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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

In vitro toxicity assessment of uranium particulates on different human lung epithelial cell models

Inhalation of uranium aerosols produced via human activities such as mining can pose a threat to human respiratory systems. Uranium oxide particulates emit short-range alpha particles that elicit DNA and direct damage, beyond associated physiochemical heavy-metal toxicity, to internal epithelial tissues. The availability of reliable in vitro models to study radiation exposure can greatly enhance our ability to understand and combat the biological impacts of exposure. However, the toxicological effects of alpha emissions and/or the oxidation states of uranium particulates vary across different human lung epithelial cell models and have not been systematically compared. We have endeavored to address this limitation by comparing impacts in three different human lung cell models: primary human bronchial and tracheal epithelial cells, primary human small airway epithelial cells, and human adenocarcinoma alveolar basal epithelial cells. Other studies have mainly investigated the toxicity of depleted uranium. Here, we compared the exposure of uranium oxide particulates (U 3 O 8 and UO 3 ) of different enrichment states on the chosen cell systems. Each cell model was exposed to 0.1, 1, 10, 50, 100, and 500 µg/mL of depleted U 3 O 8 , highly-enriched U 3 O 8 , and natural UO 3 particulates for 24 hours in submerged monolayer cultures. We compared viability and superoxide dismutase activity results across cell lines and uranium enrichment/ oxidative states. The results showed that 1) the oxide state of the particulates affected cell viability, implying that uranium’s different oxidation states contribute to different toxicological responses, and 2) each cell model reacts differently when exposed to uranium oxides, which may provide insights into the mechanistic processes associated with the exposure of radiological particulates on different biological systems. For instance, increased uranium enrichment corresponds to increased toxicity for the primary cells, but not for the immortalized cells. Our study shows that a holistic approach that incorporates similarities between model systems and types of radionuclides is required to truly develop empirical solutions for radiation exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Tracking the protein conformational motions driving HIV-1 membrane fusion

HIV-1 Env (trimeric gp120/gp41) is the surface protein responsible for membrane fusion. The Env binds to the receptor proteins, which induces gp120 shedding leading to conformational changes of gp41 from the pre-fusion to post-fusion state, allowing its fusion peptide to embed in the host cell membrane and bringing the viral and host cell membranes together. The gp41 refolding is a target of several peptide inhibitors. Yet, the molecular mechanism of this dynamic process is still not well understood. In this study, we successfully simulate the conformational change of gp41 from pre-fusion to post-fusion state in atomistic resolution using all-atom structure-based models. We reveal that maintaining the directionality of protomer interactions in both pre-fusion and post-fusion states is crucial for gp41 refolding. Additionally, we find that HR1 inherently extends as a three-helical bundle toward the host-cell membrane without any bias. Importantly, we identify native contacts in the pre-fusion state that are critical for the proper refolding of gp41 towards the post-fusion state. Lastly, by incorporating the membrane-fusion inhibitors, T20 and SFT, we identify the most vulnerable stage in the fusion pathway that exhibits the greatest sensitivity to these drugs, which could aid in a better understanding of drug resistance mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Gas Hydrate Film Growth in Microfluidic Channels for Carbon Dioxide Capture and Sequestration Applications

Gas or clathrate hydrates are a solid, crystalline compound composed of water and guest molecules that typically form at high pressure and low temperature conditions. Carbon dioxide (CO2) hydrates may be involved in several carbon dioxide capture and sequestration (CCS) applications, including CO2 pipeline transportation and CO2 offshore sequestration. Within these applications, the formation mechanism and kinetics must be well understood to manage the CCS processes, either by preventing or promoting hydrate formation. In this work, a high-pressure glass microfluidic reactor is used in tandem with visual microscopy and in-situ Raman spectroscopy to study both the morphological and kinetic behavior of gas hydrate crystals. Subcooling, pressure, and CO2 flow rate are investigated for their impact on the thickening behavior of pure CO2 hydrates, with flow rate being the only parameter to have a significant effect. Visual and Raman spectroscopy evidence show that both a dense hydrate layer and a porous hydrate layer form, and the latter may provide a path for mass transfer to continue hydrate crystallization. A first principles mass transfer model is developed to describe CO2 hydrate crystal thickening at the interface between gas and water. The impacts of gas impurities and channel wettability are also studied. This method is further applied to investigate the conversion of methane hydrate to CO2 hydrate for combined energy recovery and methane hydrate formation. The authors acknowledge the US Department of Energy Basic Energy Science award # DE-SC0022162.

Wadsworth, Lindsey [Colorado School of Mines, Gold↗
DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Demonstration and performance of an online data selection algorithm for liquid argon time projection chambers using MicroBooNE

The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nuclei using the Fermilab Booster Neutrino Beam, MicroBooNE aims to develop methodologies for rare beyond the Standard Model and off-beam physics searches. Looking ahead to the upcoming Deep Underground Neutrino Experiment (DUNE), with MicroBooNE serving as a valuable testbed, achieving high sensitivity and livetime for off-beam physics while satisfying data processing and storage constraints will require data-driven, intelligent, and online or real-time data selection techniques. These techniques are essential for reducing data rates and preserving rare signals with high accuracy. In this paper, we describe a fast data selection algorithm suitable for online execution to identify electrons from stopping cosmic ray muons in the MicroBooNE detector utilizing ionization charge information, and present its performance. This represents the first demonstration of online data selection in a LArTPC using real data and charge information exclusively and provides an important proof-of-principle for applying such techniques to other LArTPC experiments such as the Short-Baseline Near Detector and DUNE.

Abratenko, P. [Tufts U. (main)]↗
DOE OSTI2026

Development of a Techno-Economic Analysis Framework for a Solar Thermochemical Fuel Production Process

Synthetic liquid fuels can provide a drop-in substitute for fossil-based fuels in sectors such as aviation and maritime, where electrification is not a viable option due to the need for high specific energy density. However, for these alternative fuels to be adopted at a commercial scale, their price must be competitive compared to their fossil-based counterparts. The reverse water-gas shift (RWGS) reaction offers a promising pathway, using hydrogen (sourced from electrolysis) and carbon dioxide as the feed and reacting to produce syngas - a mixture of H2 and CO at a specific ratio. Syngas is a useful precursor that can be converted into fuels and chemicals via known downstream processes, such as liquid transportation fuels via Fischer-Tropsch (FT) synthesis. The RWGS reaction is currently not applied in commercial scale, unlike the rest of the components in the process chain (electrolyzers and syngas-to-fuel synthesis units). The RWGS reaction poses several challenges due to its restrictive thermodynamics. Being an equimolar reaction, high temperatures and a large excess of H2 are needed to achieve reasonable CO2 conversion at equilibrium. This has detrimental effects on practical process implementation and the quality of syngas that can be produced, with direct effect on the energy and capital requirements, as well as the need for expensive downstream separation. In this work, we are proposing to develop a new concentrating solar thermal (CST) compatible RWGS reactor, performing the reaction in a 2-step chemical looping process using metal oxide at a temperature range of 600-800 degrees Celsius. By decoupling the reactor from the solar receiver, the Generation 3 (Gen3) CST technology could be utilized, together with its proposed thermal energy storage (TES) technology, benefitting from a good match to the required temperatures. CST technology is a viable option for supplying the heat that could be rapidly deployed in scale, thus being a good match to the gas-to-liquid (GTL) process which requires a large minimal scale to be commercially viable. The integration of TES with CST also allows operating the plant at large annual capacity factors and avoids multiple shutdown/startup cycles, thus fitting into the steady-state operation mode that most GTL processes require. The main innovation in the proposed design hinges on a countercurrent reaction design using a packed bed reactor. In 2019 Metcalfe et al. showed the benefits of countercurrent species exchange could be realized in a redox chemical-looping processes, by storing the favorable countercurrent chemical potential profiles in a packed bed of non-stoichiometric oxide. Metcalfe et al. applied this breakthrough concept to the WGS reaction, which is conventionally a co-feed catalytic process, showing a dramatic improvement. Bulfin et al. (2023) performed a similar proof-of-concept demonstration for the RWGS reaction using CeO2, achieving cumulative and peak CO2 conversions of 88% and 95%, respectively, compared to a thermodynamic limit of 58% for the co-feed catalytic process at the same conditions. In our new REGENLOOP project, we are developing a reactor prototype from the heat-exchange packed bed reactor-type, a commonly used reactor in the chemical industry. The endothermic heat of reduction will be supplied to the reactor using CST, while the same heat transfer fluid (HTF) mechanism will be used to extract the exothermic heat of oxidation. An array of multiple reactors is used to supply constant high-purity CO stream, that is then mixed with H2 from electrolysis to produce a high-purity syngas at the required composition. By removing the CO-CO2 separation after the RWGS process, significant energy and cost reduction can be achieved. A physics-based TEA framework is currently being developed, covering all the major plant processes, from the solar collection through storage, chemical looping RWGS, GTL, and auxiliary unit operations, up to the liquid hydrocarbon product. This modeling framework will utilize reduced-order models for the chemical looping RWGS and TES, CST modeling using SolarPILOT, and Aspen Plus for the GTL. By using this combined physics-based approach, the effects of design/operating parameters on the performance and cost can be elucidated. In our presentation, the modeling framework will be presented in detail, including preliminary cost predictions of using this plant configuration under a few selected relevant case studies. This study will be used to identify the major cost drivers, informing further system design and optimization needed to chart the way for a commercially viable pathway.

14 SOLAR ENERGY↗
DOE OSTI2024

The impact of hot-press conditions on the durability of polymer electrolyte membrane fuel cells

The proton exchange membrane integrity can be compromised during hot-press fabrication of membrane electrode assemblies (MEAs) causing premature cell failures during operation. In this work, infrared (IR) thermography was used as a diagnostic tool to spatially visualize hydrogen (H 2 ) crossover and identify process-induced-membrane irregularities (PIMs). These irregularities were identified as seed locations for MEA failures. Fine tuning of hot-press conditions was used to mitigate premature cell failures informed by accelerated stress testing (AST). The impact of PIMs on the initial performance, high-frequency resistances, open-circuit voltage, and H 2 crossover are reported. Nafion XL and 212 membranes, hot-pressed with a force of 16 kg/cm 2 and temperature of 120°C, were found to be consistently irregularity-free. Irregularity-free MEAs using Nafion 211, 212, and XL membranes demonstrated AST lifetime improvements of 58, 64 and 400%, respectively, compared to those fabricated with non-optimized conditions. In conclusion, this work highlights the importance of fabrication parameters on premature cell failures.

08 HYDROGEN↗
DOE OSTI2021

End-to-end orientation estimation from 2D cryo-EM1images

Cryo-electron microscopy (cryo-EM) is a Nobel Prize-winning technique for deter-mining high-resolution 3D structures of biological macromolecules. A 3D structure is reconstructed from hundreds of thousands of noisy 2D projection images. However, existing 3D reconstruction methods are still time-consuming, and one of the major computational bottlenecks is to recover the unknown orientation of the particle in16each 2D image. The dominant methods typically exploit expensive global search on each image to estimate the missing orientations. Here, a novel end-to-end supervised learning method is introduced to directly recover the missing orientations from 2D cryo-EM images. A neural network is used to approximate the mapping from images to orientations. Furthermore, a robust loss function is proposed for optimizing the parameters of the network, which can handle both asymmetric and symmetric 3D structures. Experiments on synthetic datasets with various symmetry types confirm that the neural network is capable of recovering orientations from 2D cryo-EM images, and the results on one real cryo-EM dataset further demonstrate its potential in more challenging imaging conditions.

3D reconstruction↗
DOE OSTI2026

DAMSA Experiment Conceptual Design White Paper

DAMSA (DArk Messenger Searches at an Accelerator) is a novel short-baseline accelerator experiment aimed at probing short-lived physics processes, including searches for evidence of a dark sector of particle physics and well-motivated Standard Model signals. Motivated by open questions in neutrino physics and the absence of conclusive evidence for conventional weakly interacting massive particles, DAMSA targets MeV-to-sub-GeV dark-sector messengers with feeble couplings that can be produced in abundance at the PIP-II LINAC. By employing an ultra-short baseline of order one meter, DAMSA is uniquely positioned to overcome the beam-dump "ceiling" that limits sensitivity to promptly decaying particles in longer-baseline experiments. The conceptual design emphasizes a beam-dump production scheme combined with a compact detector optimized for rare decays while mitigating intense neutron-induced backgrounds inherent to high-power proton beams. To validate the experimental strategy and detector technologies, the Little DAMSA Path-Finder (LDPF) proof-of-concept experiment is proposed, focusing on axion-like particles decaying to two photons and operating with 300 MeV electron beams at FAST. Successful realization of LDPF will establish the feasibility of the DAMSA approach, enabling a broad and powerful program to explore short-lived new physics and precision Standard Model processes in a previously inaccessible regime. This conceptual design document outlines the technical details of DAMSA's physics goals, the beam facility proposals, key experimental challenges and how to overcome them, and the proposed experimental staging campaigns.

Bhattarai, Prithak [Texas U., Arlington]↗
DOE OSTI2026

Design Considerations for Phase Change Material-Incorporated Heat Exchangers in Water Heating

In recent years, the buildings sector has seen major pushes towards decarbonization through innovations that promote deep electrification. 20% of an average household's energy use comes from water heating, and in the US, over half of all households still use gas water heaters. Of those that use electricity for water heating, the majority use resistive elements rather than heat pump water heaters (HPWHs), the latter of which use 60-70% less energy than the former. However, HPWHs tend to have larger dimensions, preventing current 50-80-gallon tanks on the market from fitting into smaller utility closets sized for 30-40 gallons, such as those found in manufactured housing. Additionally, these smaller HPWHs tend to underperform relative to their larger counterparts. One solution that addresses both space and performance concerns is thermal energy storage, and in particular, phase change materials (PCMs). This study outlines the design process used to produce novel PCM heat exchangers for use in small-volume HPWH tanks, including the identification of design constraints and performance targets relevant to real-world applications. To ensure optimal PCM utilization and tank storage capacity, this works seeks to co-maximize surface area and PCM volume; therefore, this research targets triply periodic minimal surface (TPMS) lattices, which boast enhanced heat transfer capabilities compared to traditional heat exchanger geometries. Starting with a suite of TPMS lattices, we demonstrate a systematic approach for narrowing down feasible designs that comply with identified constraints while meeting PCM performance objectives. Our current results indicate that tuning lattice properties can effectively produce geometries that provide enough energy storage to achieve a 50-gallon capacity out of a 40-gallon HPWH, even when placing the PCM heat exchanger inside the tank. Additionally, we demonstrate successful fabrication of lattices with these tuned properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Mineral Scale Formation during Crossflow Reverse Osmosis at Constant Flux and Constant Transmembrane Pressure Conditions

Mineral scale formation on membrane surfaces is a significant challenge in reverse osmosis water purification. Laboratory fouling experiments are typically run such that the transmembrane pressure (TMP) is fixed, and the permeate flux decreases over time as scales accumulate on the membrane surface. However, this change in flux means that the hydrodynamic conditions at the membrane surface are continuously changing, which could affect crystallization and foulant deposition processes. Operating under constant permeate flux conditions, in contrast, is advantageous because it keeps the hydrodynamic conditions relatively consistent, making it possible to compare how membrane properties (e.g., surface chemistry) affect fouling propensity. Industrial reverse osmosis operations are not run strictly in either constant TMP or constant flux mode; while they may start at a constant TMP, feed pressure may be periodically adjusted to maintain permeate water production within a specified range. Here, the scarcity of constant permeate flux reverse osmosis scaling experiments reported in the literature frustrates efforts to compare membrane fouling processes under constant TMP and constant flux conditions. For the first time, the evolution of the fouling layer resistance was compared as a function of cumulative permeate volume per membrane area during constant TMP and constant flux reverse osmosis filtrations. Scaling experiments were conducted by challenging commercial reverse osmosis membranes with a model feed solution nearly saturated with calcium sulfate dihydrate (gypsum). At low fluxes, the increase in fouling layer resistance was quantitatively similar for the two operational modes. In contrast, at high fluxes, the fouling layer resistance increased more rapidly in constant flux filtration than in constant TMP filtration. The mechanism of scale formation in constant TMP and constant flux operation was self-limiting and self-reinforcing, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Identifying COVID-19 cases and extracting patient reported symptoms from Reddit using natural language processing

We used social media data from “covid19positive” subreddit, from 03/2020 to 03/2022 to identify COVID-19 cases and extract their reported symptoms automatically using natural language processing (NLP). We trained a Bidirectional Encoder Representations from Transformers classification model with chunking to identify COVID-19 cases; also, we developed a novel QuadArm model, which incorporates Question-answering, dual-corpus expansion, Adaptive rotation clustering, and mapping, to extract symptoms. Our classification model achieved a 91.2% accuracy for the early period (03/2020-05/2020) and was applied to the Delta (07/2021–09/2021) and Omicron (12/2021–03/2022) periods for case identification. We identified 310, 8794, and 12,094 COVID-positive authors in the three periods, respectively. The top five common symptoms extracted in the early period were coughing (57%), fever (55%), loss of sense of smell (41%), headache (40%), and sore throat (40%). During the Delta period, these symptoms remained as the top five symptoms with percent authors reporting symptoms reduced to half or fewer than the early period. During the Omicron period, loss of sense of smell was reported less while sore throat was reported more. Our study demonstrated that NLP can be used to identify COVID-19 cases accurately and extracted symptoms efficiently.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2025

Scaling deep learning for material imaging with a pseudo 3D model for domain transfer

The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.

25 ENERGY STORAGE↗
DOE OSTI2025

Measurement of the branching ratio of 16 N , 15 C , 12 B , and 13 B isotopes through the nuclear muon capture reaction in the Super-Kamiokande detector

The Super-Kamiokande detector has measured solar neutrinos for more than 25 years. The sensitivity for solar neutrino measurement is limited by the uncertainties of energy scale and background modeling. Decays of unstable isotopes with relatively long half-lives through nuclear muon capture, such as 16 N, 15 C, 12 B, and 13 B, are detected as background events for solar neutrino observations. Here, in this study, we developed a method to form a pair of stopping muon and decay candidate events and evaluated the production rates of such unstable isotopes. We then measured their branching ratios considering both their production rates and the estimated number of nuclear muon capture processes as Br⁡( 16 N) = (9.0 ± 0.1)%, Br⁡( 15 C) = (0.6 ± 0.1)%, Br⁡( 12 B) = (0.98 ± 0.18)%, Br⁡( 13 B) = (0.14 ± 0.12)%, respectively. The result for 16 N has world-leading precision at present and the results for 15 C, 12 B, and 13 B are the first branching ratio measurements for those isotopes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Image processing workflow yielding high contrast synchrotron nanoscale computed tomography data from Ni-YSZ electrodes

The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.

08 HYDROGEN↗
DOE OSTI2025

Photo-Induced Hydrogen Production from Formic Acid Using a Palladium Catalyst

Liquid organic hydrogen carriers (LOHCs) are recognized as promising sustainable hydrogen (H 2 ) carriers due to their high volumetric capacity and ability to store H 2 at ambient conditions, eliminating the need for energy-intensive liquefaction or compression processes associated with H 2 or ammonia gas. One of the main current drawbacks, however, is LOHCs’ high energy demand for H 2 release. This work presents the photo-induced liberation of H 2 from formic acid (FA) as a liquid H 2 carrier, using visible light and well-established 5 wt% palladium nanoparticles supported over carbon (Pd/C). We show that low-power light-emitting diodes (LEDs) produced higher gas flow than their thermal baseline (35 °C), with 27.2 mL/min and 7.72 mL/min, respectively. Further, the rate of gas evolved with light intensity, catalyst loading, and the concentration of FA. Light-induced dehydrogenation shows similar deactivation as the known thermal mechanisms, such as the decreased Pd 2+ /Pd 0 ratio and Pd nanoparticle agglomeration. Hence, these observations suggest a photothermal mechanism, whereby the LED provides heat efficiently absorbed by the Pd/C catalyst and enhanced by Pd’s ability to absorb light, thereby driving the FA dehydrogenation reaction at ambient conditions.

08 HYDROGEN↗