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DOE OSTI2023

Neutron Coincidence Measurements of Uranium-233 Oxide

Renewed international interest in thorium-fueled advanced reactors has challenged the safeguards community to address future proliferation concerns. Thorium-based technology presents many benefits but does not eliminate the proliferation risks associated with producing and processing fissile material. A byproduct of thorium-fueled reactors is uranium-233, which is classified as a direct-use material. As a result, the development of new or improved methods to characterize and measure materials containing 233U must mirror the pace of development of reactors and facilities that produce such material. Research is underway to assess, develop, and test approaches for safeguarding nuclear materials within the thorium fuel cycle. Neutron signatures from the nondestructive assay (NDA) of materials containing 233U are being quantified to inform the potential characterization of these materials. Using a traditional neutron coincidence counter and a series of well-documented 233U oxide samples, initial measurements have been made to assess the feasibility of 233U characterization and discrimination from other uranium isotopes, primarily 235U, using a combination of measurement techniques and analysis methods. Data acquisition is performed in list mode, allowing for a variety of analyses to be performed on the raw data that is not available using traditional shift register technology. Measurements were performed in passive and active configurations to quantify the strength of signal and to validate simulations in support of this work. This paper presents and discusses the results of the initial measurements of 233U oxide performed at Oak Ridge National Laboratory.

Lockhart, Madeline↗

DOE OSTI2025

Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B^{+}_{1}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B^{+}_{1}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000 × speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B^{+}_{1}$ fields. Next, we train a Residual Network (ResNet) to map $B^{+}_{1}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Deep learning↗

DOE OSTI2026

LaueMatching: an approach for rapid and robust indexing of Laue diffraction patterns

Traditional Laue diffraction pattern indexing often struggles with noisy data, weak signals, peak overlap and missing reflections, particularly from complex or deformed microstructures. Here, we introduce LaueMatching, a high-throughput indexing algorithm designed to overcome these limitations. LaueMatching utilizes a fundamentally different approach based on direct pattern correlation: experimentally pre-processed images are compared against a comprehensive pre-computed library of simulated diffraction patterns corresponding to a dense grid of possible orientations. This approach bypasses the need for explicit peak identification and fitting, steps that are often a failure point for traditional methods. The algorithm rapidly and robustly indexes multiple crystallographic orientations and crystal systems simultaneously, even from challenging patterns. LaueMatching's effectiveness and accuracy have been rigorously tested and validated on diverse experimental (Ni, Al, EuAl 2 O 4 ) and simulated diffraction patterns, demonstrating high-fidelity orientation refinement. Code to implement this approach on both CPU and GPU resources can be downloaded from https://github.com/AdvancedPhotonSource/LaueMatching.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Final Technical Report: The Incubation of Next-Generation Radar Technologies to Lower the Cost of Wind Energy

The National Wind Institute (NWI) at Texas Tech University (TTU) has had an impressive and well documented 46-year history of wind related research activities (http://www.depts.ttu.edu/nwi/). In 2011 with funding from the United States Department of Energy (DOE), an NWI team applied radar technologies and techniques to document the complex flows occurring across a wind plant. The resulting efforts yielded measurements that exceeded the capabilities of commercial lidar technologies with respect to maximum range, range resolution and scan speed. The NWI team was also the first to apply dual-Doppler synthesis and objective analysis techniques to resolve the full horizontal wind field (i.e. not just the line-of-sight wind speeds) to successfully define turbine inflow and wake flows across large segments of wind plants. While these successes advanced wind energy interests, the existing research radar platforms were designed to serve a diversity of meteorological applications, not specifically wind energy. Because of this broader focus and the design choices made during their development, the existing radars experienced technical limitations that inhibited their commercial viability and wide spread adoption. This DOE project enabled the development of a new radar prototype specifically designed for the purpose of documenting wind farm complex flows. Relative to other “off the shelf” radar technologies, the specialized transmitter and receiver chains were specifically designed to enhance data availability in non-precipitating atmospheres. The new radar prototype was integrated at TTU using components from various suppliers across the world, and installed at the Reese Technology Center in May 2016. Following installation, functionality and performance testing were completed, and subsequent comparative analysis indicated that the new prototype greatly enhances data availability by a factor of 3.5-50 in almost all atmospheric conditions. The new prototype also provided enhanced signal quality in clear air (i.e. non-precipitating) environments, mitigated atmospheric attenuation, and extended the useful range of data collection to beyond 30km in cooperative atmospheric conditions. Additionally, the new DOE-X prototype appears to benefit from Bragg scattering when the thermal stratification of the atmosphere is strong (i.e. nocturnal hours). This result was not possible in any capacity with the previous technology. Combined, these developments represent the achievement of all project objectives, advance the Technical Readiness Level (TRL) to a level of 7, and open the door for more widespread adoption and usage in the wind energy sector. At the same time, radar induced artifacts from multi-trip echoes and ground targets increased with the new technology, and these required additional attention for some applications. Commercialization activity accelerated in parallel with the DOE funded project, as SmartWind Technologies, L.L.C., was contracted to provide two new radar systems to DONG Energy to monitor the Westermost Rough wind plant off the east coast of the United Kingdom. These new early stage commercial radars systems were closely related to the DOE prototype, but maintain more robust ancillary support systems. The radars were installed during the summer of 2016, and have been operational since that time. Additionally, commercially funded advancements have since been made in processing sophistication to mitigate the previously identified radar artifacts. TTU and SmartWind Technologies stand ready to provide public and private partners focused on wind plant optimization with this new capability. Ancillary interests have also been identified as the initial deployments of the technology have shown the ability to identify and track avian and drone activity, opening up multi-purpose operational opportunities.

17 WIND ENERGY↗

DOE OSTI2025

A stable 15-member bacterial SynCom promotes Brachypodium growth under drought stress

Introduction: Rhizosphere microbiomes are known to drive soil nutrient cycling and influence plant fitness during adverse environmental conditions. Field-derived robust Synthetic Communities (SynComs) of microbes mimicking the diversity of rhizosphere microbiomes can greatly advance a deeper understanding of such processes. However, assembling stable, genetically tractable, reproducible, and scalable SynComs remains challenging. Methods: Here, we present a systematic approach using a combination of network analysis and cultivation-guided methods to construct a 15-member SynCom from the rhizobiome of Brachypodium distachyon. This SynCom incorporates diverse strains from five bacterial phyla. Genomic analysis of the individual strains was performed to reveal encoded plant growth-promoting traits, including genes for the synthesis of osmoprotectants (trehalose and betaine) and Na+/K+ transporters, and some predicted traits were validated by laboratory phenotypic assays. Results: The SynCom demonstrates strong stability both in vitro and in planta. Most strains encoded multiple plant growth-promoting functions, and several of these were confirmed experimentally. The presence of osmoprotectant and ion transporter genes likely contributed to the observed resilience of Brachypodium to drought stress, where plants amended with the SynCom recovered better than those without. We further observed preferential colonization of SynCom strains around root tips under stress, likely due to active interactions between plant root metabolites and bacteria. Discussion: Our results demonstrate that trait-informed construction of synthetic communities can yield stable, functionally diverse consortia that enhance plant resilience under drought. Preferential colonization near root tips points to active, localized plant-microbe signaling as a component of stress-responsive recruitment. This stable SynCom provides a scalable platform for probing mechanisms of plant-microbe interaction and for developing microbiome-based strategies to improve soil and crop performance in variable environments.

Yadav, Archana↗

DOE OSTI2025

Ligand‐Induced Crystallization Control in MAPbBr 3 Hybrid Perovskites for High Quality Nanostructured Films

AbstractControlling the formation of hybrid perovskite thin films is crucial in obtaining high‐performance optoelectronic devices, since factors like morphology and film thickness have a profound impact on a film's functionality. For light‐emitting applications grain sizes in the sub‐micrometer‐range have previously shown enhanced brightness. It is therefore crucial to develop simple, yet reliable methods to produce such films. Here, a solution‐based synthesis protocol for the on‐substrate formation of MAPbBr3 (MA = methylammonium) nanostructures by adding the bifunctional rac‐3‐aminobutyric acid to the precursor solution is reported. This synthesis route improves key optical properties such as photoluminescence quantum yields and life times of excited states by inducing a controlled slow‐down of the film formation and suppressing agglomeration effects. In situ spectroscopy reveals a delayed and slowed down crystallization process, which achieves synthesis of perovskite structures with much reduced defect densities. Further, aggregation can be controlled by the amount of amino acid added and adjusting the synthesis protocol allows to produce cubic crystallites with targeted size from nanometer to micrometer scales. The nanocrystalline MAPbBr3 samples show enhanced amplified spontaneous emission (ASE) intensities, reduced ASE thresholds and purer ASE signals, compared to pristine films, even under intense optical driving, making them promising structures for lasing applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Ultrasonic Measurements of Temperature Profile and Heat Fluxes in Coal-Fired Power Plants (Final Report)

Many industrial processes are inaccessible or inhospitable to characterization by traditional temperature measurement methods, such as thermocouples, especially over prolonged exposure to harsh environments. Ultrasound is an established characterization technology with diverse applications ranging from medical imaging to therapies to flaw detection to nondestructive evaluation. Ultrasound may characterize solid materials and components noninvasively as a nondestructive evaluation modality and obtain internal measurements of material properties. For example, the speed of ultrasound propagation changes with Young’s modulus and Poisson’s ratio, which can be found from its measurements. Traditional ultrasonic characterization assumes all material properties remain constant with the position. When this assumption holds, a property of interest may be measured by relating it to the speed of ultrasound propagation (or a speed of sound, SOS) and measuring the SOS by timing the ultrasound propagation through a known distance. However, when a property of interest is spatially distributed, the propagation time depends on the SOS changing with the position along the ultrasound propagation path. The multiple temperature distributions may lead to an identical time of flight (TOF). Temperature is one property that impacts the speed of ultrasound and often cannot be assumed to remain constant with the position. Previously, in the context of temperature, we addressed the challenge of ultrasonic characterization of spatially distributed properties by developing a method for measuring segmental temperature distributions (MSTD). This method divides the ultrasonic propagation into segments bound by echogenic features. These features provide ultrasonic interfaces where some energy is reflected toward the receiving transducer, and the rest continues through the medium. The time-of-flight between the echoes reflected from echogenic features characterizes the spatial distribution in the properties of interest in the corresponding segment of the ultrasonic propagation path. This project demonstrated the application of the MSTD method in industrial conditions of the coal-fired power plant. We implemented the MSTD using metals and alloys waveguides, which may be the existing structure for which the temperature distribution is characterized or purposefully designed waveguides added to the structure by welding or other means specifically to quantify thermal properties using the MSTD method. Previous iterations of the MSTD method used ceramic and cementitious waveguides, which significantly attenuate ultrasound. On the other hand, low attenuation in metallic waveguides creates interactions between echogenic features which compilates the signal analysis in the segmental TOF measurements. We have established the WG design principles that minimize the interferences between trailing and primary echoes and, in some cases, eliminate them. The waveguides in which echoes do not interfere improve the timing accuracy and the robustness of ultrasonic measurements of the spatial distributions in material properties. Our emphasis remained on the estimation of the temperature distributions. We have developed general recommendations for designing ultrasonically segmented waveguides with the reduced influence of trailing echoes. Two of our waveguide designs were tested in the industry. The first waveguide was designed for insertion into a combustion zone of the utility-scale coal-fired power plant boiler. The second design allows the characterization of temperature distribution in the direction normal to the boiler’s water wall, a large heat exchanger converting the chemical energy released during combustion to the steam driving the electrical power generation turbines. These waveguides were designed to operate within a restrictive space of thermally insulated water wall and incorporate densely located echogenic features while combatting the influence of trailing echoes. The project has successfully demonstrated the feasibility of using the developed method for accurate, continuous, and robust temperature measurements in extreme environments of power generation and other industrial processes. It, therefore, has achieved its overarching goal of advancing the technology readiness level of the novel Ultrasound Measurements of Segmental Temperature Distribution (US-MSTD) method for real-time measurements of the temperature distribution and heat fluxes closer to commercial availability, developing a prototype multipoint measurement system, and validating its performance on coal-fired utility boilers. The success of this project was achieved in collaboration with the power generator, Rocky Mountain Power, and set the stage for the transfer of this technology from the laboratory to the industry.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2013

GPS Data Filtration Method for Drive Cycle Analysis Applications

When employing GPS data acquisition systems to capture vehicle drive-cycle information, a number of errors often appear in the raw data samples, such as sudden signal loss, extraneous or outlying data points, speed drifting, and signal white noise, all of which limit the quality of field data for use in downstream applications. Unaddressed, these errors significantly impact the reliability ofsource data and limit the effectiveness of traditional drive-cycle analysis approaches and vehicle simulation software. Without reliable speed and time information, the validity of derived metrics for drive cycles, such as acceleration, power, and distance, become questionable. This study explores some of the common sources of error present in raw onboard GPS data and presents a detailedfiltering process designed to correct for these issues. Test data from both light and medium/heavy duty applications are examined to illustrate the effectiveness of the proposed filtration process across the range of vehicle vocations. Graphical comparisons of raw and filtered cycles are presented, and statistical analyses are performed to determine the effects of the proposed filtration processon raw data. Finally, an evaluation of the overall benefits of data filtration on raw GPS data and present potential areas for continued research is presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Optimizing transmit field inhomogeneity of parallel RF transmit design in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B+1) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can significantly degrade imaging quality and its medical applications. This study addresses B+1 field homogeneity through a novel deep learning-based strategy. Traditional methods like Magnitude Least Squares (MLS) optimization have been effective but are time-consuming and dependent on the patient’s presence. Recent machine learning approaches, such as RF Shim Prediction by Iteratively Projected Ridge Regression and deep learning frameworks, have shown promise but face limitations like extensive training times and oversimplified architectures. We propose a two-step deep learning strategy. First, we obtain the desired reference RF shimming weights from multi-channel B+1 fields using random-initialized Adaptive Moment Estimation. Then, we employ Residual Networks (ResNets) to train a model that maps B+1 fields to target RF shimming outputs. Our approach does not rely on pre-calculated reference optimizations for the testing process and efficiently learns residual functions. Comparative studies with traditional MLS optimization demonstrate our method’s advantages in terms of speed and accuracy. The proposed strategy achieves a faster and more efficient RF shimming design, significantly improving imaging quality at UHF. This advancement holds potential for broader applications in medical imaging and diagnostics.

Lu, Zhengyi [Vanderbilt University]↗

DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗

DOE OSTI2019

Final Report – Cooperative Research and Development Agreement between National Energy Technology Laboratory and Oxergy, Inc.

The NETL Raman Gas Analyzer (RGA) has been demonstrated to have an extremely fast response (<1 second) multi-gas analysis capability. All but the noble gases are Raman active, although the Raman interaction is weak. The RGA uses a reflectively lined capillary as the optical cell, providing both a small sample volume for fast gas exchange, and a much greater Raman signal collection than traditional instrument configurations. This allows very fast measurement of fuel gases (hydrogen, methane, ethane, propane) as well as other common industrial gases (carbon monoxide, carbon dioxide, nitrogen, oxygen, and water). This provides a new enabling technology for faster, smarter process control based on the chemical composition of the gases in the process – including improved fuel flexibility and efficiency for power generation systems. Research and development up to 2016 resulted in the construction of two field prototypes of the NETL RGA, which has been in applied to various laboratory applications for testing. The results of the testing have been used to improve the RGA design, with the intent to support technology transfer. The quality of the optical capillary used in the RGA is critical to its performance, and prior work at NETL produced a few optical capillaries superior to those commercially available. The production of high optical quality capillaries, however, was inconsistent in its results. With the support of the DOE Office of Technology Transitions, through the Technology Commercialization Fund program, this project was undertaken to address this commercialization barrier and to also evaluate the possible application of Raman gas analysis to a supercritical water gasification process being developed by Oxergy Inc. Oxergy, a small business located in Fairbanks, Alaska, is developing ultra-compact thermochemical reaction devices built with advanced manufacturing processes. Chief Technology Officer Jonathan Kamler holds the (pending) patent to Oxergy’s core reactor technology. Oxergy maintains wet-lab and combustion-lab space at the University of Alaska Fairbanks Center for Sustainable Living in Palmer, Alaska, where Mr. Kamler operates a pilot-scale, supercritical water gasification system designed and built by Oxergy. Mr. Kamler also operates an All Powers Labs Power Pallet gasifier coupled to a 10KW electrical generator as a dry biomass test platform at the Palmer facility. Both systems are suitable for real-world, collaborative testing of Raman gas analysis applications. The objectives of the CRADA include making the production of high-quality optical capillaries consistent and high yield, investigating other options for optical waveguides, and testing the capillaries for measurements in supercritical water gasification. Success on these objectives will support commercialization of both the RGA and supercritical water gasification technologies. Through the work performed on this project, a new apparatus was constructed and optimized to provide consistent production of high-quality optical capillaries. Several thick-walled capillaries were coated using this apparatus and shipped to Oxergy in September 2018 for testing in both dry gasification and supercritical water gasification process monitoring applications at University of Alaska Fairbanks facilities in Palmer. The strong earthquake near Anchorage and Palmer in November 2018 caused extensive damage and closure of the facilities used by Oxergy, preventing Oxergy from proceeding with experimental testing before the end of the project; the facility has been closed indefinitely. Earthquake damage prohibited the completion of all the project objectives prior to the end of the project. However, significant progress was made toward addressing a core technology commercialization barrier. The largest and most mature potential market for Raman gas analysis is its use in analytical instruments, which require sensitivity and low detection limits, and Oxergy’s market research found that market inertia is a barrier to commercial success in this market. Although NETL’s innovations in capillary coating have substantially improved Raman gas analysis capability, the likely market is expected to be niche applications that value the fast detection of real-time changes in gas composition.

Chorpening, Benjamin↗

DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Paul, Lazlo↗

DOE OSTI2015

In-vitro and in-vivo imaging of MMP activity in cartilage and joint injury

Non-destructive detection of cartilage-degrading activities represents an advance in osteoarthritis (OA) research, with implications in studies of OA pathogenesis, progression, and intervention strategies. Matrix metalloproteinases (MMPs) are principal cartilage degrading enzymes that contribute to OA pathogenesis. MMPSense750 is an in-vivo fluorimetric imaging probe with the potential to continuously and non-invasively trace real-time MMP activities, but its use in OA-related research has not been reported. Our objective is to detect and characterize the early degradation activities shortly after cartilage or joint injury with MMPSense750. We determined the appropriate concentration, assay time, and linear range using various concentrations of recombinant MMPs as standards. We then quantified MMP activity from cartilage explants subjected to either mechanical injury or inflammatory cytokine treatment in-vitro. Finally, we performed in-vivo MMP imaging of a mouse model of post-traumatic OA. Our in-vitro results showed that the optimal assay time was highly dependent on the MMP enzyme. In cartilage explant culture media, mechanical impact or cytokine treatment increased MMP activity. Injured knees of mice showed significantly higher fluorescent signal than uninjured knees. We conclude that MMPSense750 detects human MMP activities and can be used for in-vitro study with cartilage, as well as in-vivo studies of knee injury, and can offering real-time insight into the degradative processes that occurring within the joint before structural changes become evident radiographically. - Highlights: • MMPSense750 is near-infrared fluorescent probe which can detect MMP activity. • MMPSense750 can detect human MMP-3, -9, and -13. • The reaction kinetics with MMPSense750 were different for the three MMPs. • MMPSense750 can visualized real time MMP activity in mouse injured knees. • MMPSense750 is convenient tool to evaluate real-time MMP activity non-invasively.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗

DOE OSTI2026

The role of source geometry and atmospheric propagation in global bolide infrasound detectability

Global infrasound monitoring provides a persistent means of detecting energetic bolide atmospheric entries, complementing optical observations and extending coverage over remote regions. We present a global assessment of the physical factors governing bolide infrasound detectability by correlating 623 bolide events reported by the Center for Near-Earth Object Studies between 2007 and 2025 with waveform data from the International Monitoring System. We identify 311 events with confirmed infrasound detections, corresponding to a detection rate of approximately 50%, substantially higher than inferred from earlier surveys, reflecting both the maturation of the global infrasound network and advances in automated, multi-frequency array processing. Analysis of flight parameters shows that infrasound detectability is selective rather than uniform across the bolide population. Detected events are preferentially associated with steeper entry angles and lower-altitude energy deposition, while shallow, high-altitude trajectories are less consistently observed. Very high-energy events remain detectable regardless of geometry, but for the more common lower-energy regime, observability depends on specific combinations of entry parameters and propagation conditions. This geometric dependence persists across comparable energy ranges and atmospheric conditions, indicating that entry angle exerts a primary control on detectability, with energy and propagation acting as secondary modulating factors. Furthermore, these results provide new physical constraints on bolide-atmosphere interactions and improve interpretation of global infrasound observations for planetary defense and atmospheric-entry studies.

Bolides↗

DOE OSTI2018

Advanced detector signal acquisition and electron beam scanning for high resolution SEM imaging

We report the advancement of materials science at the mesoscale requires improvements in both sampling volumes/areas and spatial resolution in order to make statistically significant measurements of microstructures that influence higher-order material properties, such as fatigue and fracture. Therefore, SEM-based techniques have become desirable due to improvements in imaging resolution, large sample handling capability, and flexibility for in-situ instrumentation. By using fast sampling of SEM electron detector signals, intrinsic beam scanning defects have been identified that are related to the response time of the SEM electron beam deflectors and electron detectors. Mitigation of these beam scanning defects using detector sampling approaches and an adaptive model for settling time is shown to produce higher resolution SEM images, at faster image acquisition times, with a means to quantify the different response functions for various beam deflectors and detectors including those for electrons and ions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

58 GEOSCIENCES↗