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At least 1,675 records · Page 93

Observing the setting and hardening of cementitious materials by X-ray dark-field radiography

Novel X-ray imaging methods expand conventional attenuation-based X-ray radiography by the phase- and the dark-field contrasts. While weakly absorbing structures in the specimen can be better visualized in phase contrast, the dark-field contrast provides information about morphological sub-pixel microstructures. Here we report an application of dark-field X-ray radiography for imaging the time-resolved setting process in fresh cement. Our results demonstrate that the microstructural changes within the cement result in a decreasing dark-field signal. We quantify this imaging signal with a time-dependent dark-field scatter coefficient and show its good correlation with the compressional wave velocity. We further present images based on a pixel-wise analysis of the scattering signal and a corresponding logistic fit. These images emphasize the benefit of dark-field imaging of cementitious materials as it provides two dimensional spatial information on the processes within the sample while other established testing techniques only provide information on the bulk average.

36 MATERIALS SCIENCE↗

Optical image analysis of WSe 2 − thresholding for layer detection

The fast and reliable layer identification of two-dimensional transition metal dichalcogenide (TMD), such as WSe 2 , is essential to investigating their thickness-dependent electronic and optical properties. This article presents efficient optical image thresholding methodology designed to segment the mono, bi, and tri-layer regions of WSe 2 flakes mechanically exfoliated onto a SiO 2 /Si substrate. The optical images were first preprocessed to exclude the background effect and analyzed using the pixel medians and interquartile ranges for fundamental color channels—red, green, and blue (RGB). The analysis of red channel pixel intensities yielded three distinct ranges, serving as thresholds for layer segmentation: monolayer (111.0–118.0), bilayer (103.0–110.0), and tri-layer (93.0–103.0). Similarly, thresholds were established for each color channel, facilitating a comparative study of the segmentation performances. Further, the intersection-over-union ($IoU$) calculations revealed that the red and green channels demonstrated greater than 99 % and 90 % accuracy in differentiating each layer, respectively. This approach yields remarkable results without substantial data calibration that utilizes time-intensive heuristic techniques. Moreover, the proposed methodology offers the flexibility to compare performances across different color channels, expanding the applicability for other 2D material systems.

2D Materials↗

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗

Design and engineering challenges of a multi-energy hard x-ray camera for long-pulse profile measurements at WEST tokamak

The WEST tokamak has recently been prepared for long pulse operation with a water-cooled full first wall. Heating is provided by radiofrequency systems, including Lower Hybrid Current Drive (LHCD). The Princeton Plasma Physics Laboratory has developed a multi-energy hard x-ray camera for profile measurements at WEST. The camera is based on a new generation of 2D pixel array detectors that allow the energy threshold to be independently set for each pixel. The diagnostic will provide spatial, temporal and energy resolved measurements of the hard x-ray emission from the full plasma cross-section, investigating several physical quantities such as the electron temperature from continuum emission, the fast electron tail density produced by radiofrequency current drive and runaway electrons, as well as characteristic tungsten x-rays due to beam-target emission at the edge. Overall, this work describes the engineering challenges that the WEST long pulse scenario poses for this diagnostic and how the design addresses and solves them. Vacuum, thermal stress and heat transfer calculations are presented and discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Digital bead modeling for wire-arc directed energy deposition

Prediction of 2D cross-section and full 3D geometry for stacked weld beads is critical for the outcome of wire-arc directed energy deposition (DED) parts; however, most additive path planning software packages model beads as extrusions of a rectangle. Weld beads are not rectangular, and the resulting shape is dependent upon physics effects at the moment of deposition. Physics phenomena such as the geometry of the underlying surface, the heat input of the welding mode, and the direction of gravity contribute to bead shape. Here, this paper presents a novel implicit modeling method that discretizes a 2D area or 3D volume of space into pixels or voxels and constructs fields based on these physics phenomena. The fields are combined using a weighting scheme trained on 3D scan measurements of welds and wire-arc DED prints. Pixels or voxels are added until the known amount of deposited volume has been achieved. Thereby, a strong conservation of mass principle is applied to the process. Utilizing machine learning techniques, the present model can be trained on a database of scans allowing for the representation of a wide variety of prints. Results show that this method can produce predictions with realistic bead morphology and sub-millimeter form error.

Bead geometry modeling↗

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗

An alternative to the crystallographic reconstruction of austenite in steels

An alternative crystallographic austenite reconstruction programme written in Matlab is developed by combining the best features of the existing models: the orientation relationship refinement, the local pixel-by-pixel analysis and the nuclei identification and spreading strategy. This programme can be directly applied to experimental electron backscatter diffraction mappings. Its applicability is demonstrated on both quenching and partitioning and as-quenched lath-martensite steels. - Highlights: • An alternative crystallographic austenite reconstruction program is developed. • The method combines a local analysis and a nuclei identification/spreading strategy. • The validity of the calculated orientation relationship is verified on a Q and P steel. • The accuracy of the reconstructed microtexture is investigated on a martensite steel.

36 MATERIALS SCIENCE↗

Dynamic shear localization of a titanium alloy under high-rate tension characterized by x-ray digital image correlation

Highlights: • First systematic error analyses for XDIC • First XDIC study of Ti-6Al-4V under dynamic tension • Deformation mode transition induces dynamic ductility decrease. • Dynamic shear localization due to rate-dependent microvoid coalescence process. - Abstract: Dynamic and quasi-static tension experiments are conducted on Ti-6Al-4V alloys, with in situ, synchrotron-based, high-speed, x-ray phase contrast imaging implemented to characterize the dynamic deformation and fracture process of Ti alloys at the Advanced Photon Source. X-ray digital imaging correlation (XDIC) is applied for strain field mapping. The size distribution of x-ray speckles are quantified via a morphological analysis, with a mean of ∼20 μm. Systematic error analyses of displacement and strain field measurements are firstly conducted for XDIC, and demonstrate that the displacement and strain errors can be controlled below 0.01 pixel and 0.1%, respectively. Mesoscale strain characteristics measured via XDIC are consistent with and reveal mechanisms for the bulk-scale stress–strain responses. Under dynamic tension, a sharp transition to strain softening occurs when the bulk strain exceeds about 0.04, which leads to a lower dynamic fracture strain (0.08) than the quasi-static one (0.1). The corresponding strain filed mapping demonstrates that shear deformation localizations grow and coalesce rapidly into a narrow shear deformation band under dynamic loading, while tensile deformation and necking progresses gradually under quasi-static loading. Scanning electron microscopy shows that void nucleation occurs mainly at the interface of α and β phases for both quasi-static and dynamic tension. However, microvoids coalesce preferentially along colony boundaries or the boundary α phase under quasi-static tension, but along the maximum shear stress direction (across colonies) under dynamic tension. Fractography of recovered samples also shows consistent features.

36 MATERIALS SCIENCE↗

The GCT camera for the Cherenkov Telescope Array

The Gamma Cherenkov Telescope (GCT) is one of the designs presented for the Small Sized Telescope (SST) section of the Cherenkov Telescope Array (CTA). The GCT uses dual-mirror optics, resulting in a compact telescope with good image quality and a large field of view with a smaller, more economical, camera than is achievable with conventional single mirror solutions. The photon counting GCT camera is designed to record the flashes of atmospheric Cherenkov light from gamma and cosmic ray initiated cascades, which last only a few tens of nanoseconds. The GCT optics require that the camera detectors follow a convex surface with a radius of curvature of 1 m and a diameter of ~35 cm, which is approximated by tiling the focal plane with 32 modules. The initial camera prototype is equipped with multi-anode photomultipliers, each comprising an 8×8 array of 6×6 mm 2 pixels to provide the required angular scale, adding up to 2048 pixels in total. Detector signals are shaped, amplified and digitised by electronics based on custom ASICs that provide digitisation at 1 GSample/s. The camera is self-triggering, retaining images where the focal plane light distribution matches predefined spatial and temporal criteria. The electronics are housed in the liquid-cooled, sealed camera enclosure. LED flashers at the corners of the focal plane provide a calibration source via reflection from the secondary mirror. The first GCT camera prototype underwent preliminary laboratory tests last year. In November 2015, the camera was installed on a prototype GCT telescope (SST-GATE) in Paris and was further used to successfully record the first Cherenkov light of any CTA prototype, and the first Cherenkov light seen with such a dual-mirror optical system. A second full-camera prototype based on Silicon Photomultipliers is under construction. Up to 35 GCTs are envisaged for CTA.

47 OTHER INSTRUMENTATION↗

Design and R&D of RICH detectors for EIC experiments

An Electron-Ion Collider (EIC) has been proposed to further explore the strong force and QCD, focusing on the structure and the interaction of gluon-dominated matter. A generic detector R& D program (EIC PID consortium) for the particle identification in EIC experiments was formed to explore technologically advanced solutions in this scope. In this context two Ring Imaging Cherenkov (RICH) counters have been proposed: a modular RICH detector which consists of an aerogel radiator, a Fresnel lens, a mirrored box, and pixelated photon sensor; a dual-radiator RICH, consisting of an aerogel radiator and C 2 F 6 gas in a mirror-focused configuration. We present the simulations of the two detectors and their estimated performance.

electron-ion collider↗

Initial assessment of multilayer silicon detectors for hard X-ray imaging

Silicon detectors with lower material budget, ultrafast readout and radiation hardness are under developments. These unique features make pixelized silicon sensors a good option for hard X-ray imaging. To verify the performance of spatial resolution and energy sensitivity of silicon sensors to hard X-rays, a two layer setup of Pixelink PL-D725MU sensors has been tested at the Argonne National Laboratory Advanced Photon Source (APS) ID-10 sector with 29.2 keV high photon flux (4.5 x 10(8) to 4.5 x 10(10) photons per second) X-rays. Better than 3 mu m spatial resolution and clear energy characterization have been achieved by both layers. Commercial CMOS sensors with superior spatial resolution could be used for phase contrast imaging in current synchrotrons. These studies pave the path for future multilayer ultrafast silicon sensor development with ns to sub-ns readout speeds in hard X-ray imaging at synchrotrons and XFEL beamlines.

Hard X-ray imaging↗

Performance of 8 × 8 × 32 and 10 × 10 × 32 mm 3 CdZnTe position-sensitive virtual Frisch-grid detectors for high-energy gamma ray cameras

CdZnTe (CZT) crystals with thicknesses up to 20 mm and diameters up to 75 mm have become available from Redlen Technologies, Inc. for making large-volume gamma-ray detectors. The modified growth and optimized annealing conditions allowed Redlen to minimize the dark current and improve the uniformity of CZT crystals. Two detector designs, pixelated (H3D) and position-sensitive virtual Frisch-grid (VFG), have greatly benefitted from the availability of such big crystals. Encouraging results from testing of 40×40×15 mm 3 pixelated detectors were recently reported by the University of Michigan. In this work, we evaluated the spectroscopic properties of 8×8×32 and 10×10×32 mm 3 crystals configured as position-sensitive VFG detectors. Both digitized waveforms and an analog ASIC were used to read and process the signals from the detectors and test their spectral- and spatial-resolution. The VFG design provides the flexibility to scale-up the dimensions of the detectors for the desired efficiency, while the position information allows for correcting the detectors' response non-uniformity caused by crystal defects and devices geometry, thereby reducing the instrument cost and making them more feasible for emerging applications in gamma-ray astronomy, nonproliferation, portal screening and nuclear safeguards, where large detector arrays are often required.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

State-of-the-art and evolution of UFSD sensors design at FBK

Over the past few years, there has been growing interest in the development of silicon sensors able to simultaneously measure accurately the time of passage and the position of impinging charged particles. In this contribution, a review of the progresses in the design of UFSD (Ultra-Fast Silicon Detectors) sensors, manufactured at the FBK (Fondazione Bruno Kessler) Foundry, aiming at tracking charged particles in 4 dimensions, is presented. The state-of-the-art UFSD sensors, with excellent timing capability, are planned to be used in both ATLAS and CMS experiments detector upgrade, in order to reduce the background due to the presence of overlapping events in the same bunch crossing. The latest results on sensors characterization including time resolution, radiation resistance and uniformity of the response are here summarized, pointing out the interplay between the design of the gain layer and the UFSD performances. The research is now focusing on the maximization of the sensor fill factor, to be able to reduce the pixel size, exploring the implementation of shallow trenches for the pixel isolation and the development of resistive AC-coupled UFSD sensors. In conclusion, a brief review on research paths tailored for detection of low energy X-rays or for low material budget applications is given.

47 OTHER INSTRUMENTATION↗

Performance of neutron and proton irradiated AC-LGAD sensors

Characterization of strip and pixel AC-LGAD devices with both laser TCT and probe station (IV/CV) will be shown on AC-LGADs irradiated with 1 MeV reactor neutrons at JSI/Ljubljana and with 400 MeV protons at FNAL ITA to fluences from 1e13~$n_{eq}/cm^2$ to a few times 1e15~$n_{eq}/cm^2$. This study was conducted within the scope of the ePIC detector time of flight (TOF) layer R&D program at the EIC, which will feature AC-LGADs with strip and pixel geometry. Sensors in the TOF layer will receive up to 1e13 $n_{eq}/cm^2$ fluence over the lifetime of the experiment.

AC-LGAD strips↗

The ladder and readout cables of intermediate silicon strip detector for sPHENIX

A new silicon-strip-type detector was developed for precise charged-particle tracking in the central rapidity region of heavy ion collisions. A new detector and collaboration at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory is sPHENIX, which is a major upgrade of the PHENIX detector. The intermediate tracker (INTT) is part of the advanced tracking system of the sPHENIX detector complex together with a CMOS monolithic-active-pixel-sensor based silicon-pixel vertex detector, a time-projection chamber, and a micromegas-based detector. The INTT detector is barrel shaped and comprises 56 silicon ladders. Two different types of strip sensors of 78 micron pitch and 320 micron thick are mounted on each half of a silicon ladder. Each strip sensor is segmented into 8 × 2 and 5 × 2 blocks with lengths of 16 and 20 mm. Strips are read out with a silicon strip-readout (FPHX) chip. In order to transmit massive data from the FPHX to the down stream readout electronics card (ROC), a series of long and high speed readout cables were developed. This document focuses on the silicon ladder, the readout cables, and the ROC of the INTT. The radiation hardness is studied for some parts of the INTT devices in the last part of this document, since the INTT employed some materials from the technology frontier of the industry whose radiation hardness is not necessarily well known.

43 PARTICLE ACCELERATORS↗

Evaluation of the absolute single-photon detection efficiency of HRPPD

Pixelated High Rate Picosecond Photon Detectors (HRPPDs) by Incom Inc. are promising photosensors for use in Ring Imaging CHerenkov (RICH) detectors, where a high gain, sub-mm position resolution and sub-100ps timing resolution are required in a single photon mode. Quantum Efficiency (QE) has been measured for the first batch of EIC HRPPDs both by Incom and EIC research groups at Jefferson Lab and Brookhaven Lab, with peak values at ∼365 nm typically exceeding 30%. Furthermore, in this study, we present a first direct measurement of Photon Detection Efficiency being equal to (17.1 ± 0.1 [stat] ± 0.3 [sys])% at 398.6 nm, for a pixel near the center of HRPPD, in a photoelectron pulse counting mode using a picosecond diode laser. HRPPD QE at the same spot and at the same wavelength was evaluated to be (24.4 ± 0.1 [stat] ± 0.3 [sys])%, leading to a Collection Efficiency estimate of (70.3 ± 1.6)%, which is consistent with the expectations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Beam test performance of AstroPix sensor with 120 GeV protons

AstroPix is a High-Voltage CMOS Monolithic Active Pixel Sensor (HV-CMOS MAPS) developed for precision gamma-ray imaging and spectroscopy in the medium-energy regime, as well as for precise shower imaging and tracking in the Barrel Imaging Calorimeter (BIC) of the Electron Proton/Ion Collider (ePIC) detector at the future Electron–Ion Collider (EIC). We present beam test results of the AstroPix_v3 sensor using a 120 GeV proton beam at the Fermilab Test Beam Facility (FTBF), performed as part of the broader experimental campaign for the BIC prototype calorimeter. The sensor’s 500 µm pixel pitch enabled precise measurement of the beam profile, providing important information for the calorimeter performance studies. Using the measured 120 GeV proton data, we measure the energy deposit of minimum ionizing particles (MIP) and use them to extract the corresponding effective depletion depth at a single bias voltage of −150 V .

AstroPix↗