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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scintillators

As concerns about the illicit movement of radioactive materials across international borders increase, so too has the need for increased protection of those borders both foreign and domestic. The challenge is not only to detect hidden radioactive materials, but also to distinguish them from legitimate radionuclides such as radio-pharmaceuticals that are often transported across borders and shipped throughout a country. With more than 600 U.S. border sites to protect, screening imported radioactive material requires a careful balance of high throughput and high search efficiency. However, these requirements are difficult to meet as rapid screening operations leave less time for radiation detectors to efficiently evaluate materials. In support of border security, Sandia developed organic glass scintillators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Evaluation of the Turbine Integrated Mortality Reduction (TIMR SM ) Technology as a Smart Curtailment Approach (Final Summary Report)

Wind energy is a crucial technology for achieving net-zero emissions by 2050. However, the growth and deployment of wind energy in North America have led to the deaths of many bat species due to operating wind turbines. Hundreds of thousands of bats are estimated to die at wind turbines annually in North America. Operational minimization, which includes feathering turbine blades and curtailment, has been documented to reduce bat fatality effectively. Curtailment refers to altering turbine operation based on wind speed, time of year, temperature, sensors, and activity models. However, when turbines are curtailed, they do not generate power, resulting in energy loss and revenue for wind energy facilities. The Electric Power Research Institute (EPRI) funded the development of Turbine Integrated Mortality Reduction (TIM SM ) Technology, which curtails turbine operation when bats are detected. The initial TIMR system research showed promising results, with an 85% reduction in overall bat fatalities and a 91% reduction for the little brown bat. However, these results were based on a single site during one fall season, and it was unclear if similar results could be replicated at other wind energy facilities. This research aimed to validate the TIMR system results from the prior field study at a second site in the U.S., estimate the power production and reduction in bat mortality at turbines with installed TIMR systems relative to blanket curtailment and fully operational turbines, test the TIMR system in two calendar years and during the summer and fall periods, and evaluate the operational and commercial characteristics of the TIMR system for potential wind industry adoption. The study was conducted at a 500.9-MW wind energy facility in southeast Adair County, Iowa. Three experimental treatments were involved in this randomized block design study: TIMR, Curtailment at 5.0 m/s, and Normal Operation. In 2021, three treatments were used at 18 turbines, expanding to four treatments across 36 turbines in 2022. The TIMR system worked as designed throughout the entire study; however, because of unexpected wind turbine operational challenges in 2021, there was not sufficient sample size to evaluate the treatment differences. In 2022, there were significant differences in fatality levels between treatment types and normal operating turbines. Curtailment at 5.0 m/s reduced fatalities by 30.8% compared to normal operations, and TIMR decreased fatalities by 48.6% compared to normal operations. Two different methods were used to evaluate the differences in energy loss for each treatment. The TIMR system resulted in 1.3% to 1.6 % annual energy loss in 2021 and 1.0% to 1.2 % in 2022. The Curtailment at 5.0 m/s resulted in 0.6% to 0.8 % annual energy loss in 2021 and 0.5% to 0.6 % in 2022. The project achieved all the stated objectives and demonstrated that TIMR is an effective technology that balances bat fatality reduction with energy generation. The results will support the deployment of TIMR and other acoustic sensor-based technologies. The research provides valuable insights into the impact of different treatments on fatality rates and energy outputs, contributing to the ongoing efforts to mitigate the environmental impact of wind energy.

17 WIND ENERGY↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
DOE OSTI2025

Pilot-Scale Validation of Distributed Optical Fiber Sensors for Underground Pipeline Monitoring

Distributed fiber optic sensing is a cutting-edge technology that has found extensive applications in the monitoring of Ensuring the safety, integrity, and operational efficiency of underground product pipelines is vital for maintaining the nation’s critical infrastructure. Monitoring parameters such as hoop strain, pressure, and acoustic vibrations is key to detecting potential leaks, intrusions, or structural issues. Distributed optical fiber sensor (DOFS) systems provide a compelling solution for continuous, real-time monitoring over long distances. This paper details the development and pilot-scale implementation of DOFS systems for underground pipeline monitoring, evolving from a proof-of-concept stage. Multiple custom-designed DOFS interrogator units—such as optical frequency-domain reflectometry (OFDR), Brillouin optical time-domain analysis (BOTDA), and multimodal interferometer-based fiber acoustic sensors—were employed to measure key parameters like hoop strain, pressure, and acoustic vibrations. The underground product pipeline's outer diameter is 30 inches, the wall thickness is 1.28 inches, and the 3-foot depth. The fiber deployment strategies, and sensing data acquisition methods for these systems are discussed. The results demonstrate the effectiveness of DOFS in detecting hoop strain, temperature changes, and acoustic vibrations, showcasing their potential for real-time monitoring and enhancing pipeline safety.

distributed fiber sensing↗
DOE OSTI2025

STILGAR End-of-Project Report

The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.

58 GEOSCIENCES↗
DOE OSTI2022

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™

Ultra-high performance liquid chromatography–high-resolution mass spectrometry (UPHLC–HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000’s of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

environmental monitoring↗
DOE OSTI2020

Innovation in Radiological Security, Part 1 of 2 – Using a Cloud Solution to Compress the Timely Detection Model

The design and implementation of new security technologies must account for numerous and complex operational challenges. For example, traditionally isolated systems must now survive amid the proliferation of network connectivity and endure the dynamic environments created by organizations tolerating bring-your-own-device policies. When evaluating the cyber, physical, or cyber-physical security of an asset, a common practice in the security industry is to apply the Timely Detection Model (TDM)—a versatile concept that relates the security functions of detection, delay, and response to the progression of an oppositional force’s attack timeline. If implemented correctly, security enhancements can compress the TDM to offer efficiencies to the stakeholders responsible for adjudicating threat scenarios. The efficiencies gained by the response force allow for more effective protection strategies to be realized. One such enhancement, deployed within the radiological security domain—the Sentry-Remote Monitoring System (Sentry-RMS)—is a stand-alone security system that detects, assesses, and communicates priority alarms as a means of thwarting internal and external threats. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform—an optional feature of the Sentry-RMS—is being developed to facilitate more efficient alarm adjudication by site stakeholders and, if necessary, a faster response by law enforcement. This Cloud-hosted platform receives protected information from deployed Sentry-RMS units and relays it to stakeholders that have vested interests in maintaining an elevated level of situational awareness. Operationally, this enables real-time delivery of high-priority alarm and video imagery directly to an identified response stakeholder, such as local law enforcement or site management, via natively developed mobile applications or full platform integration. The Sentry-SECURE platform, as implemented by the U.S. Department of Energy’s Office of Radiological Security, is an example of an innovative security technology that compresses the TDM by (1) enabling a more efficient time to target and (2) better informing a response force’s predetermined tactics, techniques, and procedures. This paper explores the platform’s operational roles, highlights its principal functions, and presents a use case that demonstrates how the platform provides enhanced situational awareness when adjudicating priority alarms.

Sentry-RMS, Timely Detection Model, Sentry-SECURE↗
DOE OSTI2026

Genetic and epigenetic signatures of Populus trichocarpa in response to abiotic stresses

Plants respond to abiotic stresses such as drought, heat, and salinity through both shared and stress-specific regulatory pathways. However, the role of epigenetic mechanisms, particularly DNA methylation, in modulating these responses is still underexplored. Here, we integrated transcriptome profiling with whole-genome bisulfite sequencing (WGBS) to investigate the dynamic relationship between gene expression and DNA methylation in Populus trichocarpa during brief early-stage exposure to abiotic stress. Each stress elicited distinct transcriptional and methylation signatures; however, a subset of conserved stress-responsive genes was commonly regulated across treatments. We found that ∼5-6% of differentially expressed genes also displayed differential methylation patterns, suggesting a coordinated role for DNA methylation in regulating gene expression. Motif enrichment analysis of differentially methylated regions revealed binding sites of key transcription factor families, including ERF, bHLH, and ABF, highlighting potential role for methylation in modulating transcription factor targeting. Furthermore, we identified stress-inducible molecular markers with potential applications in early stress detection and functional dissection of gene regulatory pathways. Together, these findings provide new insights into the coordinated genetic and epigenetic responses to abiotic stress and provide a foundation for developing biosensors and breeding strategies to enhance stress resilience woody plants.

DNA methylation↗
DOE OSTI2025

Confronting Large‐Eddy Simulations With Stereo Camera Data by Means of Reconstructed Hemispheric Cloud Size Distributions

High-resolution hemispheric camera images at a meteorological site in western Germany are used to analyze the multi-dimensional spatial characteristics of continental cumulus cloud fields, and to evaluate Large-Eddy Simulations on this aspect. Traditional non-hemispheric cloud-detecting instruments provide additional reference data. The main model-observation comparison focuses on cloud size distributions (CSDs), employing two methods: (a) directly using three-dimensional model fields, direct CSDs, and (b) using rendered hemispheric images of the model fields as produced by a camera simulator based on path-tracing. In the latter method, both the real and rendered images are used to three-dimensionally reconstruct the cloud fields, yielding hemispheric CSDs. Advantages of hemispheric comparisons over more classic approaches include (a) fair comparisons between model and data, and (b) full use of the enhanced resolutions and hemispheric spatial coverage of the camera imagery. Basic evaluation of the simulations demonstrates good agreement on thermodynamic structure and its diurnal cycle. Cloud heights and cloud cover are intercompared between the model, camera data and other instrumentation, providing insight into their structural differences. A consistent alignment is found between the hemispheric CSDs from both the model and the cameras. Power law fits reveal structurally lower exponents in hemispheric CSDs compared to non-hemispheric CSDs, which particularly caution against directly comparing hemispheric CSDs to non-hemispheric distributions. This result is robust for sample size and fitting method. These findings inform future use of hemispheric camera systems for studying cumulus cloud field morphology and model evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Metal-organic frameworks as effective sensors and scavengers for toxic environmental pollutants

Abstract Metal-organic frameworks (MOFs) constructed from a rich library of organic struts and metal ions/clusters represent promising candidates for a wide range of applications. The unique structure, porous nature, easy tunability and processability of these materials make them an outstanding class of materials for tackling serious global problems relating to energy and environment. Among them, environmental pollution is one aspect that has increased at an alarming rate in the past decade or so. With rapid urbanization and industrialization, toxic environmental pollutants are constantly released and accumulated leading to serious contamination in water bodies and thereby having adverse effects on human health. Recent studies have shown that many toxic pollutants, as listed by the World Health Organization and the US Environmental Protection Agency, can be selectively detected, captured, sequestered and removed by MOFs from air and aquatic systems. Most of these sensing/capture processes in MOFs are quantifiable and effective for even a trace amount of the targeted chemical species. The functional sites (ligands and metals) play a critical role in such recognition processes and offer an extensive scope of structural tunability for guest (pollutants, toxic entities) recognition. Whereas on the one hand, the underlying mechanisms governing such sensing and capture are important, it is also crucial to identify MOFs that are best suited for commercial applications for the future. In this review article, we provide an overview of the most recent progress in the sensing, capture and removal of various common toxic pollutants, including neutral and ionic, inorganic and organic species, with brief discussions on the mechanism and efficacy of selected MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Overcoming photobleaching in imaging of single barium atoms in a solid xenon matrix

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is 136 Xe, which would double beta decay into 136 Ba. Detecting the single 136 Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Developing a remediation framework

When harmful contaminants are detected in soil, sediment, or groundwater (or suspected due to industrial activities), remediation efforts may be required to protect human health and the environment. This chapter will introduce and discuss the steps in the general site remediation process that are key when approaching any remediation strategy, regardless of the site complexity. While these steps are described in a linear fashion, in most cases, the approach is more fluid and iterative depending on the desired outcomes and success of each progressive clean-up effort along the way. This chapter will additionally articulate some key considerations which may be important to the overall remediation strategy depending on the particular needs and desired outcomes. From these more general discussions, the chapter will branch into a case-study of the remediation approach, or process steps, previously/currently/in the future taking place at the Hanford Site, as one example of a complex waste sites in the United States. As indicated earlier, while a remediation strategy can be more simply described by a progression of general process steps, it is far more likely to observe fluidity and strategy evolution over time from the first conceived strategy as remediation progress occurs. The chapter concludes with a brief discussion of how the strategy at the Hanford Site has evolved to provide a singular example of what this evolution might look like for other complex waste sites.

Muller, Katherine A.↗
DOE OSTI2020

Small Mammal Prey Base for American Marten (Martes americana) within the Manistee National Forest of Michigan

American martens (Martes americana) are typically found in late-successional forests with closed canopy cover and high structural diversity. Reintroduced populations of martens in the Northern Lower Peninsula of Michigan inhabit areas that are devoid of many of these features, which may impact their prey base. The goal of our study was to evaluate the small mammal prey base available to martens in the Northern Lower Peninsula. To assess prey availability, diversity, and composition, as well as effects of trap type and habitat features on trapping success, we sampled 24 study plots within the Manistee National Forest for small mammals in 2013 (n=24) and 2014 (n=20). Study plots were situated in four habitat types: conifer, deciduous, mixed conifer-deciduous, and mixed oak. Total capture rates were significantly and positively associated with relative deciduous tree cover. Furthermore, this result highlights how managing tracts of land for small mammal prey base may overlap with goals set forth by researchers for marten habitat needs (e.g., resting site preferences). We found large Sherman traps had significantly higher total capture rates than other trap types (medium Sherman and pitfall traps), and we recommend that researchers use a variety of trap types to maximize detectability of small mammal species diversity and richness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Genetic_and_epigenetic_signatures_of_Populus_Trichocarpa_in_response_to_abiotic_stress

Plants respond to abiotic stresses such as drought, heat, and salinity through both shared and stress-specific regulatory pathways. However, the role of epigenetic mechanisms, particularly DNA methylation, in modulating these responses is still underexplored. Here, we integrated transcriptome profiling with whole-genome bisulfite sequencing (WGBS) to investigate the dynamic relationship between gene expression and DNA methylation in Populus trichocarpa during brief early-stage exposure to abiotic stress. Each stress elicited distinct transcriptional and methylation signatures; however, a subset of conserved stress-responsive genes was commonly regulated across treatments. Approximately 5% of differentially expressed genes also displayed differential methylation patterns, suggesting a coordinated role for DNA methylation in regulating gene expression. Motif enrichment analysis of differentially methylated regions revealed binding sites of key transcription factor families, including ERF, bHLH, and ABF, highlighting potential role for methylation in modulating transcription factor targeting. Furthermore, we identified stress-inducible molecular markers with potential applications in early stress detection and functional dissection of gene regulatory pathways. Together, these findings provide new insights into the coordinated genetic and epigenetic responses to abiotic stress and provide a foundation for developing biosensors and breeding strategies to enhance stress resilience woody plants.

CBI↗
DOE OSTI2025

Smartphone application for visualizing building air leakage

Annually, unwanted air leakage through building envelopes accounts for 4 quads of energy consumption in the United States, which translates to about 10% of total building energy consumption. Locating and sealing leakage sites is crucial fordecreasing building energy consumption. Smartphones are ubiquitous and contain sophisticated cameras and highperformance processors that could be employed to visualize air leakage using the background-oriented schlieren imaging technique, making leak detection cheaper and easier. This technique requires a textured and high-contrast background such as a brick or concrete masonry unit wall, a building air leak that has a temperature difference compared with the ambient air, and an imaging system. This work focuses on using smartphones as the imaging system to visualize air leakages. The paper discusses application development and the results of testing to determine leakage visualization performance as a function of leak temperature. The results show that leaks with a temperature difference greater than 16°C compared with the ambient air temperature were visualized using existing smartphones.

Boudreaux, Philip [ORNL] (ORCID:0000000229564665)↗
DOE OSTI2025

Real-Time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-Negative Matrix Factorization

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems because the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate or sacrifice detection sensitivity to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) is a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation, it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. Here, we have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Anomaly detection↗
DOE OSTI2023

Resolving metal binding properties within subunits of a multimeric enzyme Mnx by surface induced dissociation and native ion mobility mass spectrometry

Multi-subunit enzymes function as coordinated assemblies. Yet most enzymatic assays measure the summed output of all populations in solution and cannot easily differentiate contributions of individual subunits. Native mass spectrometry detects intact protein complexes in the gas phase. Surface induced dissociation further releases subunits from protein complexes while retaining compact conformations and bound ligands. Combined with ion mobility, the released subunits can then be carefully monitored for more in-depth structural analysis. Mnx is a unique bacterial multicopper oxidase complex that oxidizes Mn(II) to form MnO 2 minerals, and is composed of three subunits: MnxG, a multicopper oxidase containing the active site, and two accessory proteins, MnxE and MnxF which also bind copper ions. Other known multicopper oxidases do not require accessory proteins, therefore the functions of MnxE and MnxF are not well understood. Here, we use native mass spectrometry with surface induced dissociation and ion mobility to characterize the metal binding properties of Mnx with two metals, catalytic Cu(II) and Mn(II) substrate. We demonstrate our assay can detect subtle structural changes within each subunit, which are presumably related to the allosteric mechanism. We also noticed that ionic strength and solution composition can impact metal binding and must be carefully investigated for such experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗