration of Selective Single-Barium Ion Detection with Dry Diazacrown Ether Naphthalimide Turn-on Chemosensors

Single-molecule fluorescence imaging (SMFI) of gas-phase ions has been proposed for “barium tagging,” a burgeoning area of research in particle physics to detect individual barium daughter ions. This has potential to significantly enhance the sensitivity of searches for neutrinoless double-beta decay (0νββ) that is obscured by background radiation events. The chemistry required to make such sensitive detection of Ba 2+ by SMFI in dry Xe gas at solid interfaces has implications for solid-phase detection methods but has not been demonstrated. Here, we synthesized simple, robust, and effective Ba 2+ -selective chemosensors capable of function within ultrapure high-pressure 136 Xe gas. Turn-on fluorescent naphthalimide-(di)azacrown ether chemosensors were Ba 2+ -selective and achieved SMFI in a polyacrylamide matrix. Here, fluorescence and NMR experiments supported a photoinduced electron transfer mechanism for turn-on sensing. Ba 2+ selectivity was achieved with computational calculations correctly predicting the fluorescence responses of sensors to barium, mercury, and potassium ions. With these molecules, dry-phase single-Ba 2+ ion imaging with turn-on fluorescence was realized using an oil-free microscopy technique for the first time—a significant advance toward single-Ba 2+ ion detection within large volumes of 136 Xe, plausibly enabling a background-independent technique to search for the hypothetical process of 0νββ.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
DOE OSTI2023

Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection

As the operational time window of experimental rotating detonation engines (RDEs) is expanded and the technology matures toward integration within gas turbines, monitoring techniques must evolve to offer computationally efficient and highly time-resolved diagnostics. In this study, computer vision object detection methodology that seeks to reduce data processing time and calculate wave velocity within drastically reduced time intervals as compared to traditional high-frame-rate RDE images analysis techniques is proposed. The adapted you-only-look-once object detection network is trained to detect individual detonation waves within single down-axis RDE images. The wave location and rotational direction detected within a frame are tracked through a series of high-speed images to calculate the frame-to-frame wave velocity with the time-step resolution of $\mathrm{20 μs}$ across a series of frames. The analysis of the annotation box size and image linearization effects is presented, demonstrating the lowest frame-to-frame velocity total uncertainty of $\mathrm{±3.8\%}$ and the highest classification speed of 9.5 frames per second using linearized images. Linearized images “unwrap” the RDE annulus pixel region to a reduced image size. Here, this new method offers great reductions in data processing times and unsteady detonation behavior insight at intervals more comparable to the timescales of detonation wave interactions via the application of machine learning to experimental RDE data.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Dynamical Classification of Trans-Neptunian Objects Detected by the Dark Energy Survey

The outer solar system contains a large number of small bodies (known as trans-Neptunian objects or TNOs) that exhibit diverse types of dynamical behavior. The classification of bodies in this distant region into dynamical classes—subpopulations that experience similar orbital evolution—aids in our understanding of the structure and formation of the solar system. In this work, we propose an updated dynamical classification scheme for the outer solar system. This approach includes the construction of a new (automated) method for identifying mean motion resonances. We apply this algorithm to the current data set of TNOs observed by the Dark Energy Survey (DES) and present a working classification for all of the DES TNOs detected to date. Our classification scheme yields 1 inner centaur, 19 outer centaurs, 21 scattering disk objects, 47 detached TNOs, 48 securely resonant objects, 7 resonant candidates, and 97 classical belt objects. Among the scattering and detached objects, we detect 8 TNOs with semimajor axes greater than 150 au.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery

In this paper we propose IRTR-DETR an Interactive and Real-Time Rotated DEtection TRansformer that extends IRTDETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTDETR but introduces rotation-aware heads for improved detection of objects with arbitrary orientations. Similarly to IRTDETR IRTR-DETR can be trained with a small labeled sample set in an interactive setting but we show that it can also be pretrained on related but not identical data--such as a building damage dataset--before being applied to tasks like identifying buildings under construction. We demonstrate the efficacy of our approach on the publicly available Tiny-DOTA and xBD dataset as well as two study-cases on proprietary datasets of greenhouses and houses under construction ("waffle homes"). Detecting greenhouses is highly relevant in the context of damage assessment while "waffle homes" aid understanding typical floorplans and building codes in different areas both thereby supporting population modeling emergency response and policy planning. Our method outperforms the state of the art in interactive rotated object detection on the Tiny-DOTA dataset by 5.7 percent and improves upon the non interactive RTDETR by 7.85 to 19.39 percent (depending on the number of provided samples) while maintaining its real-time efficiency.

Burges, Marvin [ORNL] (ORCID:0000000312690769)↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2022

Bimetallic Metal–Organic Framework Fe/Co-MIL-88(NH 2 ) Exhibiting High Peroxidase-like Activity and Its Application in Detection of Extracellular Vesicles

Metal–organic frameworks (MOFs) have many attractive features, including tunable composition, rigid structure, controllable pore size, and large specific surface area, and thus are highly applicable in molecular analysis. Depending on the MOF structure, a high number of un-saturated metal sites can be exposed to catalyze chemical reactions. In the present work, we report that by using both Co(II) and Fe(III) to prepare the MIL-88(NH 2 ) MOF, we can produce the bimetallic MOF that can catalyze the conversion of 3,3', 5,5"-tetramethylbenzidine (TMB) to a color product through reaction with H 2 O 2 at a higher reaction rate than the monometallic Fe-MIL-88(NH 2 ). The Michaelis constants (K m ) of the catalytic reaction for TMB and H 2 O 2 are 3-5 times smaller, and the catalytic constants (k cat ) are 5-10 times higher than those of the horse-radish peroxidase (HRP), supporting ultrahigh peroxidase-like activity. These values are also much more superior to those of the HRP-mimicking MOFs reported previously. Interestingly, the bimetallic MOF can be coupled with glucose oxidase (GOx) to trigger the cascade enzymatic reaction for highly sensitive detection of extracellular vesicles (EVs), a family of important biomarkers. Through conjugation to the aptamer that recognizes the marker protein on EV surface, the MOF can help isolate the EVs from biological matrices, which are subsequently labeled by GOx via antibody recognition. The cascade enzymatic reaction between MOF and GOx enables detection of EVs at a concentration as low as 7.8 × 10 4 particles/ml. Further, the assay can be applied to monitor EV secretion by cultured cells, and also can successfully detect the different EV quantities in the sera samples collected from cancer patients and healthy controls. Overall, we prove that the bimetallic Fe/Co-MIL-88(NH 2 ) MOF, with its high peroxidase-activity and high biocompatibility, is a valuable tool deployable in clinical assays to facility disease diagnosis and prognosis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Detection and Quantitation of Hydrogen Emissions Role and Status of Detection Technology

Hydrogen is a critical strategy to decarbonize energy and manufacturing industries. Hydrogen is nontoxic and can be handled safely, but potential for secondary greenhouse impacts. Hydrogen releases arise from a variety of mechanisms (process, design features, "leaks") that contribute to total hydrogen releases. Detection methodologies will be critical to detect and quantify hydrogen emissions. Detection is to be integrated with advanced analytics (AI) and behavior modelling to effectively identify, quantify, and source locate hydrogen releases. Modelling of emissions will contribute to facility safety and reliability. DOE is committed to develop the tools to model and mitigate the impact of hydrogen releases which include: Support modeling to elucidate released hydrogen degradation; Support the development of tools for emissions quantitation; and Support engineering advancements to minimize hydrogen losses along the value chain (including process, design features, and leaks).

detection↗
DOE OSTI2024

A comparison of the neutron detection efficiency and response characteristics of two pixelated PSD-capable organic scintillator detectors with different photo-detection readout methods

Here, we characterize the performance of two pixelated neutron detectors: a PMT-based array that utilizes Anger logic for pixel identification and a SiPM-based array that employs individual pixel readout. The SiPM-based array offers improved performance over the previously developed PMT-based detector both in terms of uniformity and neutron detection efficiency. Each detector array uses PSD-capable plastic scintillator as a detection medium. We describe the calibration and neutron efficiency measurement of both detectors using a 137 Cs source for energy calibration and a 252 Cf source for calibration of the neutron response. We find that the intrinsic neutron detection efficiency of the SiPM-based array is (30.2 ± 0.9)%, which is almost twice that of the PMT-based array, which we measure to be (16.9 ± 0.1)%.

47 OTHER INSTRUMENTATION↗