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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 OSTI2017

Resolution Versus Error for Computational Electron Microscopy

Images that are collected via scanning transmission electron microscopy (STEM) can be undersampled to avoid damage to the specimen while maintaining resolution [1, 2]. We have used BPFA to impute missing data and reduce noise [3]. The reconstruction is typically evaluated using the peak signal-to-noise ratio (PSNR). This measure is too conservative for STEM images and we propose that the Fourier ring correlation (FRC) is used instead to evaluate the reconstruction. We are not concerned with exact reconstruction of the truth image, and therefore PSNR is a conservative estimation of the quality of the reconstruction. Instead, we are concerned with the visual resolution of the image and whether atoms can be distinguished. We have evaluated the reconstruction of a simulated STEM image using the FRC and compared the results with the PSNR measurements. The FRC captures the resolution of the image and is not affected by a large MSE if the atom peaks are still distinguishable. The noisy and reconstructed images are shown in Figure 1. The simulated STEM image was sampled at 100%, 80%, 40%, and 20% of the original pixels to simulate an undersampled scan. The reconstruction was done using BPFA with a patch size of 10 x 10 and no overlapping patches. Not having overlapping patches produces inferior results but they are still acceptable. The dictionary size is 64 and 30 iterations were completed during each reconstruction. The 100% image was denoised instead of reconstructed. Poisson noise was applied to the simulated image with λ values of 500, 50, and 5 to simulate lower imaging dose. The original simulated STEM image was also included in our calculations and was generated using a dose of 1000. The simulated STEM image is 100 by 100 pixels and has essentially no high frequency components. The image reconstruction tends to smooth the data, also resulting in no high frequency components. This causes the FRC of the two images to be large at higher resolutions and may be misleading. For this reason, the BPFA has no overlap to avoid excessive smoothing. Moreover, the resolution of the simulated image is approximately 9.2 (1/nm), so we only look that far in the frequency domain when performing FRC. If the FRC curve does not crossover the threshold, a resolution value of 9.2 is used. We emphasize that our reported results are conservative. The FRC and PSNR values using the ground truth and the reconstructed images are shown in Tables 1 and 2. The left side show the metrics without using BPFA (missing pixels) and the right side show the metrics after using BPFA. When we did not use BPFA, the Fourier transform was estimated [4]. Some threshold curves have been studied [5], but they are derived for additive noise models. Since we have a Poisson noise model, we have used the more conservative threshold of 0.5 for our calculations. Ten images were used to construct each cell of tables in the form of the mean of the metric plus or minus its standard deviation. As expected, the PSNR dies off much quicker than the FRC values for the same image. For the 100% and 80% sampled versions of the truth image, the resolution only dies off when the dose is 5. However, the PSNR dies off rapidly as the dose is reduced. For the 1000, 500, and 50 dose images, the FRC is the maximum, or close, until we undersample at 20%. The PSNR for these values tapers down as we get into the bottom right hand corner of the table, even though the resolution remains high. Overall, we find that undersampled images can be reconstructed to acceptable resolution even when the dose per pixel is also reduced[6]. References: [1]A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2]A Stevens, L Kovarik, P Abellan et al. Advanced Structural and Chemical Imaging 1(1), (2015), pp. 1. [3]M Zhou, H Chen, J Paisley et al. Image Processing, IEEE Transactions on 21(1), (2012), pp. 130. [4]V. Y. Liepin’sh. Automatic control and computer sciences 30(3), (1996), pp. 20.

Luzi, Lorenzo↗
DOE OSTI2022

Effects of Molecular Size on Resolution in Charge Detection Mass Spectrometry

Instrumental resolution of Fourier transform-charge detection mass spectrometry instruments with electrostatic ion trap detection of individual ions depends on the precision with which ion energy is determined. Energy can be selected using ion optic filters or from harmonic amplitude ratios (HARs) that provide Fellgett’s advantage and eliminate the necessity of ion transmission loss to improve resolution. Unlike the ion energy-filtering method, the resolution of the HAR method increases with charge (improved S/N) and thus with mass. An analysis of the HAR method with current instrumentation indicates that higher resolution can be obtained with the HAR method than the best resolution demonstrated for instruments with energy-selective optics for ions in the low MDa range and above. However, this gain is typically unrealized because the resolution obtainable with molecular systems in this mass range is limited by sample heterogeneity. Here, this phenomenon is illustrated with both tobacco mosaic virus (0.6–2.7 MDa) and AAV9 (3.7–4.7 MDa) samples where mass spectral resolution is limited by the sample, including salt adducts, and not by instrument resolution. Nevertheless, the ratio of full to empty AAV9 capsids and the included genome mass can be accurately obtained in a few minutes from 1× PBS buffer solution and an elution buffer containing 300+ mM nonvolatile content despite extensive adduction and lower resolution. Empty and full capsids adduct similarly indicating that salts encrust the complexes during late stages of droplet evaporation and that mass shifts can be calibrated in order to obtain accurate analyte masses even from highly salty solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Model Resolution Sensitivity of the Simulation of North Atlantic Oscillation Teleconnections to Precipitation Extremes

We evaluate a high-resolution (0.25°), four-member ensemble simulation of the global climate (1979–2005) with the U.S. Department of Energy's Energy Exascale Earth System Model v0.3—forced with observed ocean surface temperatures and sea ice extent—for its ability to represent the North Atlantic Oscillation (NAO) teleconnections to winter precipitation extremes over western Europe. As compared to the low-resolution model (1°), it simulates a stronger impact of NAO on daily precipitation extremes over the western slopes of mountain ranges over southwestern Norway, northwestern United Kingdom, and the Western Balkan states. Precipitation extremes and their linear relationship with NAO are quantified using the generalized extreme value distribution. NAO-dependent large-scale (stratiform) precipitation intensity strengthens in the high-resolution model on seasonal time scales and plays a dominant role during simulated daily precipitation extremes. Improvements in the high-resolution model over these varied-topography regions largely appear to be due to finer resolved scales of motion that amplify NAO-dependent seasonal vertical moisture fluxes and enhance stable condensation. However, the high-resolution model simulates a weaker than observed impact of NAO on extratropical cyclone activity and total precipitable water, generally underperforming the low-resolution model These effects possibly offset the impact of enhanced vertical moisture fluxes on NAO-dependent precipitation extremes in the high-resolution model in these regions. Over the southwestern Iberian peninsula, the high-resolution model underperforms the low-resolution model simulating weaker than observed impact of NAO on precipitation extremes. Finally, this appears to be due to the reduction in total precipitable water despite an increase in NAO-dependent extratropical activity there.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The Resolution Sensitivity of Northern Hemisphere Blocking in Four 25-km Atmospheric Global Circulation Models

The aim of this study is to investigate if the representation of Northern Hemisphere blocking is sensitive to resolution in current-generation atmospheric global circulation models (AGCMs). An evaluation is thus conducted of how well atmospheric blocking is represented in four AGCMs whose horizontal resolution is increased from a grid spacing of more than 100 km to about 25 km. It is shown that Euro-Atlantic blocking is simulated overall more credibly at higher resolution (i.e., in better agreement with a 50-yr reference blocking climatology created from the reanalyses ERA-40 and ERA-Interim). The improvement seen with resolution depends on the season and to some extent on the model considered. Euro-Atlantic blocking is simulated more realistically at higher resolution in winter, spring, and autumn, and robustly so across the model ensemble. The improvement in spring is larger than that in winter and autumn. Summer blocking is found to be better simulated at higher resolution by one model only, with little change seen in the other three models. The representation of Pacific blocking is not found to systematically depend on resolution. Despite the improvements seen with resolution, the 25-km models still exhibit large biases in Euro-Atlantic blocking. For example, three of the four 25-km models underestimate winter northern European blocking frequency by about one-third. The resolution sensitivity and biases in the simulated blocking are shown to be in part associated with the mean-state biases in the models' midlatitude circulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble

A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simulation of ENSO Teleconnections to Precipitation Extremes over the United States in the High-Resolution Version of E3SM

Abstract We evaluate the simulated teleconnection of El Niño–Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Niño–associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Niña–induced storm track activity. Significance Statement New high-resolution Earth system models (ESMs) solve mathematical equations of fluid flow at much smaller spatial scales than prevalent ESMs, and thus are prohibitively expensive to compute. However, they can be useful for simulating accurate details of regional climate extremes that are driven by naturally occurring climate oscillations like El Niño–Southern Oscillation (ENSO). Here, we evaluate the simulation of ENSO-driven precipitation extremes over the United States in the high-resolution version of the U.S. Department of Energy’s new Energy Exascale Earth System Model version 1. We find that the high-resolution model improves upon its low-resolution counterpart over the southeastern United States by producing a better transport of moisture into the region from the Gulf of Mexico during El Niño. Over the U.S. Pacific Northwest, the high-resolution model simulates the atmospheric flow in more detail over the complex mountainous terrain. However, it also brings in more moisture from the Pacific Ocean just like the low-resolution model. This causes it to produce precipitation extremes during La Niña years there that are stronger than that observed in the real world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗
NASA NTRS1985

Retinally stabilized differential resolution television display

A remote television viewing system employing an eye tracker is disclosed, wherein a small region of the image appears in high resolution, and the remainder of the image appears in low resolution. The eye tracker monitors the position of the viewer's line of sight. The eye tracker position data is transmitted to the remote television camera and control. Both the remote camera and television display are adapted to have selectable high-resolution and low resolution raster scan modes. The position data from the eye tracker is used to determine the point at which the high-resolution scan is to commence. The video data defining the observed image is encoded in a novel format, wherein in each data field, the data representing the position of the high resolution region of predetermined size appears first, followed by the high resolution zone video data and then the low-resolution region data. As the viewer's line of sight relative to the displayed image changes, the position of the high resolution region changes to track the viewer's line of sight.

Ruoff, C. F., Jr.↗
NASA NTRS1993

Performance of high resolution decoding with Multi-Anode Microchannel Array detectors

The Multi-Anode Microchannel Array (MAMA) is a microchannel plate based photon counting detector with applications in ground-based and space-based astronomy. The detector electronics decode the position of each photon event, and the decoding algorithm that associates a given event with the appropriate pixel is determined by the geometry of the anode array. The standard MAMA detector has a spatial resolution set by the anode array of 25 microns, but the MCP pore resolution exceeds this. The performance of a new algorithm that halves the pixel spacing and improves the pixel spatial resolution is described. The new algorithm does not degrade the pulse-pair resolution of the detector and does not require any modifications to the detector tube. Measurements of the detector's response demonstrate that high resolution decoding yields a 60 percent enhancement in spatial resolution. Measurements of the performance of the high resolution algorithm with a 14 micron MAMA detector are also described. The parameters that control high resolution performance are discussed. Results of the application of high resolution decoding to speckle interferometry are presented.

Kasle, David B.↗
NASA NTRS2006

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Source record↗
NASA NTRS2023

How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?

Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: (1) a fused MODIS–Landsat product with daily 30 m spatial resolution and (2) a harmonized Landsat 8 and Sentinel 2A and B (HLS) product with 3–4 d temporal and 30 m spatial resolution. State-of-the-art spectral unmixing techniques are applied to surface reflectance products from 1 and 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size) but greater mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts, e.g., basin boundary delineation. Other explanations are (1) the importance of daily acquisitions and (2) the limitations of downscaled forcings for reconstruction. Conclusions are as follows: (1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models and (2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations, but existing moderate-resolution sensors still offer value.

Edward H. Bair↗
DOE OSTI2024

Optimal Spectral Resolution for Infrared Studies of Solids and Liquids

Due to a legacy originating in the limited capability of early computers, the spectroscopic resolution used in Fourier transform infrared spectroscopy and other systems has largely been implemented using only powers of two for more than 50 years. In this study, we investigate debunking the spectroscopic lore of, e.g., using only 2, 4, 8, or 16 cm −1 resolution and determine the optimal resolution in terms of both (i) a desired signal-to-noise ratio and (ii) efficient use of acquisition time. The study is facilitated by the availability of solids and liquids reference spectral data recorded at 2.0 cm −1 resolution and is based on an examination in the 4000–400 cm −1 range of 61 liquids and 70 solids spectra, with a total analysis of 4237 peaks, each of which was also examined for being singlet/multiplet in nature. Of the 1765 liquid bands examined, only 27 had widths <5 cm −1 . Of the 2472 solid bands examined, only 39 peaks have widths <5 cm −1 . For both the liquid and solid bands, a skewed distribution of peak widths was observed: For liquids, the mean peak width was 24.7 cm −1 but the median peak width was 13.7 cm −1 , and, similarly, for solids, the mean peak width was 22.2 cm −1 but the median peak width was 11.2 cm −1 . While recognizing other studies may differ in scope and limiting the analysis to only room temperature data, we have found that a resolution to resolve 95% of all bands is 5.7 cm −1 for liquids and 5.3 cm −1 for solids; such a resolution would capture the native linewidth (not accounting for instrumental broadening) for 95% of all the solids and liquid bands, respectively. After decades of measuring liquids and solids at 4, 8, or 16 cm −1 resolution, we suggest that, when accounting only for intrinsic linewidths, an optimized resolution of 6.0 cm −1 will capture 91% of all condensed-phase bands, i.e., broadening of only 9% of the narrowest of bands, but yielding a large gain in signal-to-noise with minimal loss of specificity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2015

Nanoscale analysis of unstained biological specimens in water without radiation damage using high-resolution frequency transmission electric-field system based on FE-SEM

Scanning electron microscopy (SEM) has been widely used to examine biological specimens of bacteria, viruses and proteins. Until now, atmospheric and/or wet biological specimens have been examined using various atmospheric holders or special equipment involving SEM. Unfortunately, they undergo heavy radiation damage by the direct electron beam. In addition, images of unstained biological samples in water yield poor contrast. We recently developed a new analytical technology involving a frequency transmission electric-field (FTE) method based on thermionic SEM. This method is suitable for high-contrast imaging of unstained biological specimens. Our aim was to optimise the method. Here we describe a high-resolution FTE system based on field-emission SEM; it allows for imaging and nanoscale examination of various biological specimens in water without radiation damage. The spatial resolution is 8 nm, which is higher than 41 nm of the existing FTE system. Our new method can be easily utilised for examination of unstained biological specimens including bacteria, viruses and protein complexes. Furthermore, our high-resolution FTE system can be used for diverse liquid samples across a broad range of scientific fields, e.g. nanoparticles, nanotubes and organic and catalytic materials. - Highlights: • We developed a high-resolution frequency transmission electric-field (FTE) system. • High-resolution FTE system is introduced in the field-emission SEM. • The spatial resolution of high-resolution FTE method is 8 nm. • High-resolution FTE system enables observation of the intact IgM particles in water.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Experimental Study of an Easily Controlled Ultra-High-Resolution Pixel-Matched Parallel-Hole Collimator with a Small Cadmium Zinc Telluride Pixelated Gamma Camera System

The aim of this study was to develop an easily controlled, ultra-high-resolution, tungsten parallel-hole collimator based on a small pixelated gamma camera system. A small cadmium zinc telluride (CZT) pixelated semiconductor detector (eValuator-2500 detector [eV product, Saxonburg, PA]) was evaluated. This detector is composed of an array of 51.2 × 0.8 × 3-mm{sup 3} individual CZT crystal elements. The ultra-high-resolution, pixel-matched, parallel-hole collimators consisted of six layers, with the same between the hole and pixel size. The basic characteristics of the imaging system, such as sensitivity and spatial resolution, was measured using a {sup 57}Co point source. The measured averages of sensitivity and spatial resolution varied depending on the septal heights of the ultra-high-resolution parallel-hole collimator and source-to-collimator distances. When the 30-mm septal height was at 1-cm source-to-collimator distance, the spatial resolution was approximately 0.85 mm. Using 5-mm septal height, over 0.3 cps/kBq sensitivity was achieved. One advantage of our system is the use of stacked collimators that can select the best combination of system sensitivity and spatial resolution. Our results demonstrated that the developed CZT–pixelated gamma camera system using an ultra-high-resolution parallel-hole collimator of various collimator geometric designs has potential as an effective instrument.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Abstract The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the $${\hbox {HfO}}_{2}$$ HfO 2 based memristive device. $$1\textrm{st}$$ 1 st and $$2\textrm{nd}$$ 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to $$\sim$$ ∼ 4.3 $$\times$$ × and $$\sim$$ ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

97 MATHEMATICS AND COMPUTING↗