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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Furthermore, studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

TDCOSMO. X. Automated modeling of nine strongly lensed quasars and comparison between lens-modeling software

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars with GLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of the Hubble Space Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling software Lenstronomy revealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Octofitter: Fast, Flexible, and Accurate Orbit Modeling to Detect Exoplanets

As next-generation imaging instruments and interferometers search for planets closer to their stars, they must contend with increasing orbital motion and longer integration times. These compounding effects make it difficult to detect faint planets but also present an opportunity. Increased orbital motion makes it possible to move the search for planets into the orbital domain, where direct images can be freely combined with the radial velocity and proper motion anomaly, even without a confirmed detection in any single epoch. In this paper, we present a fast and differentiable multimethod orbit-modeling and planet detection code called Octofitter. This code is designed to be highly modular and allows users to easily adjust priors, change parameterizations, and specify arbitrary function relations between the parameters of one or more planets. Octofitter further supplies tools for examining model outputs including prior and posterior predictive checks and simulation-based calibration. We demonstrate the capabilities of Octofitter on real and simulated data from different instruments and methods, including HD 91312, simulated JWST/NIRISS aperture masking interferometry observations, radial velocity curves, and grids of images from the Gemini Planet Imager. We show that Octofitter can reliably recover faint planets in long sequences of images with arbitrary orbital motion. This publicly available tool will enable the broad application of multiepoch and multimethod exoplanet detection, which could improve how future targeted ground- and space-based surveys are performed. Finally, its rapid convergence makes it a useful addition to the existing ecosystem of tools for modeling the orbits of directly imaged planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Root responses to warming and hurricane disturbances in a wet tropical forest of Puerto Rico: R code and data

The purpose of this data was to generate a scientific article that describes the responses of tropical roots to a warming experiment and to the effect of two consecutive hurricanes in Puerto Rico (Yaffar et al. in review). This data is from 10 months of minirhizotron images taken every 2 weeks at the experimental warming Tropical Responses to Altered Climate Experiment (TRACE) plots in Puerto Rico before and after Hurricanes Irma and Maria. This project has 3 warmed plots (plot 2,4,6) and 3 control plots (plot 1,3,5) with 2 minirhizotron tubes at each plot. As part of the data, there is also root data taken from cores and in-growth cores. Additionally, there is soil nutrient concentration data, soil microclimate, total leaf area, and canopy openness taken by Reed et al. 2020, and the TRACE census. Data files are in CSV format and the R code included in this package can be used with R 3.4.4 (R Core Team and contributors worldwide).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

4D Imaging of ZnO-Coated Nanoporous Al 2 O 3 Aerogels by Chemically Sensitive Ptychographic Tomography: Implications for Designer Catalysts

The 3D chemical structure (4D spectromicroscopy) of nanoporous Al 2 O 3 aerogels coated with ZnO by atomic layer deposition (ALD) was studied by multienergy scanning transmission X-ray microscopy. These materials are representative of a class of designer catalysts in which the nanoporous support is prepared separately from the active catalyst material, which is subsequently introduced by ALD, thereby allowing independent optimization of the morphology, chemistry, and spatial distribution of the support and catalyst. The samples studied were prepared by Ga ion and Xe plasma focused ion beam (FIB) milling as well as drop casting from water suspension. Zn L and Al K edge spectra of six samples with three different ZnO loadings were measured to investigate how loading and different sample preparation methods affect the 3D distribution of the ZnO and Al 2 O 3 . Scanning transmission X-ray microscopy (STXM) and ptychographic imaging at two energies each at the Zn L 3 and Al K edge were measured. The ptychography data were analyzed by using the SHARP reconstruction code to generate quantitative 2D chemical maps of the Al 2 O 3 and the ZnO. The STXM and ptychography maps were then measured at a sequence of tilt angles, covering up to 160° of rotation. The 3D structure of the ZnO and Al 2 O 3 was derived from the tilt series data by tomographic reconstruction using a compressed sensing algorithm. A twodimensional spatial resolution (half-period) of 6 nm, measured by Fourier ring correlation, and a 3D spatial resolution (half-period) of 9 nm, measured by Fourier shell correlation, were achieved when using the COSMIC beamline at the Advanced Light Source (ALS). The results show that for all of the ZnO loadings studied there is nonuniform coverage of the ZnO on the Al 2 O 3 aerogel framework. In addition, we found that both FIB methods create sample artifacts, although the distortion was less with Xe plasma than Ga ion FIB.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Simulations of coherent scattering experiments at storage ring synchrotron radiation sources in the hard x-ray range

Detailed simulations of experiments carried out at modern light sources are directly related to the most efficient and productive use of these facilities for research in multiple branches of science and technology. The “Synchrotron Radiation Workshop” computer code with its Python interface, and Sirepo web-browser-based graphical user interface, currently supports physical optics simulations of coherent X-ray scattering and imaging experiments on user-defined virtual samples. We present examples of simulations of coherent scattering experiments that are typically performed at the Coherent Hard X-ray beamline at Brookhaven National Laboratory’s (BNL) National Synchrotron Light Source II. We also present several comparisons of the simulations with the results of actual coherent X-ray scattering experiments with nano-fabricated test samples produced at BNL’s Center for Functional Nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of a 1D-imaging high-energy x-ray backlighter driven by the National Ignition Facility Advanced Radiographic Capability laser

Plastic deformation of samples compressed to Mbar pressures at high strain rates at the National Ignition Facility (NIF) forms the basis of ongoing material strength experiments in conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Hard x-ray radiography is the primary means of measuring the evolution of these samples, typically employing a slit-collimated high-Z microdot driven by the NIF laser to generate >40 keV x rays [E. Gumbrell et al., Rev. Sci. Instrum. 89, 10G118 (2018) and C. M. Huntington et al., Rev. Sci. Instrum. 89, 10G121 (2018)]. Alternatively, a dysprosium “micro-flag” target driven by the Advanced Radiographic Capability laser (~2 kJ, 10 ps) can deliver significantly higher spatiotemporal resolution [M. P. Hill et al., Rev. Sci. Instrum. 92, 033535 (2021)], especially in high-opacity samples. Here, initial experiments revealed problematic brightness and spectral gradients from this source, but by radiographing a set of diamond-turned, 105 µm-thick Pb test objects and supported by simulations using the 3D Monte Carlo code GEANT4, these geometry-dependent gradients across the field of view are quantified and mitigation strategies are assessed. In addition to significantly enhancing the modulation transfer function compared to the existing system, image stacking from multiple layers of image plate is shown to almost double the signal to noise ratio that will reduce uncertainties in future dynamic strength experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic information from a style effectively or suffer from high computational costs and inefficiencies in feature disentanglement due to using pre-trained models. This work proposes a lightweight but effective model, AesFA---Aesthetic Feature-Aware NST. The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely. Finally, to improve the network's ability to extract more distinct representations and further enhance the stylization quality, this work introduces a new aesthetic feature: contrastive loss. Extensive experiments and ablations show the approach not only outperforms recent NST methods in terms of stylization quality, but it also achieves faster inference. Codes are available at https://github.com/Sooyyoungg/AesFA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Real space iterative reconstruction for vector tomography (RESIRE-V)

Tomography has had an important impact on the physical, biological, and medical sciences. To date, most tomographic applications have been focused on 3D scalar reconstructions. However, in some crucial applications, vector tomography is required to reconstruct 3D vector fields such as the electric and magnetic fields. Over the years, several vector tomography methods have been developed. Here, we present the mathematical foundation and algorithmic implementation of REal Space Iterative REconstruction for Vector tomography, termed RESIRE-V. RESIRE-V uses multiple tilt series of projections and iterates between the projections and a 3D reconstruction. Each iteration consists of a forward step using the Radon transform and a backward step using its transpose, then updates the object via gradient descent. Incorporating with a 3D support constraint, the algorithm iteratively minimizes an error metric, defined as the difference between the measured and calculated projections. The algorithm can also be used to refine the tilt angles and further improve the 3D reconstruction. To validate RESIRE-V, we first apply it to a simulated data set of the 3D magnetization vector field, consisting of two orthogonal tilt series, each with a missing wedge. Our quantitative analysis shows that the three components of the reconstructed magnetization vector field agree well with the ground-truth counterparts. We then use RESIRE-V to reconstruct the 3D magnetization vector field of a ferromagnetic meta-lattice consisting of three tilt series. Our 3D vector reconstruction reveals the existence of topological magnetic defects with positive and negative charges. We expect that RESIRE-V can be incorporated into different imaging modalities as a general vector tomography method. To make the algorithm accessible to a broad user community, we have made our RESIRE-V MATLAB source codes and the data freely available at https://github.com/minhpham0309/RESIRE-V.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A New Miniaturized Acoustic Transmitter for Marine Animal Tracking

Most marine renewable energy (MRE) technologies are still in their infancy. Many uncertainties remain regarding MRE systems’ impacts on the marine environment and species where they are deployed, which is one of the major hurdles for these systems to be widely adopted at a commercial scale. Currently, the primary technologies to monitor marine animals’ behavior around existing or potential MRE deployment sites are optical and acoustic imaging. However, these methods cannot identify individual animals and thus are less useful in identifying protected species and studying their behavior. Implantable acoustic transmitters with unique identification codes could serve as a complementary technology. To minimize potential bias introduced to the study results, the implanted transmitters must be sufficiently small and light to not affect the animals’ behavior while still having acceptable signal strengths and service life. Recently, we developed a new miniaturized acoustic transmitter that offers a significantly improved signal strength and service life (140 days [projected value] at a 5-s transmission interval) in a smaller and lighter package (0.45 g in the air), compared to the existing technologies. The detection range and detection efficiency of the new transmitter were tested in an actual marine environment and demonstrated a detection range of up to 330 m, a 65% improvement over the existing commercial counterparts operating at similar frequencies. Here, we report the design principles, performance, and manufacturing procedure of the transmitter. Finally, theoretical estimates of the detection range, the actual range, and detection efficiency results from the field testing are presented.

acoustic telemetry↗
DOE OSTI2025

Data Files for Runoff Evaluation in an Earth System Land Model for Permafrost Regions

Modeling of hydrological runoff is essential for accurately capturing spatiotemporal feedbacks within the land–atmosphere system, particularly in sensitive regions such as permafrost landscapes. However, substantial uncertainties persist in the terrestrial runoff parameterization schemes used in Earth system and land surface models. This is particularly true in permafrost regions, where landscape heterogeneity is high and reliable observational data are scarce.This data set includes all files that were produced and applied in the paper Runoff Evaluation in an Earth System Land Model for Permafrost Regions [Xiang et al. in review]. The paper is in review as of July 1 2025 in Geoscientific Model Development (GMD). In this study, we evaluate the performance of runoff parameterization schemes in the Energy Exascale Earth System Model (E3SM) land model (ELM). Our proposed framework leverages simulation results from the Advanced Terrestrial Simulator (ATS), which is a physics-rich integrated surface/subsurface hydrologic model that has been successfully evaluated previously in Arctic tundra regions. We used ATS to simulate runoff from 22 representative hillslopes in the Sagavanirktok River basin, located on the North Slope of Alaska, then compared the output with ELM’s parameterized representation of total runoff. This dataset contains 2 figure image files (*.png, *jpg) that describe the study site and methods, as well as folders (Figure*.zip) that contain the associated data files (*.csv, *.dat) and python code notebooks (*.ipynb) for figures 3-7 in the paper. Jupyter notebook (*.ipynb) files that produce the figure files using the associated data files will run within a python environment configured with Jupyter Lab or Notebook packages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2022

Magnetic state switching in FeGa microstructures

This work demonstrates that magnetoelectric composite heterostructures can be designed at the length scale of 10µms that can be switched from a magnetized state to a vortex state, effectively switching the magnetization off, using electric field induced strain. This was accomplished using thin film magnetoelectric heterostructures of Fe 81.4 Ga 18.6 on a single crystal (011) [Pb(Mg 1/3 Nb 2/3 )O 3 ] 0.68 -[PbTiO 3 ] 0.32 (PMN-32PT) ferroelectric substrate. Here, the heterostructures were tripped from a multi-domain magnetized state to a flux closure vortex state using voltage induced strain in a piezoelectric substrate. FeGa heterostructures were deposited on a Si-substrate for superconducting quantum interference device magnetometry characterization of the magnetic properties. The magnetoelectric coupling of a FeGa continuous film on PMN-32PT was characterized using a magneto optical Kerr effect magnetometer with bi-axial strain gauges, and magnetic multi-domain heterostructures were imaged using x-ray magnetic circular dichroism—photoemission electron microscopy during the transition to the vortex state. The domain structures were modelled using MuMax 3 , a micromagnetics code, and compared with observations. The results provide considerable insight into designing magnetoelectric heterostructures that can be switched from an ‘on’ state to an ‘off’ state using electric field induced strain.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Jupiter in the Ultraviolet: Acetylene and Ethane Abundances in the Stratosphere of Jupiter from Cassini Observations between 0.15 and 0.19 μm

At wavelengths between 0.15 and 0.19 μm, the far-ultraviolet spectrum of Jupiter is dominated by the scattered solar spectrum, attenuated by molecular absorptions primarily by acetylene and ethane, and to a lesser extent ammonia and phosphine. We describe the development of our radiative transfer code that enables the retrieval of abundances of these molecular species from ultraviolet reflectance spectra. As a proof-of-concept we present an analysis of Cassini Ultraviolet Imaging Spectrograph (UVIS) observations of the disk of Jupiter during the 2000/2001 flyby. The ultraviolet-retrieved acetylene abundances in the upper stratosphere are lower than those predicted by models based solely on infrared thermal emission from the mid-stratosphere observed by the Composite Infrared Spectrometer (CIRS), requiring an adjustment to the vertical profiles above 1 mbar. We produce a vertical acetylene abundance profile that is compatible with both CIRS and UVIS, with reduced abundances at pressures <1 mbar: the 0.1 mbar abundances are 1.21 ± 0.07 ppm for acetylene and 20.8 ± 5.1 ppm for ethane. Finally, we perform a sensitivity study for the JUICE ultraviolet spectrograph, which has extended wavelength coverage out to 0.21 μm, enabling the retrieval of ammonia and phosphine abundances, in addition to acetylene and ethane.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Deep-learning-based image registration for nano-resolution tomographic reconstruction

Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. We report the method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.

deep learning↗
DOE OSTI2023

Investigation of Nanoparticle Degradation in Hydrogen Fuel Cell Systems through Automated Electron Microscopy

Proton exchange membrane fuel cells (PEMFC) are promising devices for the deployment of hydrogen-powered heavy-duty vehicles, providing a higher efficiency for similar driving range and fueling time than the existing ones. However, PEMFCs still encounter durability challenges mainly due to catalyst degradation in the cathode. Mitigating these performance losses requires a better understanding of the degradation mechanisms under heavy-duty accelerated stress tests (ASTs) [1]. Scanning transmission electron microscopy (STEM) combined with energy dispersive X-ray spectroscopy (EDS) are key tools for the analysis of Pt and PtCo nanoparticle size, spatial distribution and composition [2]. Electron tomography is also used to determine the rate and type of degradation of catalyst nanoparticles as a function of their position on the carbon support. In this work, automated data acquisition software, paired with a custom Python code, have been used to study the effect of different accelerated stress tests (ASTs) on nanoparticle coarsening [2]. Figure 1 shows high-angle annular dark-field (HAADF)-STEM images and EDS maps comparing the cathodes of membrane electrode assemblies (MEAs) following an electrocatalyst AST performed under H2/N2 with that of the heavy-duty AST performed under H2/air. We will discuss how AST conditions affect considerably the spatial distribution of the nanoparticles across the electrode between the membrane and microporous layer. Although the median particle size increased more in the MEA aged under the heavy-duty AST, as determined using a high-throughput image analysis, the quantitative EDS measurements demonstrate that the electrocatalyst AST resulted in more Pt and Co dissolution from the cathode, which is another important indicator of electrocatalyst degradation. We will further present the impact of the relative humidity (% RH) on the degradation mechanisms demonstrated using the same approach. Electron tomography has been used to distinguish the Pt nanoparticles residing on the carbon support surface (exterior) from those within the pore structure (interior) in order to determine the relative stability of interior and exterior nanoparticles. As shown in Figure 2, we will compare the Pt catalyst particle size at the beginning of test (BOT) and end of test (EOT), and discuss the importance of automating the electron tomography workflow, i.e. acquisition, reconstruction, and visualization, to increase sampling and determine the standard deviation of these measurement. The outlook for utilizing low-dose cryo-tomography for limiting damage to the catalyst, support, and especially proton-conducting ionomer will also be discussed [3].

Amichi, Lynda↗
DOE OSTI2025

Measurement of directional muon beams generated at the Berkeley Lab Laser Accelerator

We present the detection of directional muon beams produced using a PW laser facility at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a laser-plasma accelerator interacting with a high- converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that are in excellent agreement with the experiments, we demonstrate that laser-plasma accelerators have the capability of generating electron beams with characteristics suitable to produce GeV-scale muons that offer unique advantages with respect to the cosmic background. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high yields, which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the eant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

Terzani, Davide↗