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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
DOE OSTI2023

Direct observation of C 3 S particle dissolution using fast nano X-ray computed tomography

Tricalcium silicate (C 3 S) occupies 50 % to 70 % of ordinary portland cement (OPC) by mass and it is an important component affecting the hydration of OPC [1], [2], [3], [4], [5], [6], [7]. Generally, the hydration of C 3 S is described by two processes: the dissolution of C 3 S particles and the precipitation of hydration products. While it is understood that the dissolution rates of C 3 S vary with time, more precise measurements are needed to understand this process. Many mechanisms have been proposed to explain the time-evolving dissolution rates of C 3 S [2]. The metastable barrier hypothesis suggests that a thin metastable layer of hydrates forms around the C 3 S particle surface and prohibits the access of grains to the aqueous solution [8], [9], [10], [11], [12]. The slow dissolution step hypothesis suggests that the increased ion concentration from the initial reaction delays the C 3 S dissolution [2], [13], [14], [15], [16], [17]. More recent publications suggest that C 3 S may react differently depending on the existence of crystallographic defects [18], [19], [20]. Etch pits are thought to open on the particle surface during the initial reaction and this contributes to the C 3 S dissolution [21], [22]. As hydrates precipitate and cover these highly reactive surfaces, hydration slows down and the induction period starts [18], [23], [24], [25], [26]. Many experiments have been conducted to test the aforementioned mechanisms. Some hydration studies utilize bulk measurements, such as isothermal calorimetry [27], [28], [29], pore solution analysis [30], quasi-elastic neutron scattering [31], and nuclear magnetic resonance spectroscopy [32], [33]. One limitation of these measurements is that they do not provide direct and detailed information on the individual C 3 S particles. Some other studies utilize imaging techniques, such as scanning electron microscopy (SEM) [34], [35], [36] and transmission electron microscopy (TEM) [37]. However, SEM/TEM cannot track the evolution of individual particles throughout hydration [34], [35], [38], [39], [40] and they do not give insights into the microstructure of materials before hydration [34], [35], [39]. This makes it challenging to draw strong conclusions from only SEM or TEM observations. Synchrotron X-ray tomography techniques have been used more broadly in recent years to study cement hydration. They are not only non-destructive but also able to image a sample in full 3D with resolutions that can reach from micron to nanoscale. Nano computed tomography (nCT) is one technique that has been applied to study cement hydration at the nanoscale [26], [41]. A typical nCT can reach a pixel size from 15 to 65 nm, providing enough detail for observing features <1 μm. However, nCT often takes >0.5 h to finish one scan. This makes the application of this technique on continuous scans for in-situ observations challenging. Fast X-ray computerized tomography (fCT) is another technique that has shown success in studying the time-evolving cement microstructures [20], [42], [43], [44], [45], [46], [47], [48], [49]. Due to the high flux of the X-ray beam from the synchrotron ring, fCT allows a scan to be captured within 1 min at a pixel size of 1 μm. This allows a paste sample to be continuously scanned during the hydration process. However, the micron-sized resolutions limit does not provide detailed insights for particles <5 μm [20], [49]. Fortunately, the combination of nCT and fCT has allowed the development of fast nano X-ray computed tomography (fnCT). fnCT can capture a 3D data set in <2 min at a pixel size of 50 nm. This makes this procedure an exciting method to evaluate hydrating pastes. fnCT collects multiple X-ray radiographs at various rotation angles and generates a 3D model of the scanned sample, which is also referred to as a 3D tomography [50], [51]. In one tomography, the X-ray absorptions of different components (e.g., C 3 S and hydrates) differ as functions of density and chemistry [52], [53]. These X-ray absorption contrasts can be used to extract detailed information about the 3D microstructure [26], [54], [55]. In this paper, fnCT is used to collect time-lapse tomographs of hydrating C 3 S paste from 18 min after mixing to 7 h of hydration. The bulk measurements of anhydrous C 3 S, as well as the microstructural changes of individual C 3 S particles, are directly observed, quantified, and discussed. The dissolution behavior of C 3 S particles at various size scales is systematically analyzed and compared. This work aims to find the relationship between the size of C 3 S particle sizes and their dissolution rates. This provides significant insights into the early-age hydration of C 3 S on length and time scales not previously possible. Because of the magnitude of the data and the substantial amount of observations, this work will solely focus on the change in the anhydrous particles. Changes in the hydration products will be reported in future work.

42 ENGINEERING↗
DOE OSTI2024

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network

Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Anisotropic neutron response of trans-stilbene and impact on a handheld dual particle imager

Here this manuscript details measurements of the anisotropic light-output response of trans-stilbene, henceforth referred to as stilbene, along the three primary crystal planes and analyzes how this anisotropic response impacts neutron image reconstruction in a handheld dual particle imager (H2DPI). A prototype H2DPI composed of stilbene pillars (6 x 6 x 50 mm 3 ) coupled to silicon photomultipliers was built and is capable of imaging both fast neutrons and gamma rays from kilogram quantities of special nuclear material. Stilbene was chosen as the scintillating medium for the imager because of its pulse shape discrimination capability, time resolution and relatively high light output. A drawback to using stilbene, however, is the anisotropic response. A recoiling proton from a neutron elastic-scattering event in stilbene will yield different amounts of scintillation light depending on the direction of the recoiling proton with respect to the crystal lattice. This manuscript analyzes how this anisotropic response impacts neutron image reconstruction in 4π. The light output in the three primary crystal planes of stilbene were independently measured 6–7 times for proton recoil energies ranging from 0.5–5.0 MeV using a quasi-monoenergetic time-of-flight neutron source. The measured light-output data in the three crystal planes were fit with a semi-empirical function based on Birks’ formula. These fits were used to reconstruct the location of a 252 Cf source in front of the imager (0°, -0.8°), to the left of the imager (-90°, -0.8°) and directly above the imager (0°, 90°) to determine if it is necessary to apply the directionally-dependent response of stilbene to accurately reconstruct source locations. A bootstrapping technique was applied to the measured data sets to produce 1000 images composed of 2000 cone projections for each source location and each measured light-output curve. List-mode maximum likelihood expectation maximization was applied to each image; the highest pixel location and the full width at half maximum (FWHM) were then recorded. The average and standard deviations of these parameters were taken for each set of images. The maximum difference in the average azimuthal pixel location when varying between the appropriate and incorrect light-output curves for the source locations at (0°, -0.8°) and (-90°, -0.8°) were respectively found to be 1.85 ± 1.39° and 2.03 ± 1.78°. All other pixel locations and FWHMs were within a single standard deviation of uncertainty. Taken together, the anisotropic response of stilbene has negligible impact on the neutron image reconstruction capability of the H2DPI.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

What lies beneath: Vertical temperature heterogeneity in a Mediterranean woodland savanna

As the availability of satellite and airborne thermal infrared remote sensing (TIR-RS) data increases and their spatial, temporal, and spectral resolutions improve, researchers are finding diverse applications for TIR-RS measurements. TIR-RS is now commonly applied in regional- and continental-scale analyses, such as those focused on fire and surface energy balance. However, its application lags in plant physiology and ecology, for which a finer-scale understanding of plant canopy temperatures would be useful to elucidate plant water dynamics, for example. In particular, while methods to disaggregate TIR-RS pixels in horizontal space have advanced, possible vertical stratification of plant canopy temperature and its implications for understanding the correspondence between TIR-RS and finer-scale, field-based thermal measurements (e.g. made with a thermal camera) remain unexplored. Here, we use data from a thermal camera deployed concurrently with the recent ECOSTRESS mission to quantify vertical temperature gradients within tree canopies and temperatures of over- vs. under-story plants in a Mediterranean woodland savanna. We then leverage diverse ancillary data to maximize the geometric comparability of ECOSTRESS and thermal camera measurements, in order to assess the extent to which the two forms of thermal measurements correspond. Specifically, we ask: (1) What are the patterns of intra-canopy and over- vs. under-story vertical temperature in a Mediterranean woodland savanna?, and (2) How can vertically-resolved, but spatially-limited field-based temperature measurements be reconciled with spatially-extensive, but surface-only, temperature measurements of a space-borne remote sensor? Further, we found consistent patterns of vertical thermal heterogeneity both within tree canopies and between ecosystem over- and under-stories. The daytime difference between the top and bottom thirds of blue oak canopies was, on average, 0.48 ° C – and sometimes several times larger. Notably, canopy tops are cooler, likely associated with the under-story grass reaching daytime temperatures often exceeding over-story temperatures by 10° C. Given the consistency of the intra-canopy temperature gradients, we expected the ECOSTRESS sensor would be in better agreement with camera measurements of canopy tops than bulk canopies or canopy bottoms. However, within-canopy gradients were overwhelmed by other sources of disagreement between the measurements, in part associated with upscaling camera measurements across space. Overall, thermal camera and ECOSTRESS measurements were largely in agreement at night (pixel RMSE = 1.1°C), but they were more divergent during times of low (but >0 W/m2) and high incoming solar radiation (daytime pixel RMSE = 3.5°C).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year -1 ). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r 2 =0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r 2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface albedo spatial variability in North America: Gridded data vs. local measurements

Considering the current booming interest for the large-scale deployment of bifacial photovoltaic modules, the solar industry now requires accurate estimates of broadband surface albedo at high spatial resolution. In this context, the present study evaluates the adequacy and performance over North America of two Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo products (at 500-m and 1-km resolution) and the National Solar Radiation Database (NSRDB) product at 4-km resolution. Two variations of the 500-m MODIS product are also considered: black-sky albedo and all-sky albedo. Albedo observations from 36 radiometric stations during 2011–2015 are analyzed while considering the apparent homogeneity of the surface characteristics over the 4x4 km NSRDB pixel in which they are located. Even at sites where the albedo around the station has been found “homogeneous” in the literature, marked differences are found between the daily observations and the gridded estimates at any spatial resolution. Furthermore, differences in seasonal behavior and between the three different albedo types also impact the accuracy of the albedo estimates, with overtones caused by local specificities and inhomogeneities. All this precludes the desirable evaluation of the local albedo at a specific site of relatively small size compared to its corresponding 4x4-km pixel if only the mean albedo over that pixel is known. Significant discrepancies are also found at snow-impacted sites, most importantly in the case of the NSRDB albedo estimates, which are typically much too high.

14 SOLAR ENERGY↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
DOE OSTI2021

Holes in Optical Lightning Flashes: Identifying Poorly Transmissive Clouds in Lightning Imager Data

Space-based optical lightning sensors including the lightning imaging sensor (LIS) and geostationary lightning mapper (GLM) are pixelated imagers that detect lightning as transient increases in cloud top illumination. Detection requires optical emissions to escape the cloud top to space with sufficient energy to trigger a pixel on the imaging array. Through scattering and absorption, certain clouds are able to block most light from reaching the instrument, causing a reduction in detection efficiency (DE) and possibly location accuracy (LA). Radiant lightning emissions that illuminate large cloud top areas are used to examine scenarios where clouds block light from reaching orbit. In some cases, these anomalies in the spatial radiance distribution from the lightning pulse lead to “holes” in the optical lightning flash where certain pixels fail to trigger. Such holes are identified algorithmically in the Tropical Rainfall Measuring Mission satellite LIS record and the microphysical properties of the coincident storm region are queried. We find that holes primarily occur in tall (IR T b < 235 K) convection (87%) and overhanging anvil clouds (10%). The remaining 3% of holes occur in moderate-to-weak convection or in clear air breaks between stormclouds. We further demonstrate how an algorithm that assesses the spatial radiance patterns from energetic lightning pulses might be used to construct an optical transmission gridded stoplight product for GLM that could help operators identify clouds with a potentially reduced DE and LA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

FORTE Measurements of Global Optical Lightning Waveforms and Implications for Optical Lightning Detection

Lightning processes generate a diverse collection of optical pulses from current traversing the lightning channels. These signals are then broadened spatially and temporally via scattering in the clouds. The resulting waveforms measured from space with instruments like the photodiode detector (PDD) on the Fast On-orbit Recording of Transient Events (FORTE) satellite have a variety of shapes. In this study, we use coincident optical and Radio-Frequency measurements to document the properties of optical PDD waveforms associated with different types of lightning, estimate delays from scattering in the clouds, and comment on how pulse shape impacts optical lightning detection. We find that the attributes of optical pulses recorded by the PDD are consistent with prior studies, but vary globally and with event amplitude. The most powerful lightning tends to be single-peaked with faster rise times (median: ~100 µs) and shorter effective widths (median: ~400 µs) than normal lightning. Particularly dim events, meanwhile, include cases of broad optical waveforms with sustained optical emission throughout the PDD record, which the pixelated FORTE Lightning Location System (LLS) instrument has difficulty detecting. We propose that this is due to the optical signal being divided between individual LLS pixels that are each, individually, not bright enough to trigger. We use PDD waveforms and Monte Carlo radiative transfer modeling to demonstrate that increasing the temporal and spatial resolution of a pixelated lightning imager will make it more difficult to detect these broad/dim pulses as their energy becomes divided between additional pixels/integration frames.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combined Optical and Radio–Frequency Perspectives on the Time Evolution of Lightning Measured by the FORTE Satellite

We use a cluster feature data set for the Fast On-orbit Recording of Transient Events (FORTE) satellite that combines detections from its pixelated Lightning Locating System (LLS), photodiode detector (PDD) and Radio-Frequency (RF) instrumentation to generate statistics describing the frequency and timing of lightning events detected by each instrument during lightning flashes. Coincident observations from the same vantage point allow us to directly compare flash details that can be resolved by the wide Field of View (FOV) instruments relative to the pixelated LLS—whose design is based on NASA's Lightning Imaging Sensor. We find that both the PDD and RF system typically generate more detections than the lightning imager (mean: 1.5 PDD events per LLS group, 2 RF events per LLS group) from pulses that are either not sufficiently bright in the optical band (in the case of RF) or that lack the optical energy density (in the case of the PDD) required to trigger one of the pixels on the LLS imaging array. This includes additional activity before the first LLS group or after the final LLS group. These FORTE results demonstrate that certain lightning processes would be better resolved by wide-FOV optical and RF instruments than lightning imagers. Current/future space-based missions that use/plan to use similar instruments will improve our understanding of flash evolution by resolving details missed by lightning imagers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Vertical full-colour micro-LEDs via 2D materials-based layer transfer

Micro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance. However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density. Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down, but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. Furthermore, these results establish routes to creating full-colour µLED displays for augmented and virtual reality, while also offering a generalizable platform for broader classes of three-dimensional integrated devices.

42 ENGINEERING↗
DOE OSTI2025

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Calibration of a versatile multi-energy soft x-ray diagnostic for WEST long pulse plasmas

A compact multi-energy soft x-ray diagnostic is being installed on the W Environment in Steady-state Tokamak (WEST), which was designed and built to test ITER-like tungsten plasma facing components in a long pulse (~1000 s) scenario. The diagnostic consists of a pinhole camera fielded with the PILATUS3 photon-counting Si-based detector (≲100 kpixel). The detector has sensitivity in the range 1.6–30 keV and enables energy discrimination, providing a higher energy resolution than conventional systems with metal foils and diodes with adequate space and time resolution (≲1 cm and 2 ms). The lower-absorption cut-off energy is set independently on each one of the ~100 kpixels, providing a unique opportunity to measure simultaneously the plasma emissivity in multiple energy ranges and deduce a variety of plasma parameters (e.g., T e , n Z , and ΔZ eff ). The energy dependence of each pixel is calibrated here over the range 3–22 keV. The detector is exposed to a variety of monochromatic sources—fluorescence emission from metallic targets—and for each pixel, the lower energy threshold is scanned to calibrate the energy dependence. The data are fit to a responsivity curve (“S-curve”) that determines the mapping between the possible detector settings and the energy response for each pixel. Here, the calibration is performed for three energy ranges: low (2.3–6 keV), medium (4.5–13.5 keV), and high (5.4–21 keV). We determine the achievable energy resolutions for the low, medium, and high energy ranges as 330 eV, 640 eV, and 950 eV, respectively. The main limitation for the energy resolution is found to be the finite width of the S-curve.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Multi-energy reconstructions, central electron temperature measurements, and early detection of the birth and growth of runaway electrons using a versatile soft x-ray pinhole camera at MST

A multi-energy soft x-ray pinhole camera has been designed, built, and deployed at the Madison Symmetric Torus to aid the study of particle and thermal transport, as well as MHD stability physics. This novel imaging diagnostic technique employs a pixelated x-ray detector in which the lower energy threshold for photon detection can be adjusted independently on each pixel. The detector of choice is a PILATUS3 100 K with a 450 μm thick silicon sensor and nearly 100 000 pixels sensitive to photon energies between 1.6 and 30 keV. An ensemble of cubic spline smoothing functions has been applied to the line-integrated data for each time-frame and energy-range, obtaining a reduced standard-deviation when compared to that dominated by photon-noise. The multi-energy local emissivity profiles are obtained from a 1D matrix-based Abel-inversion procedure. Central values of Te can be obtained by modeling the slope of the continuum radiation from ratios of the inverted radial emissivity profiles over multiple energy ranges with no a priori assumptions of plasma profiles, magnetic field reconstruction constraints, high-density limitations, or need of shot-to-shot reproducibility. In tokamak plasmas, a novel application has recently been tested for early detection, 1D imaging, and study of the birth, exponential growth, and saturation of runaway electrons at energies comparable to 100 × Te,0; thus, early results are also presented.

Delgado-Aparicio, L. F. (ORCID:0000000167394380)↗
DOE OSTI2022

Spatial calibration and synthetic diagnostic of a multi-energy hard x-ray camera at WEST tokamak

WEST (tungsten environment in steady-state tokamak) is starting operation for the first time with a water-cooled full tungsten divertor, enabling long pulse operation. Heating is provided by radiofrequency systems, including lower hybrid current drive (LHCD). In this context, a compact multi-energy hard x-ray camera has been installed for energy and space-resolved measurements of the electron temperature, the fast electron tail density produced by LHCD and runaway electrons, and the beam–target emission of tungsten at the target due to fast electron losses interacting with the divertor plates. The diagnostic is a pinhole camera based on a 2D pixel array detector (Pilatus 3 CdTe CMOS Hybrid-Pixel detector produced by DECTRIS). The novelty of this diagnostic technique is the detector’s capability of adjusting the threshold energy at pixel level. This innovation provides great flexibility in the energy configuration, allowing simultaneous space and energy-resolved x-ray measurements. This contribution details two important steps in the preparation of the diagnostic operation. First, the in-vessel spatial calibration that was carried out with a radioactive source. Second, the synthetic diagnostic is obtained by the suite of codes ALOHA/C3PO/LUKE/R5-X2, which simulates LH wave propagation and absorption, as well as the fast electron bremsstrahlung production.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗
DOE OSTI2021

Characterization of the CMS Endcap Timing Layer readout chip prototype with charge injection

We present the characterization of a readout Application-Specific Integrated Circuit (ASIC) for the CMS Endcap Timing Layer (ETL) of the High-Luminosity LHC upgrade with charge injection. The ASIC, named ETROC and developed in a 65 nm CMOS technology, reads out a 16× 16 pixel matrix of the Low-Gain Avalanche Detector (LGAD). The jitter contribution from ETROC is required to be below 40 ps to achieve the 50 ps overall time resolution per hit. The analog readout circuits in ETROC consist of the preamplifier and the discriminator. The preamplifier handles the LGAD charge signal with the most probable value of around 15 fC. The discriminator generates the digital pulse, which provides the Time-Of-Arrival (TOA, leading edge) and Time-Over-Threshold (TOT, pulse width) information. The prototype of ETROC (ETROC0) that implements a single channel of analog readout circuits has been evaluated with charge injection. The jitter of the analog readout circuits, measured from the discriminator's leading edge, is better than 16 ps for a charge larger than 15 fC with the sensor capacitance. The time walk resulting from different pulse heights can be corrected using the TOT measurement. The time resolution distribution has a standard deviation of 29 ps after the time-walk correction from the charge injection. At room temperature, the preamplifier's power consumption is measured to be 0.74 mW and 1.53 mW per pixel in the low- and high-power mode, respectively. Finally, the measured power consumption of the discriminator is 0.84 mW per pixel. With the ASIC alone or the LGAD sensor, The characterization performances fulfill the ETL's challenging requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗