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DOE OSTI2026

Emerging hierarchical dislocation structures: Insights from scanning electron microscopy-electron backscatter diffraction in situ tensile testing and multifractal analysis

Understanding the evolution of dislocation structures during plastic deformation is critical for predicting the mechanical performance of metallic materials. In this work, we applied in situ scanning electron microscopy/electron backscatter diffraction tensile testing combined with multifractal (MF) analysis to assess deformation-induced dislocation structure evolution in solution-annealed 304 L stainless steel, both in its as-received and neutron-irradiated states (5.4 displacements per atom). The analysis of kernel average misorientation patterns revealed the formation of hierarchical dislocation arrangements that exhibit clear MF scaling behavior. Despite pronounced visual differences between nonirradiated and irradiated specimens—most notably, the appearance of dislocation channels after irradiation—the singularity spectra suggest that both conditions give rise to similar underlying hierarchical structures. MF analysis provides a quantitative measure of the spatial complexity and self-organization of dislocation patterns, highlighting the accelerated emergence and evolution of the dislocation structures in irradiated polycrystalline materials, as well as the limitation of their spatial extent. The findings indicate that irradiation not only modifies microstructure but also alters correlation-driven dislocation organization. More generally, they demonstrate that MF analysis is a powerful tool for probing mesoscale deformation mechanisms.

Dislocation structures↗

NASA NTRS1995

Searching for patterns in remote sensing image databases using neural networks

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

Paola, Justin D.↗

NASA NTRS2007

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

Sun, Guoqing↗

DOE OSTI2025

High resolution variability in wet deposition in the southeastern United States

Rainwater chemistry is determined by atmospheric pollutants and particles which vary spatially and temporally. Industrial and agricultural activities and meteorological events (e.g. sea breezes, severe weather, blowing dust) alter atmospheric particle and trace gas compositions. These gases and particles are scavenged by cloud and rain droplets that drive wet deposition. During an Intensive Operation Period (IOP) from April to October 2021, rainwater was collected at higher frequency intervals, usually daily, after precipitation events at three locations on the Savannah River Site (SRS). The farthest locations were separated by approximately 20 km. The mean concentration (μeq/L) of seven ions followed the Cl⁻ > SO 4 2− > Na⁺ > NO 3 ⁻ > K⁺ > Mg 2+ > Ca 2+ downward trend. Ion concentrations were compared to background ion concentrations from the National Atmospheric Deposition Program (NADP). The high frequency monthly averaged SRS data compared well with the monthly averaged NADP background but demonstrated extensive variability. In some months in 2021, the high frequency data compared better to the NADP site near the coast while in other months inland sites compared better. Strong spatial variability for ion concentrations was observed across SRS which was attributed to localized impacts in rainfall spatial variability. High frequency measurements allowed for comparison to regional weather patterns indicating influences from the Atlantic Ocean, Gulf of Mexico, and cities. This can account for spatial variability in the wet deposition flux. Sea breezes, Saharan dust, and anthropogenic sources were shown to impact wet deposition flux variability. Higher frequency precipitation chemistry sampling at numerous locations better captures ion concentration variability and improves measurement representativeness.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Prospects for Jovian seismological observations following the impact of comet Shoemaker-Levy 9

The impact of each fragment of comet SL-9 will produce a downward-propagating pressure wave which will travel at the sound speed through the jovian interior. Since the sound speed increases with depth, most of the energy in the pressure pulse will be strongly refracted and return to the surface, as recently computed by Marley (1994). This wave may in principle be observable as it propagates into the stratosphere, using sufficiently sensitive thermal infrared imaging. If so, it will provide a unique opportunity to constrain models of the jovian interior. This paper extends Marley's calculations to include the effect of the limited spatial resolution which will be characteristic of real observations. The wave pattern on the disk will consist of closely spaced regions of alternating temperature increases and decreases. Spatial averaging will significantly reduce the observed amplitude for resolutions attainable using earth-based telescopes, but the waves should remain above the detection limit.

Deming, Drake↗

DOE OSTI2023

Temporal Dynamics of Place and Mobility

Despite variations in the population, climate, economics, politics, and culture, every country and city around the world shares the same time constraints: there are only 24 hours per day. Yet, the time-dependent activity patterns of when people interact with or move between public, private, and commercial locations change across space and across spatial scales. The temporal dynamics of a place reveal unique patterns based on the complex social, economic, and cultural interactions of humans across the built environment. The continued expansion of multi-modal temporal and geospatial data has attracted many disciplines to study temporal dynamics, each with its own interests, data, methods, and use cases. A comprehensive understanding of how the temporal patterns of a place are created, disrupted, and evolve is reliant on disciplines collaborating and sharing their unique perspectives. This chapter highlights ongoing work in this field and proposes core research questions that should be pursued with the appropriate collaboration and synthesis of data.

Sparks, Kevin↗

NASA NTRS2002

Spatial Metadata for Global Change Investigations Using Remote Sensing

Satellite and aircraft-borne remote sensors have gathered petabytes of data over the past 30+ years. These images are an important resource for establishing cause and effect relationships between human-induced land cover changes and alterations in climate and other biophysical patterns at local to global scales. However, the spatial, temporal, and spectral characteristics of these datasets vary, thus complicating long-term studies involving several types of imagery. As the geographical and temporal coverage, the spectral and spatial resolution, and the number of individual sensors increase, the sheer volume and complexity of available data sets will complicate management and use of the rapidly growing archive of earth imagery. Mining this vast data resource for images that provide the necessary information for climate change studies becomes more difficult as more sensors are launched and more imagery is obtained.

Emerson, Charles W.↗

NASA NTRSDate not supplied

Unveiling Urban Pollution Patterns from a Bird’s-Eye View

Low-earth orbiting satellites have been observing the global distribution of nitrogen dioxide (NO2) since the 1990s and these observations have been used to identify pollution source regions, temporal trends, and links to health effects and community disparities. However, historically, space-based NO2 observations have been coarse in spatial and temporal resolution resulting in challenges in data interpretation and product validation; therefore, NASA developed airborne capabilities and measurements strategies to observe ultraviolet-visible (UV-VIS) trace gases at high temporal and spatial resolutions to unveiling never before seen spatiotemporal patterns over major urban hubs during recent air quality field studies. In this talk, Dr. Judd will share the story of these field studies with datasets collected from NASA’s airborne spectrometers, GeoTASO and GCAS, and how they integrate in with other space- and ground-based instrumentation to illustrate the conceptual model of air quality episodes in coastal urban environments, contribute to satellite validation and regional chemical transport model evaluation. These imagers have the capability to observe UV-VIS absorbing species, typically NO2 and HCHO, at spatial resolutions as fine as 250 m when observing from the upper-troposphere. Flight strategies allowed for the collection of repeated raster samples over urban areas multiple times per flight day in cities like Chicago, Los Angeles, and New York City, capturing never before seen pictures of the diurnal evolution of emissions, chemistry, and meteorology associated with NO2 heterogeneity. These airborne efforts help local air quality management better understand their air quality challenges as well as benefit the larger air quality community in preparation for geostationary air quality observations and the development of the global air quality observing system.

Laura Judd↗

DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

Rainfall↗

NASA NTRS1981

Antenna pattern modification for single reflector antennas

The problem of correcting the antenna temperatures observed with a single reflector is addressed from two viewpoints with emphasis on decreasing the level of the side lobes without greatly amplifying the noise. One viewpoint is that of matrix operations where the elements of the matrix are integrals of the antenna pattern. This formulation is directly applicable to developing algorithms. The other is that of an analysis of the problem by treating the antenna as a spatial filter and using Fourier transforms of the antenna pattern. This approach leads to an understanding of the problem, but is not suitable for performing calculations. The method developed can be used to correct data from mechanically-scanned microwave instruments that view Earth from space.

Milman, A. S.↗

DOE OSTI2025

3D nanolithography with metalens arrays and spatially adaptive illumination

The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here, in this study, we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL) beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm 2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 10 8 voxels s −1 . By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics, biomedicine, quantum technology and high-energy laser targets.

Materials science↗

NASA NTRS1994

Surface temperatures in the polar regions from Nimbus 7 temperature humidity infrared radiometer

Monthly surface temperatures in the Arctic and Antarctic regions have been derived from the 11.5 micrometer thermal infrared channel of the Nimbus 7 temperature humidity infrared radiometer (THIR) for a whole year in 1979 and for a winter and a summer month from 1980 through 1985. The data set shows interannual variability and provides spatial details that allow identification of temperature patterns over sea ice and ice sheet surfaces. For example, the coldest spot in the southern hemisphere is observed to be consistently in the Antarctic plateau in the southern hemisphere, while that in the northern hemisphere is usually located in Greenland, or one of three other general areas: Siberia, the central Arctic, or the Canadian Archipelago. Also, in the southern hemisphere, the amplitude of the seasonal fluctuation of ice sheet temperatures is about 3 times that of sea ice, while in the northern hemisphere, the corresponding fluctuations for the two surfaces are about the same. The main sources of error in the retrieval are cloud and other atmospheric effects. These were minimized by first choosing the highest radiance value from the set of measurements during the day taken within a 30 km by 30 km grid of each daily map. Then the difference of daily maps was taken and where the difference is greater than a certain threshold (which in this case is 12 C), the data element is deleted. Overall, the monthly maps derived from the resulting daily maps are spatially and temporally consistent, are coherent with the topograph y of the Antarctic continent and the location of the sea ice edge, and are in qualitative agreement with climatological data. Quantitatively, THIR data are in good agreement with Antarctic ice sheet surface air temperature station data with a correlation coefficient of 0.997 and a standard deviation of 2.0 C. The absolute values are not as good over the sea ice edges, but a comparison with Russian 2-m drift station temperatures shows very high correlation (with correlation coefficient at 0.998) and a standard deviation of 1.1 C. Overall, the rms error is estimated to be from 1 to 2 C, depending on the surface, while the average bias when compared with in situ data is less than 2 C.

Comiso, Josefino C.↗

DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Post-IOC space station: Models of operation and their implications for organizational behavior, performance and effectiveness

Issues of crew productivity during design work on space station are discussed. The crew productivity is defined almost exclusively in terms of human factors engineering and habitability design concerns. While such spatial environmental conditions are necessary to support crew performance and productivity, they are not sufficient to ensure high levels of crew performance and productivity on the post-Initial Operational Configurations (IOC) space station. The role of the organizational environment as a complement to the spatial environment for influencing crew performance in such isolated and confined work settings is examined. Three possible models of operation for post-IOC space station's organizational environment are identified and it is explained how they and space station's spatial environment will combine and interact to occasion patterns of crew behavior is suggested. A three phase program of research design: (1) identify patterns of crew behavior likely to be occasioned on post-IOC space station for each of the three models of operation; and (2) to determine proactive/preventative management strategies which could be adopted to maximize the emergence of preferred outcomes in crew behavior under each of the several spatial and organizational environment combinations.

Danford, S.↗

DOE OSTI2022

Laser-induced patterning for a diffraction grating using the phase change material of Ge 2 Sb 2 Te 5 (GST) as a spatial light modulator in X-ray optics: a proof of concept

The proposed X-ray spatial light modulator (SLM) concept is based on the difference of X-ray scattering from amorphous and crystalline regions of phase change materials (PCMs) such as Ge 2 Sb 2 Te 5 (GST). In our X-ray SLM design, the “ on” and “ off” states correspond to a patterned and homogeneous state of a GST thin film, respectively. The patterned state is obtained by exposing the homogeneous film to laser pulses. In this paper, we present patterning results in GST thin films characterized by microwave impedance microscopy and X-ray small-angle scattering at the Stanford Synchrotron Radiation Lightsource.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗