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Rapid and standoff analysis of uranium is important for many applications. We present the recent advances in standoff detection of uranium using emission and laser-induced fluorescence spectroscopy in conjunction with ultrafast laser filament ablation-based solid sampling.
Quantum ghost imaging approaches have been proposed to enhance biological microscopy, for example, using 2D visible detectors to provide IR images or providing additional dimensions of spatial or spectral information. Toward the goal of making such imaging schemes practical, we compare image quality and depth-of-field between traditional images and ghost images at the same excitation levels. We measure how image quality and depth-of-field depend on the parameters of the entangled light produced using type-I spontaneous parametric down-conversion (SPDC). We use a pair of time-synchronized, photon-timing single-photon avalanche diode (SPAD) array detectors to capture two distinct microscope imaging paths simultaneously on a photon-pair-by-photon-pair basis: one in a traditional imaging pathway and the other a quantum ghost imaging pathway. We calculate the depth-of-field, resolution, contrast, and signal-to-noise ratio (SNR) through the parameter space of a β-Barium Borate (BBO) type-I bulk non-linear crystal length and angle. Our results provide a basis for choosing parameters for quantum ghost imaging with type-I SPDC sources.
Multi-modal Free-moving Data Fusion (MFDF) is a python package that implements low and high-level functionality for performing qualitative and quantitative gamma-ray imaging analyses. Such analyses enable the use of gamma-ray spectrometers and/or imagers to estimate the distribution and quantity of radiological materials in an environment. Free-moving 3D imaging requires additional information about the 3D trajectory and orientation of the system, however all of these methods can be applied to 2D static gamma-ray imaging as well. The package includes methods (MLEM and MAP) for distributed source reconstruction, methods (PSL) for point source reconstruction, the ability to ingest and utilize quantitative detector response functions (for absolute analyses), and the ability to perform 3D estimation of dose-rates from quantitative reconstructions.
Refraction of an optical probe beam by a plasma can be measured with angular filter refractometry (AFR), which produces an image of the beam’s 2D spatial profile that contains intensity contours corresponding to curves of constant refraction angle. Further analysis is required to reconstruct the underlying line-integrated electron density. Most prior efforts to calculate density from AFR data have been limited to 1D analysis or forward-fitting techniques. Here, in this paper, we detail the use of a fast-marching Eikonal solver to directly invert AFR data and obtain the full 2D line-integrated electron density. The analysis method is first verified with synthetic data and then applied to experimental measurements of single and colliding plasma plumes collected at the OMEGA EP Laser Facility. The calculated densities agree with 1D results and are shown to be consistent with the original AFR measurements via forward modeling. We also discuss ways to improve the precision of this technique.
A simple and fast algorithm for generating two correlated stochastic twodimensional (2D) cloud fields is described. The algorithm is illustrated with two broken cumulus cloud fields: cloud optical depth and cloud top height retrieved from Moderate Resolution Imaging Spectrometer (MODIS). Only two 2D fields are required as an input. The algorithm output is statistical realizations of these two fields with approximately the same correlation and joint distribution functions as the original ones. The major assumption of the algorithm is statistical isotropy of the fields. In contrast to fractals and the Fourier filtering methods frequently used for stochastic cloud modeling, the proposed method is based on spectral models of homogeneous random fields. For keeping the same probability density function as the (first) original field, the method of inverse distribution function is used. When the spatial distribution of the first field has been generated, a realization of the correlated second field is simulated using a conditional distribution matrix. This paper is served as a theoretical justification to the publicly available software that has been recently released by the authors and can be freely downloaded from http://i3rc.gsfc.nasa.gov/Public codes clouds.htm. Though 2D rather than full 3D, stochastic realizations of two correlated cloud fields that mimic statistics of given fields have proved to be very useful to study 3D radiative transfer features of broken cumulus clouds for better understanding of shortwave radiation and interpretation of the remote sensing retrievals.
Exploring new two-dimensional (2D) van der Waals (vdW) systems is at the forefront of materials physics. Here, through molecular beam epitaxy on graphene-covered SiC(0001), we report successful growth of AlSb in the double-layer honeycomb (DLHC) structure, a 2D vdW material which has no direct analogue to its 3D bulk and is predicted kinetically stable when freestanding. The structural morphology and electronic structure of the experimental 2D AlSb are characterized with spectroscopic imaging scanning tunneling microscopy and cross-sectional imaging scanning transmission electron microscopy, which compare well to the proposed DLHC structure. The 2D AlSb exhibits a bandgap of 0.93 eV versus the predicted 1.06 eV, which is substantially smaller than the 1.6 eV of bulk. We also attempt the less-stable InSb DLHC structure; however, it grows into bulk islands instead. Here, the successful growth of a DLHC material here opens the door for the realization of a large family of novel 2D DLHC traditional semiconductors with unique excitonic, topological, and electronic properties.
VESsel GENeration (VESGEN) Analysis is an automated software that maps and quantifies effects of vascular regulators on vascular morphology by analyzing important vessel parameters. Quantification parameters include vessel diameter, length, branch points, density, and fractal dimension. For vascular trees, measurements are reported as dependent functions of vessel branching generation. VESGEN maps and quantifies vascular morphological events according to fractal-based vascular branching generation. It also relies on careful imaging of branching and networked vascular form. It was developed as a plug-in for ImageJ (National Institutes of Health, USA). VESGEN uses image-processing concepts of 8-neighbor pixel connectivity, skeleton, and distance map to analyze 2D, black-and-white (binary) images of vascular trees, networks, and tree-network composites. VESGEN maps typically 5 to 12 (or more) generations of vascular branching, starting from a single parent vessel. These generations are tracked and measured for critical vascular parameters that include vessel diameter, length, density and number, and tortuosity per branching generation. The effects of vascular therapeutics and regulators on vascular morphology and branching tested in human clinical or laboratory animal experimental studies are quantified by comparing vascular parameters with control groups. VESGEN provides a user interface to both guide and allow control over the users vascular analysis process. An option is provided to select a morphological tissue type of vascular trees, network or tree-network composites, which determines the general collections of algorithms, intermediate images, and output images and measurements that will be produced.
Optical reflectance imaging is a popular technique for characterizing 2D materials, thanks to its simplicity and speed of data acquisition. The use of this method for studying interlayer phenomena in stacked 2D layers has, however, remained limited. Here we demonstrate that optical imaging can reveal the nature of interlayer coupling in stacked MoS 2 and WS 2 bilayers through their observed reflectance contrast versus the substrate. Successful determination of interlayer coupling requires co-optimization of the illumination wavelength and the thickness of an underlying SiO 2 film. Our observations are supported by multilayer optical calculations together with an analysis of the effect of any interlayer gap. This approach promises quick characterization of constructed 2D material systems.
System-on-chip (SoC) millimeter wave integrated circuit technology has been developed and employed in chip heterodyne radiometers in Electron Cyclotron Emission Imaging (ECEI) and Microwave Imaging Reflectometer (MIR) on the DIII-D tokamak for 2D electron temperature and density fluctuation diagnostics. The W-band (75-110 GHz) ECEI system has been employed with 20 poloidal horn receiver modules with customized monolithic integrated circuit chips and internal local oscillator coupling path. The new SoC approach demonstrates > 30 dB additional gain on the receiver and 95% electronics noise suppression compared to the previous Schottky diode mixer array . An F-band (110-140 GHz) receiver chip has been developed to measure MHD and turbulence behavior on the DIII-D high-field side, such as Alfven eigenmodes. Furthermore, it can image pedestal structure evolution on fusion facilities with stronger magnetic fields, such as ASDEX-U, EAST, HL-2M, and LHD. The F-band chip performance has been tested in the laboratory. Also, V-band (55-75 GHz) transmitter and receiver modules (55-75 GHz) have been developed for DIII-D and NSTX-U Furthermore, microwave Imaging Reflectometer (MIR) application suitability for 2D density fluctuation imaging on the pedestal region.
This document is the final report for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” This project was motivated by the availability of three-dimensional (3D) tomographic neutron transmission and induced-reaction data following the development of associated-particle imaging (API) systems with two-dimensional (2D) or “area” neutron detectors such as the Advanced Portable Neutron Imaging System, which is capable of simultaneous measurement of transmission and induced-reaction neutrons.
In operando 2D X-ray absorption near edge structure (XANES) imaging was performed near the Cu K-edge during cycling of Cu 6 Sn 5 composite anodes for lithium ion batteries. Galvanostatic lithiation and delithiation with intermittent constant voltage holds near reaction plateaus show evolution of absorption spectra for active material particles. XANES spectra obtained from images taken during cycling were compared to standard spectra for Cu, Cu 6 Sn 5 , and Li 2 CuSn. Chemical composition was assessed for Cu-containing phases. Distinct Cu, Cu 6 Sn 5 , and Li 2 CuSn regions were identified for each voltage plateau. Mechanical degradation, electrode particle fracture and expansion were observed during delithiation. Furthermore, movement of particles during cycling suggests that expansion also impacts the supporting secondary phases and the transport networks therein. These results demonstrate that spectroscopic X-ray imaging methods can clearly distinguish chemically distinct phases in alloy electrodes and have the versatility to observe the evolution of these phases during lithiation and delithiation.
Lipid bilayers are supramolecular structures responsible for a range of processes, such as transmembrane transport of ions and solutes, and sorting and replication of genetic materials, to name just a few. Some of these processes are transient and currently, cannot be visualized in real space and time. Here, we developed an approach using 1D, 2D, and 3D Van Hove correlation functions to image collective headgroup dipole motions in zwitterionic phospholipid bilayers. We show that both 2D and 3D spatiotemporal images of headgroup dipoles are consistent with commonly understood dynamic features of fluids. However, analysis of the 1D Van Hove function reveals lateral transient and re-emergent collective dynamics of the headgroup dipoles—occurring at picosecond time scales—that transmit and dissipate heat at longer times, due to relaxation processes. At the same time, the headgroup dipoles also generate membrane surface undulations due a collective tilting of the headgroup dipoles. A continuous intensity band of headgroup dipole spatiotemporal correlations—at nanometer length and nanosecond time scales—indicates that dipoles undergo stretching and squeezing elastic deformations. Importantly, the above mentioned intrinsic headgroup dipole motions can be externally stimulated at GHz-frequency scale, enhancing their flexoelectric and piezoelectric capabilities (i.e., increased conversion efficiency of mechanical energy into electric energy). In conclusion, we discuss how lipid membranes can provide molecular-level insights about biological learning and memory, and as platforms for the development of the next generation of neuromorphic computers.
Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.
Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.
The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.
Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.
A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.
The Wigner crystal has fascinated condensed matter physicists for nearly 90 years. Signatures of two-dimensional (2D) Wigner crystals were first observed in 2D electron gases under high magnetic field, and recently reported in transition metal dichalcogenide moiré superlattices. Direct observation of the 2D Wigner crystal lattice in real space, however, has remained an outstanding challenge. Conventional scanning tunnelling microscopy (STM) has sufficient spatial resolution but induces perturbations that can potentially alter this fragile state. Here we demonstrate real-space imaging of 2D Wigner crystals in WSe 2 /WS 2 moiré heterostructures using a specially designed non-invasive STM spectroscopy technique. This employs a graphene sensing layer held close to the WSe 2 /WS 2 moiré superlattice. Local STM tunnel current into the graphene layer is modulated by the underlying Wigner crystal electron lattice in the WSe 2 /WS 2 heterostructure. Different Wigner crystal lattice configurations at fractional electron fillings of n = 1/3, 1/2 and 2/3, where n is the electron number per site, are directly visualized. The n = 1/3 and n = 2/3 Wigner crystals exhibit triangular and honeycomb lattices, respectively, to minimize nearest-neighbour occupations. The n = 1/2 state spontaneously breaks the original C3 symmetry and forms a stripe phase. Here our study lays a solid foundation for understanding Wigner crystal states in WSe 2 /WS 2 moiré heterostructures and provides an approach that is generally applicable for imaging novel correlated electron lattices in other systems.