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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Feedback on Forty-year Long Clean-up Operations of a Contaminated Soil for Environmental Purpose - 20026

One of the CEA facilities in France is the place of miscellaneous mapping and clean-up operations since the 1980's. The final purpose of the conducted work deals with the environmental remediation of the building. In France, the absence of a regulatory framework for the management of sites polluted by radioactive substances, especially as the lack of release thresholds, has led to develop different approaches of soil radiological characterization. The geostatistical approach is part of them. Preliminary investigations are an unavoidable step prior to clearance and remediation. First, historical analysis enables to define site perimeters to investigate as well as to precisely locate expected contaminated parts. For radioactive pollutants, the media (water, soil, air) and transfer routes participate in the definition of the investigation perimeter and thus also need to be identified through a geological study. At the same time, functional analysis leads to choose the best measurement means which could provide useful information when combined with visual inspection. An equipment well adapted to easy-to-measure radionuclides as gamma emitters, such as gamma probe or on-site gamma spectrometry, is usually selected as a non-destructive assay. Coupled with spatial positions, processed on-site radiological data can be mapped. Data processing consists in mapping and kriging before a geostatistical analysis in order to identify the zones of interest to be targeted. Selected zones are not only linked to high level count rates but also to low level ones so that a pollution-free reference is known in the area. This data processing also enables to categorize waste types according to their origin and contamination levels. An expected volume of different waste then follows as well as the waste characterization equipments. In-depth investigations, e.g. core drillings and samplings, naturally ensue to aim at remediation optimization through the assessment of environmental impact. After clean-up operations, non-destructive assays as well as destructive samplings enable to check the effective clearance. Following this methodology in the present remediation work, results of the first non-destructive assay campaign highlighted a long-lived transuranic radionuclides contamination in the concrete flagstone. Successive non-destructive assays were then performed in the defined zones of interest. Count rates measured with a surface probe were first mapped. The best localization of destructive assays, i.e. core drillings at a 1 meter depth, came from this surface mapping followed by a geostatistical analysis. The concrete flagstone was totally removed. Then, the depth of soil to remove under the flagstone was optimized from the results of radiological activities measured in core drillings samples. A new surface mapping was then drawn after this partial soil excavation. The sand and demolition rubble samples, homogeneously constituted from the flagstone fragments, were analyzed by gamma spectrometry. Meanwhile, waste drums and other containers were also measured by gamma spectrometry and passive neutron measurement devices. All these results have brought miscellaneous pieces of information. In this context, major issues appear in terms of physical constraints such as the premises tininess as well as of soil sampling techniques and in terms of measurement performances assessment and the representativeness of homogeneous samples. The choice of radiological soil and waste characterization devices is described in the light of enhanced performances by keeping in mind radiation protection requirements and a willing to always optimize waste categories (very low level waste) and volume. The improvement of the characterization methods appears through this forty-year long work while sharing the feedback of encountered difficulties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A Scalable Gaussian Process Approach to Shear Mapping with MuyGPs

Analysis of cosmic shear is an integral part of understanding structure growth across cosmic time, which in turn provides us with information about the nature of dark energy. Conventional methods generate shear maps from which we can infer the matter distribution in the universe. Current methods (e.g., Kaiser–Squires inversion) for generating these maps, however, are tricky to implement and can introduce bias. Recent alternatives construct a spatial process prior for the lensing potential, which allows for inference of the convergence and shear parameters given lensing shear measurements. Realizing these spatial processes, however, scales cubically in the number of observations—an unacceptable expense as near-term surveys expect billions of correlated measurements. Therefore, we present a linearly scaling shear map construction alternative using a scalable Gaussian process prior called MuyGPs. MuyGPs avoids cubic scaling by conditioning interpolation on only nearest neighbors and fits hyperparameters using batched leave-one-out cross-validation. This work is the first step toward a full, scalable mass mapping method. We work in a simplified regime where we validate our method by interpolating and analyzing maps given noisy point-estimate data from all three shear fields, taken from a suite of N -body ray-tracing simulations. We also show that we can perform these operations at the scale of billions of galaxies on high-performance computing platforms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Warped and Hooked: Mapping the Magellanic Clouds in Three Dimensions Using Red Clump Stars

The Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) are the Milky Way’s nearest interacting galaxy pair, offering a unique laboratory for studying tidal effects on galactic disks. Despite extensive survey efforts, the 3D geometry of the Magellanic Clouds, particularly the putative warp of the LMC, remains poorly constrained due to incompleteness in their crowded centers and the low stellar density of their peripheries, which demand wide-field coverage. Using red clump (RC) stars as standard candles, corrected for age- and metallicity-dependent population effects with empirically calibrated color–magnitude relations and spatially resolved star formation histories, we construct the most detailed distance map of the Magellanic system to date. Based on ∼2.3 million RC stars from Gaia Data Release 3 combined with modern reddening maps, we measure median heliocentric distances of 50.62 ± 2.32 kpc for the LMC (to ∼23°) and 60.75 ± 2.85 kpc for the SMC (to ∼12°). The maps reveal substructures including the LMC Northern Arm, southern hooks, the Magellanic Bridge, and SMC peripheral overdensities, with refreshed distance estimates. Fitting the LMC disk within 7° yields a global inclination of $i=25\mathop{.}\limits^{^\circ }32\pm 0\mathop{.}\limits^{^\circ }10$ and a line-of-nodes position angle of $\theta = 142\mathop{.}\limits^{^\circ }34\pm 0\mathop{.}\limits^{^\circ }21$. Most strikingly, we find the LMC periphery is warped azimuthally into a U-shaped structure reaching a vertical amplitude of ∼7 kpc at a radius of ∼15 kpc. In future work, we will perform detailed comparisons with live N-body simulations to assess possible formation scenarios for the LMC warp.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data–Driven Velocity Model Evaluation Using K–Means Clustering

In this work, we develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.

58 GEOSCIENCES↗
DOE OSTI2020

Less than the sum of its parts: the dust-corrected H α luminosity of star-forming galaxies explored at different spatial resolutions with MaNGA and MUSE

ABSTRACT The H α and H β emission-line luminosities measured in a single integrated spectrum are affected in non-trivial ways by point-to-point variations in dust attenuation in a galaxy. This work investigates the impact of this variation when estimating global H α luminosities corrected for the presence of dust by a global Balmer decrement. Analytical arguments show that the dust-corrected H α luminosity is always underestimated when using the global H α/H β flux ratio to correct for dust attenuation. We measure this effect on 156 face-on star-forming galaxies from the Mapping Nearby Galaxies at APO (MaNGA) survey. At 1–2 kpc spatial resolution, the effect is small but systematic, with the integrated dust-corrected H α luminosity underestimated by 2–4 per cent (and typically not more than by 10 per cent), and depends on the specific star formation rate of the galaxy. Given the spatial resolution of MaNGA, these are lower limits for the effect. From Multi Unit Spectroscopic Explorer (MUSE) observations of NGC 628 with a resolution of 36 pc, we find the discrepancy between the globally and the point-by-point dust-corrected H α luminosity to be 14 ± 1 per cent, which may still underestimate the true effect. We use toy models and simulations to show that the true difference depends strongly on the spatial variance of the H α/H β flux ratio, and on the slope of the relation between H αluminosity and dust attenuation within a galaxy. Larger samples of higher spatial resolution observations are required to quantify the dependence of this effect as a function of galaxy properties.

Vale Asari, N.↗
DOE OSTI2024

One-dimensional mapping of femtosecond laser filaments using coherent microwave scattering

Here, this paper reports on the use of coherent microwave scattering (CMS) for spatially resolved electron number density measurements of elongated plasma structures induced at mid-IR femtosecond filamentation in air. The presented studies comprise one-dimensional mapping of laser filaments induced via 3.9 µm, 127.3 fs laser pulses at output energies up to 15 mJ. The axial electron number density was measured to be invariant (about 2 × 10 15 c⁢m −3 ) along the entire filament length and for all tested laser pulse energies 5–15 mJ, and the corresponding laser intensity in the middle portion of the filament was estimated to be nearly constant for 5–15 mJ pulse energies (about 30−40 TW/c⁢m 2 ). These findings support that intensity clamping conditions were achieved in the experiments. The proposed approach enables capabilities that are currently unavailable to perform absolute and longitudinally resolved measurements of electron number density in laser filaments and to precisely characterize conditions associated with self-focusing and intensity clamping.

plasma diagnostic techniques↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-resolution national mapping of natural gas composition substantially updates methane leakage impacts

Methane is emitted from oil and gas operations alongside heavier hydrocarbons and non-hydrocarbon gases, shaping emissions management decision-making, including air quality impacts. Yet, most assessments assume fixed gas composition, overlooking significant spatial and temporal variations. Here, we generate a high-resolution, data-driven map of natural gas composition across the United States, reconstructing methane, heavier hydrocarbons, and non-hydrocarbon species using spatio-temporal interpolation and oil-and-gas production patterns. Our approach is able to reduce composition prediction errors by 39% in terms of Mean Absolute Error (MAE) compared to standard techniques and reveals that methane loss rates have been underestimated by more than 50% in some regions. Beyond methane, we uncover substantial variability in co-emitted gases, exposing blind spots in current emissions inventories and emissions management frameworks. Our work enables more accurate emissions assessments, guides targeted measurement strategies, and informs emissions management decision-making. It also provides a general framework for prediction in environmental applications that integrate sparse measurements with auxiliary variables.

03 NATURAL GAS↗
DOE OSTI2021

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis

Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ~2-3x, as compared to random priors. We explore and compare three distinct new methods: (1) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (2) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (3) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current-voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current-voltage curves with a Schottky emission formula.

42 ENGINEERING↗
DOE OSTI2024

X-ray chemical imaging for assessing redox microsites within soils and sediments

Redox reactions underlie several biogeochemical processes and are typically spatiotemporally heterogeneous in soils and sediments. However, redox heterogeneity has yet to be incorporated into mainstream conceptualizations and modeling of soil biogeochemistry. Anoxic microsites, a defining feature of soil redox heterogeneity, are non-majority oxygen depleted zones in otherwise oxic environments. Neglecting to account for anoxic microsites can generate major uncertainties in quantitative assessments of greenhouse gas emissions, C sequestration, as well as nutrient and contaminant cycling at the ecosystem to global scales. However, only a few studies have observed/characterized anoxic microsites in undisturbed soils, primarily, because soil is opaque and microsites require µm-cm scale resolution over cm-m scales. Consequently, our current understanding of microsite characteristics does not support model parameterization. To resolve this knowledge gap, we demonstrate through this proof-of-concept study that X-ray fluorescence (XRF) 2D mapping can reliably detect, quantify, and provide basic redox characterization of anoxic microsites using solid phase “forensic” evidence. First, we tested and developed a systematic data processing approach to eliminate false positive redox microsites, i.e., artefacts, detected from synchrotron-based multiple-energy XRF 2D mapping of Fe (as a proxy of redox-sensitive elements) in Fe-“rich” sediment cores with artificially injected microsites. Then, spatial distribution of Fe II and Fe III species from full, natural soil core slices (over cm-m lengths/widths) were mapped at 1–100 µm resolution. These investigations revealed direct evidence of anoxic microsites in predominantly oxic soils such as from an oak savanna and toeslope soil of a mountainous watershed, where anaerobicity would typically not be expected. We also revealed preferential spatial distribution of redox microsites inside aggregates from oak savanna soils. We anticipate that this approach will advance our understanding of soil biogeochemistry and help resolve “anomalous” occurrences of reduced products in nominally oxic soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mesoscale Magnetostructural Phase Separation in Fe‐deficient Fe 5 GeTe 2

Two-dimensional van der Waals ferromagnet Fe 5-x GeTe 2 (F5GT) is promising for spintronic applications due to its high Curie temperature, layered structure, and ability to host complex magnetic textures. However, the origin of its sample-dependent magnetic anisotropy remains unclear, hindering control of its magnetic behavior. Here, we use spatially resolved cryogenic scanning transmission electron microscopy (STEM) to correlatively map magnetism, lattice structure, and chemistry across atomic-to-micron scales. We reveal that only mesoscale, not nanoscale, inclusions of a Fe-deficient secondary phase significantly modify magnetic behavior, establishing a previously unrecognized critical length scale. This phase separation, induced by quenching, leads to in-plane magnetic anisotropy, while slow cooling confines separation to a few nanometers and preserves out-of-plane anisotropy. These findings reconcile prior inconsistencies and establish a predictive framework for tuning magnetism in F5GT through thermal processing, with broader implications for controlling anisotropy in other two-dimensional magnetic materials.

2D ferromagnets↗
DOE OSTI2025

Modulation of thermal conductivity of iron-doped ß-Ga2O3 by helium-ion irradiation

This study examines the impact of helium-ion irradiation on the thermal conductivity of ß-Ga2O3. A laser-based spatial domain thermoreflectance technique is used to investigate thermal conductivity map for both un-irradiated and irradiated ß-Ga2O3, which are then validated against simulation results derived from density functional theory-based phonon transport simulations. Since helium bubble evolution was ob- served at the nanoscale using transmission electron microscopy, the simulation study was carried out on eight distinct helium-induced sites in ß-Ga2O3. Our findings indicate a reduction in thermal conductivity for the irradiated samples. Experimental results show a significant reduction in thermal conductivity in irradiated samples, with de- creases of approximately 25% along the [100] direction and 40% along [001] directions. Phonon transport simulations closely replicate these findings, particularly when helium occupying interstitial sites, predicting reductions of ˜53% along [100] and ˜50% along [001] directions. This work underscores the role of irradiation-induced microstructural changes in the heat transport properties of ß-Ga2O3 which is crucial for its application in sensor devices in extreme environments.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns

There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Rapid Accumulation of Soil Inorganics on Plastics: Implications for Plastic Degradation and Contaminant Fate

As plastics degrade in the environment, chemical oxidation of the plastic surface enables inorganics to adsorb and form inorganic coatings, likely through a combination of adsorption of minerals and in situ mineral formation. The presence of inorganic coatings on aged plastics has negative implications for plastics fate, hindering our ability to recycle weathered plastics and increasing the potential for plastics to adsorb contaminants. Here, inorganic coatings formed on terrestrially weathered polyethylene were characterized using synchrotron spectroscopy and microscopy techniques across spatial scales including optical microscopy, nano-X-ray-fluorescence mapping (nano-XRF), nano-X-ray absorption near edge structure (nano-XANES), and high-energy resolution fluorescence detected-XANES (HERFD-XANES). Results indicate a heterogeneous elemental distribution and speciation which includes inorganics common to soil terrestrial environments including iron oxides and oxyhydroxides, aluminosilicates, and carbonates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Imaging Valley Excitons in a 2D Semiconductor with Scanning Tunneling Microscope-Induced Luminescence

Valley excitons dominate the optoelectronic response of transition-metal dichalcogenides and are drastically affected by structural and environmental inhomogeneities localized in these materials. Critical to understanding and controlling these nanoscale excitonic changes is the ability to correlate the imaging of excitonic states with crystalline structures on the atomic scale. Here, in this study, we apply scanning tunneling microscope-induced luminescence microscopy to image valley excitons in a semiconducting transition-metal dichalcogenide monolayer decoupled by a 10 nanometer-thick hexagonal-boron-nitride flake incorporated in a lateral homojunction on an Au electrode surface. This design enables the observation of chiral excitonic emission arising from neutral and charged valley excitons of the monolayer semiconductor at ambipolar voltages with a quantum efficiency up to similar to ~10 -5 photon/electron. The measured light helicity demonstrates considerable circular polarization dependent on the sample voltage, reaching as much as 40%. The real-space luminescence imaging maps$-$at subnanometer resolution$-$of the valley excitons reveal striking spatial variations associated with localized inhomogeneities, including surface impurities and possibly nanoscale dielectric and/or potential disorders in the monolayer. Our study introduces a promising format for 2D materials to explore and tailor their optoelectronic processes at the atomic scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Multi-modal characterization of nitrate reduction nano-catalysts with periodic strain distribution

Strain engineering serves as a pivotal strategy to optimize catalytic activity in electrocatalysis. However, the catalyst sizes under industrial conditions are usually large and even beyond nanometer regime. The critical methodological limitations on strain imaging of such catalysts with both large field of view and high spatial resolution obscure the mechanistic understanding of strain-performance correlations. Here, we present an optimized four-dimensional scanning transmission electron microscopy (4D-STEM) method to acquire strain mapping of both bulk and surface across particles up to 500 nm with 0.6 nm spatial resolution and 0.55% precision. We observe the ripple-like periodic strain coupled with elemental fluctuations inside a perovskite-type hydroxide CuCoSn(OH) 6 and find it correlated to electrocatalytic nitrate reduction (NO 3 – RR) absorption energy to achieve the 92.6% Faradaic efficiency and long-term test over 1000 h at membrane electrode assembly (MEA) for ammonia electrosynthesis. This universal framework design offers a practical method that not only develops an advanced measurement combining multi-modal characterization techniques but also reveals the intrinsic structure-property constitutive law of industry-level catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗