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DOE OSTI2026

Monte Carlo simulation methodology for characterizing the energy response function and resolving power of a digital Flat Panel Detector (FPD) assembly

The use of high-resolution, digital flat panel detectors (FPDs) makes quantitative assessments of radiographic and 3D tomographic representations feasible. However, the energy response function and spatial resolving power of the detector must be known. Here, we present a methodology for characterizing an FPD assembly using Monte Carlo simulations. The detector assembly modeled here was fielded in the 3-Ring Flash X-ray Imaging System designed and built at Lawrence Livermore National Laboratory (LLNL), which employs fifteen Carestream ® HPX-DR3543 FPDs enclosed in protective housings. Detailed Monte Carlo (MCNP6) simulations of an as-fielded detector assembly were performed to estimate its energy response function in terms of analog-to-digital unit (ADU) “counts” per pixel per incident x-ray as a function of energy up to ∼ 500 keV and its theoretical (idealized) resolving power. The practical resolving power of the FPD assembly in a realistic scenario was then estimated by simulating radiographic images of a variant of the United Kingdom (U.K.) “Kaleidoscope” resolution target.

Physics↗

DOE OSTI2021

Bragg coherent diffraction imaging by simultaneous reconstruction of multiple diffraction peaks

Bragg coherent diffractive imaging (BCDI) is a non-invasive microscopy technique that can visualize the morphology and internal lattice deviations of crystals with nanoscale spatial resolution and picometer deformation sensitivity. While BCDI has been successfully applied in various studies of materials, it is less successful for highly-strained crystals. Specifically, it is difficult to correctly reconstruct the electron density of a highly-strained object using conventional phase retrieval algorithms. Although various algorithms have been developed to overcome this challenge, most of them require a priori knowledge that is not always available in practice. Here we report on a new phase retrieval workflow that can invert diffraction patterns from multiple Bragg peaks simultaneously. The new workflow is explored via simulated diffraction from crystals with various strain conditions. Furthermore, reconstructions from the new workflow consistently demonstrate more accurate electron density maps, in comparison with the conventional method. For highly-strained crystals, the new workflow improves the reliability and consistency of BCDI phase retrieval significantly.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI

Disentangling human brain connectivity requires an accurate description of nerve fiber trajectories, unveiled via detailed mapping of axonal orientations. However, this is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer axonal connectivity because it is sensitive to axonal alignment, but it has limited spatial resolution and specificity. Scattered light imaging (SLI) and small-angle X-ray scattering (SAXS) reveal axonal orientations with microscopic resolution and high specificity, respectively. Here, we apply both scattering techniques on the same samples and cross-validate them, laying the groundwork for ground-truth axonal orientation imaging and validating dMRI. We evaluate brain regions that include unidirectional and crossing fibers in human and vervet monkey brain sections. SLI and SAXS quantitatively agree regarding in-plane fiber orientations including crossings, while dMRI agrees in the majority of voxels with small discrepancies. We further use SAXS and dMRI to confirm theoretical predictions regarding SLI determination of through-plane fiber orientations. Scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, facilitating detailed investigations of complex fiber architecture in the animal and human brain.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Uptake and speciation of zinc in edible plants grown in smelter contaminated soils

Heavy metal accumulation in edible plants grown in contaminated soils poses a major environmental risk to humans and grazing animals. This study focused on the concentration and speciation of Zn in different edible plants grown in soils contaminated with smelter wastes (Spelter, WV, USA) containing high levels of the metals Zn, Cu, Pb, Cd. Their accumulation was examined in different parts (roots, stem, and leaves) of plants and as a function of growth stage (dry seed, sprouting seed, cotyledon, and leaves) in the root vegetables radish, the leafy vegetable spinach and the legume clover. Although the accumulation of metals varied significantly with plant species, the average metal concentrations were [Zn] > [Pb] > [Cu] > [Cd]. Metal uptake studies were complemented with bulk and micro X-ray absorption spectroscopy (XAS) at Zn K-edge and micro X-ray fluorescence (μXRF) measurements to evaluate the speciation and distribution of Zn in these plant species. Dynamic interplay between the histidine and malate complexation of Zn was observed in all plant species. XRF mapping of spinach leaves at micron spatial resolution demonstrated the accumulation of Zn in vacuoles and leaf tips. Radish root showed accumulation of Zn in root hairs, likely as ZnS nanoparticles. At locations of high Zn concentration in spinach leaves, μXANES suggests Zn complexation with histidine, as opposed to malate in the bulk leaf. These findings shed new light on the dynamic nature of Zn speciation in plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Quantitative assessment of selenium diffusion and passivation in CdSeTe solar cells probed by spatially resolved cathodoluminescence

The introduction of selenium in CdSeTe/CdTe solar cells has led to improved device performances attributed to the passivation of bulk defects. In this work, high-resolution cathodoluminescence experiments are performed on a series of CdSeTe/CdTe thin films with different Se concentrations to quantify the mechanisms and the passivation role of Se. We demonstrate a universal dependence between the Se concentration and the radiative efficiency and a ten-fold enhancement of the luminescence between CdTe and CdSe 0.4 Te 0.6 . Raw luminescence maps are converted into maps of the Se concentration, revealing its graded profile within the stack. We demonstrate the diffusion of Se along CdTe grain boundaries induced by the cadmium chloride annealing treatment and determine the diffusion coefficients, which are more than eight times higher at grain boundaries than in grain interiors. These results provide microscopic insights into the distribution of Se and its impact on the passivation of CdSeTe/CdTe solar cells.

14 SOLAR ENERGY↗

DOE OSTI2026

Data for Yield from Iowa’s first commercial miscanthus fields: implications of spatial variability for productivity and sustainability beyond research plots

This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)

Potential yield, yield gap, in-field management, y↗

DOE OSTI2022

H α emission in local galaxies: star formation, time variability, and the diffuse ionized gas

ABSTRACT The nebular recombination line H α is widely used as a star formation rate (SFR) indicator in the local and high-redshift Universe. We present a detailed H α radiative transfer study of high-resolution isolated Milky-Way and Large Magellanic Cloud simulations that include radiative transfer, non-equilibrium thermochemistry, and dust evolution. We focus on the spatial morphology and temporal variability of the H α emission, and its connection to the underlying gas and star formation properties. The H α and H β radial and vertical surface brightness profiles are in excellent agreement with observations of nearby galaxies. We find that the fraction of H α emission from collisional excitation amounts to fcol ∼ 5–$10{{\ \rm per\ cent}}$, only weakly dependent on radius and vertical height, and that scattering boosts the H α luminosity by $\sim 40{{\ \rm per\ cent}}$. The dust correction via the Balmer decrement works well (intrinsic H α emission recoverable within 25 per cent), though the dust attenuation law depends on the amount of attenuation itself both on spatially resolved and integrated scales. Important for the understanding of the H α–SFR connection is the dust and helium absorption of ionizing radiation (Lyman continuum [LyC] photons), which are about $f_{\rm abs}\approx 28{{\ \rm per\ cent}}$ and $f_{\rm He}\approx 9{{\ \rm per\ cent}}$, respectively. Together with an escape fraction of $f_{\rm esc}\approx 6{{\ \rm per\ cent}}$, this reduces the available budget for hydrogen line emission by nearly half ($f_{\rm H}\approx 57{{\ \rm per\ cent}}$). We discuss the impact of the diffuse ionized gas, showing – among other things – that the extraplanar H α emission is powered by LyC photons escaping the disc. Future applications of this framework to cosmological (zoom-in) simulations will assist in the interpretation of spectroscopy of high-redshift galaxies with the upcoming James Webb Space Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Ordering effects on deformation substructures and strain hardening behavior of a CrCoNi based medium entropy alloy

Here, a CrCoNi based medium entropy alloy with small additions of Ti, Al and Nb (denoted as (CrCoNi) 93 Al 4 Ti 2 Nb) in the as-quenched condition, exhibits tensile properties comparable to those of the equiatomic CrCoNi alloy at room temperature. Dark field transmission electron microscopy (TEM), atomic resolution high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) together with atom probe tomography (APT) show that spatially-localized long range ordering (LRO) L1 2 domains exist in this alloy. The evolution of deformation substructure with plastic deformation in this alloy was characterized using electron backscatter diffraction (EBSD), electron channeling contrast imaging (ECCI) and STEM based techniques including the recently developed weak beam dark field STEM imaging. Plastic deformation occurs by the slip of a/2<110>dislocations, which are narrowly dissociated into Shockley partial dislocations on {111} slip planes. Their dissociation distances in the (CrCoNi) 93 Al 4 Ti 2 Nb alloy are much smaller than the widths of the corresponding partials in the equiatomic CrCoNi alloy due to one or more of the minor alloying elements (Al, Ti, Nb). Dislocation slip in this alloy has a pronounced planar character. The leading dislocations in slip bands glide as pairs due to the existence of LRO domains. Multipoles were formed through the slip of dislocations with opposite signs on adjacent {111} slip planes. Those multipoles serve as building blocks for the formation of subgrain structures consisting of fine slip bands. The distances between slip bands were continuously refined during plastic deformation and dynamic refinement of slip bands plays a crucial role in strain hardening. The effects of LRO domains on planar dislocation slip, the deactivation of deformation twinning and strain hardening of this alloy are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Order-disorder behavior at thin film oxide interfaces

Order-disorder processes fundamentally determine the structure and properties of many important oxide systems for energy and computing applications. While these processes have been intensively studied in bulk materials, they are less investigated and understood for nanostructured oxides in highly non-equilibrium conditions. These systems can now be realized through a range of deposition techniques and probed at exceptional spatial and chemical resolution, leading to a greater focus on interface dynamics. In this work, we survey a selection of recent studies of order-disorder behavior at thin film oxide interfaces, with a particular emphasis on the emergence of order during synthesis and disorder in extreme irradiation environments. We summarize key trends and identify directions for future study in this growing research area.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Hillslope-Channel Transitions and the Role of Water Tracks in a Changing Permafrost Landscape

The Arctic is experiencing rapid climate change, and the effect on hydrologic processes and resulting geomorphic changes to hillslopes and channels is unclear because we lack quantitative models and theory for rapid changes resulting from thawing permafrost. Here, the presence of permafrost modulates water flow and the stability of soil-mantled slopes, implying that there should be a signature of permafrost processes, including warming-driven disturbance, in channel network extent. To inform understanding of hillslope-channel dynamics under changing climates, we examined soil-mantled hillslopes within a ~300 km 2 area of the Seward Peninsula, western Alaska, where discontinuous permafrost is particularly susceptible to thaw and rapid landscape change. In this study, we pair high-resolution topographic and satellite data to multi-annual observations of InSAR-derived surface displacement over a 5-year period to quantify spatial variations in topographic change across an upland landscape. We find that neither the basin slope nor the presence of knickzones controls the magnitude of recent surface displacements within the study basin, as may be expected under conceptual models of temperate hillslope evolution. Rather, the highest displacement magnitudes tended to occur at the broad hillslope-channel transition zone. In this study area, this zone is occupied by water tracks, which are zero-order ecogeomorphic features that concentrate surface and subsurface flow paths. Our results suggest that water tracks, which appear to occupy hillslope positions between saturation and incision thresholds, are vulnerable to warming-induced subsidence and incision. We hypothesize that gullying within water tracks will outpace infilling by hillslope processes, resulting in the growth of the channel network under future warming.

58 GEOSCIENCES↗

DOE OSTI2023

Direct implicit and explicit energy-conserving particle-in-cell methods for modeling of capacitively coupled plasma devices

Achieving large-scale kinetic modeling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications, such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving particle-in-cell method suffers from restrictive stability constraints on spatial cell size and temporal time step, requiring resolution of the electron Debye length and electron plasma period, respectively. This results in a very high computational cost, making the technique prohibitive for large volume device modeling. We investigate the direct implicit algorithm and the explicit energy conserving algorithm as alternatives to the standard approach, both of which can reduce computational cost with a minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated via 2D capacitively coupled plasma discharge simulations. The investigation reveals that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in a reduced runtime, while incurring only minor inaccuracies in plasma parameters. The direct implicit method also allows for time steps larger than the electron plasma period; however, care must be taken to avoid numerical heating or cooling. It is demonstrated that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to an acceptable level.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

iMaNGA: mock MaNGA galaxies based on IllustrisTNG and MaStar SSPs – II. The catalogue

ABSTRACT Strengthening the synergy between simulations and observations is essential to test galaxy formation and evolution theories. To achieve this goal, in the first paper of this series, we presented a method to generate mock SDSS-IV/Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) integral-field spectroscopic galaxy observations from cosmological simulations. In this second paper, we build the iMaNGA catalogue consisting of ∼1000 unique galaxies from the TNG50 cosmological simulations, selected to mimic the SDSS-IV/MaNGA-Primary sample selection. Here, we present and discuss the iMaNGA sample and its comparison to the MaNGA Primary catalogue. The iMaNGA sample well recovers the MaNGA-Primary sample in terms of stellar mass versus angular size relation and spatial resolution. The Sérsic index versus angular size relation, instead, is not reproduced well by the simulations, mostly because of a paucity of high-mass elliptical galaxies in TNG50. We also investigate our ability to recover the galaxy kinematics and stellar population properties with full-spectral fitting. We demonstrate that ‘intrinsic’ and ‘recovered’ stellar kinematics, stellar ages, and metallicities are consistent, with residuals compatible with zero within 1σ. Also ‘intrinsic’ and ‘recovered’ star formation histories display a great resemblance. We conclude that our mock generation and spectral fitting processes do not distort the ‘intrinsic’ galaxy properties. Therefore, in the third paper of this series, we can meaningfully test the cosmological simulations, comparing the stellar population properties and kinematics of the iMaNGA mock galaxies and the MaNGA observational results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic instruments observing fusion plasmas. Here we apply this neural network architecture to the popular problem of disruption prediction in fusion tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. ECEi measures a fundamental plasma quantity (electron temperature) with high temporal resolution over the entire plasma discharge, making it sensitive to a number of potential pre-disruptions markers with different temporal and spatial scales. Promising, initial disruption prediction results are obtained training a deep CNN with large receptive field ({$\sim$}30k), achieving an $F_1$-score of {$\sim$}91\% on individual time-slices using only the ECEi data.

convolutional neural networks↗

DOE OSTI2021

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020

The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly available remote sensing products lack the necessary temporal resolution to assess fine-scale growing season dynamics. Near-surface sensing methods provide a middle ground with high temporal frequency and tractable spatial scales (from sub-meter to hillslope scale). This data package includes csv files of Normalized Difference Vegetation Index (NDVI) timeseries (SM_NDVI_dailymax.csv) and phenological thresholds (SM_NDVI_summary.csv) for sub-plots (1m2) and Green Chromatic Coordinate (GCC) phenological thresholds (SM_GCC_summary.csv) at the plot scale (10m x 14m). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020” and “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018”.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Chemical and bonding analysis of liquids using liquid cell electron microscopy

Liquid cell transmission electron microscopy (TEM) has become an essential tool for studying the structure and properties of both hard and soft condensed-matter samples, as well as liquids themselves. Liquid cell sample holders, often consisting of two thin window layers separating the liquid sample from the high vacuum of the microscope column, have been designed to control in situ conditions, including temperature, voltage/current, or flow through the window region. While high-resolution and time-resolved TEM imaging probes the structure, shape, and dynamics of liquid cell samples, information about the chemical composition and spatially resolved bonding is often difficult to obtain due to the liquid thickness, the window layers, the holder configuration, or beam-induced radiolysis. Here, we review different approaches to quantitative liquid cell electron microscopy, including recent developments to perform energy-dispersive x-ray and electron energy-loss spectroscopy experiments on samples in a liquid environment or the liquid itself. We also cover graphene liquid cells and other ultrathin window layer holders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗

DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Time-resolved X-ray imaging of the current filamentation instability in solid-density plasmas

The streaming of energetic charged particles can magnetize astrophysical and laboratory plasmas via the current filamentation instability. Despite its importance, the experimental characterization of this instability has remained a challenge. Here, we report an experiment combining a high-intensity optical laser with a high-brightness X-ray free electron laser that successfully images the instability in solid-density plasmas with 200nm spatial and 50fs temporal resolution. We characterize the development of µm-scale filamentary structures and their evolution over tens of picoseconds through a non-linear merging process. The measured plasma density modulations and long merging time reveal the critical importance of space-charge effects and ion motion on this electron-driven instability. Supporting theoretical analysis and kinetic simulations help distinguish the relative role of space-charge and resistive effects. Our findings indicate that magnetic fields on the order of 10megagauss are produced, with important implications for transport and radiation emission of energetic particles in plasmas.

Schoenwaelder, Christopher↗