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DOE OSTI2023

Adaptive methods of generating complex light arrays

Structured light arrays of various shapes have been a cornerstone in optical science, driven by the complexities of precise and adaptable generation. This study introduces an approach using a spatial light modulator (SLM) as a generator for these arrays. By projecting a holographic mask onto the SLM, it functions simultaneously as an optical convolution device, focusing mechanism, and structured light beam mask. Our approach offers unmatched versatility, allowing for the experimental fabrication of traditional beam arrays like azimuthal Laguerre–Gaussian (LG), Bessel–Gaussian (BG), and Hermite–Gauss (HG) in the far-field. Notably, it has enabled a method of generating Ince–Gauss (IG) and LG radial mode beam arrays using a convolution solution. Our system provides exceptional control over array periodicity and intensity distribution, bypassing the Talbot self-imaging phenomenon seen in traditional setups. We provide an in-depth theoretical discussion, supported by empirical evidence, of our far-field results. This method has vast potential for applications in optical communication, data processing, and multi-particle manipulation. It paves the way for rapid generation of structured light with high spatial frequencies and complex shapes, promising transformative advances in these domains.

Optics↗

DOE OSTI2020

Detector Tilt Considerations in Bragg Coherent Diffraction Imaging: A Simulation Study

This paper addresses the three-dimensional signal distortion and image reconstruction issues in X-ray Bragg coherent diffraction imaging (BCDI) in the event of a general non-orthogonal orientation of the area detector with respect to the diffracted beam. Growing interest in novel BCDI adaptations at fourth-generation synchrotron light sources has necessitated improvisations in the experimental configuration and the subsequent data analysis. One such possibly unavoidable improvisation that is envisioned in this paper is a photon-counting area detector whose face is tilted away from the perpendicular to the Bragg-diffracted beam during the acquisition of the coherent diffraction signal. We describe a likely circumstance in which one would require such a detector configuration, along with the experimental precedent at third-generation synchrotrons. Using physically accurate diffraction simulations from synthetic scatterers in the presence of such tilted detectors, we analyze the general nature of the observed signal distortion qualitatively and quantitatively and provide a prescription to correct for it during image reconstruction. Our simulations and reconstructions are based on an adaptation of the known theory of BCDI sampling geometry, as well as the recently developed projection-based methods of wavefield propagation. Such configurational modifications and their numerical remedies are potentially valuable in realizing unconventional coherent diffraction measurement geometries, eventually paving the way for the integration of BCDI into new material characterization experiments at next-generation light sources.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Improved noise performance from the next-generation buried-channel p-MOSFET SiSeROs

The Single electron Sensitive Read Out (SiSeRO) is a novel on-chip charge detector output stage for charge-coupled device (CCD) image sensors. Developed at MIT Lincoln Laboratory, this technology uses a p-MOSFET transistor with a depleted internal gate beneath the transistor channel. The transistor source-drain current is modulated by the transfer of charge into the internal gate. At Stanford, we have developed a readout module based on the drain current of the on-chip transistor to characterize the device. In our earlier work, we characterized a number of first prototype SiSeROs with the MOSFET transistor channels at the surface layer. An equivalent noise charge (ENC) of around 15 electrons root mean square (RMS) was obtained. In this work, we examine the first buried-channel SiSeRO. Here we have achieved substantially improved noise performance of around 4.5 electrons root mean square (RMS) and a full width half maximum (FWHM) energy resolution of 132 eV at 5.9 keV, for a readout speed of 625 kpixel/s. We also discuss how digital filtering techniques can be used to further improve the SiSeRO noise performance. Additional measurements and device simulations will be essential to further mature the SiSeRO technology. This new device class presents an exciting new technology for the next-generation astronomical X-ray telescopes requiring fast, low-noise, radiation-hard megapixel imagers with moderate spectroscopic resolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Imaging Nanoscale Energy Transport and Conversion with Ultrafast Electron Microscopy (Final Technical Report)

Light-matter interactions are ubiquitous in nature and reside at the heart of innumerable technologies. The cascade of processes that occur when a material absorbs a photon of light are exceedingly complex and are interwoven in both space and time, rendering precise determination of the atomic-scale and ultrafast mechanisms immensely challenging. The advent of methods for generating short pulses of light several decades ago led to major advances in understanding the initial moments of light absorption and the resultant effects, though directly interrogating the response of the atoms within the material continued to prove challenging. More recently, methods for generating ultrashort pulses of X-rays and fast electrons have opened the way to probing photoinduced structural dynamics of a wide range of matter in multiple phases. An especially promising laboratory-scale method is ultrafast electron microscopy (UEM), wherein the modalities of conventional transmission electron microscopes (imaging, diffraction, spectroscopy) are extended into the femtosecond temporal regime. Here, the ultrafast imaging and diffraction modalities of UEM were used to study the transient structural aspects of photoexcitation of semiconducting materials (e.g., spatially-resolved electron-phonon coupling, excitation and emission of acoustic phonons, and discrete nanoscale scattering processes). The project had three main objectives: (1) determination of the excitation mechanisms of dense, hypersonic charge-carrier waves and the spatially mediated means by which they couple to the lattice via coherent phonon emission, especially with UEM imaging, wherein effects of nanoscale structural and morphological features on the coupling and relaxation dynamics are expected to dictate nucleation sites and preferred wave vectors, (2) elucidation of photoinduced acoustic-phonon seeding, emergence, propagation, and decay over nanoscale crystal regions and especially with respect to local strain fields and atomic-scale disruptions in lattice order, and (3) interwoven with the first two were efforts aimed at realizing combined angstrom-femtosecond spatiotemporal imaging with UEM. The outcomes and impacts of this project were the generation of new knowledge with respect to fundamental light-matter interactions and, especially, the spatially-mediated excitation and evolution of the structural response of materials following coherent photoexcitation. Importantly, the spatial and temporal resolutions of the UEM imaging modalities used are well-suited for such studies and enable spatially-resolved mechanisms to be determined as a function of atomic order, structural features, and morphology. In addition, ultrafast crystallographic measurements were used to correlate real- and reciprocal-space dynamics in order to determine atomic-scale preferential wave vectors and ultrafast scattering mechanisms, especially as dictated by specimen boundary conditions. The obtained results, as detailed in peer-reviewed publications and presentations, illustrate the importance of ultrafast, angstrom-scale real-space imaging for developing a comprehensive understanding of energy transport and conversion in materials.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

The role of ambient gas confinement, plasma chemistry, and focusing conditions on emission features of femtosecond laser-produced plasmas

Ultrafast laser ablation coupled with optical emission spectroscopy is currently under development for standoff detection of elements and their isotopes for a wide range of application areas. In this work, we explore the role of ambient air pressure during the interaction of an ultrafast laser beam with two metallic targets (brass and uranium) with significantly different oxygen gas-phase reactivity. Plasma plumes were generated by focusing ~800 nm, ~35 fs pulses from an ultrafast laser system using a long focal length lens with f = 1 m. Two-dimensional spectral imaging was performed over the pressure range of 30 mTorr-700 Torr air to evaluate emission dynamics, plasma chemistry, signal-to-background ratio, and characteristic parameters (i.e. excitation temperature, electron density). An increase in ambient air pressure during ultrafast laser ablation leads to plume confinement and subsequent changes in the emission dynamics due to plasma chemistry as well as changes in plasma generation conditions (i.e. focused fs laser ablation versus filament ablation). Ablation crater morphologies were also investigated via scanning electron microscopy. Results indicate atomic emission intensity and signal-to-background ratios peak at moderate pressure levels (~50-100 Torr air) for both targets studied, although plasma chemistry influences uranium emission signatures. The emission features of the uranium plasma at pressures = 10 Torr showed the presence of atoms and oxide molecules which are co-located within the plume. We also find filament ablation leads to wider, more shallow craters compared to focused laser ablation. Our study provides unique insight into the interplay between plume dynamics, confinement, and oxygen chemistry of fs laser-produced plasmas and how these phenomena evolve with changing ambient air pressure.

Laser-produced plasma↗

DOE OSTI2023

Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys

The Hubble tension, revealed by a ~5σ discrepancy between measurements of the Hubble-Lemaitre constant among observations of the early and local Universe, is one of the most significant problems in modern cosmology. In order to better understand the origin of this mismatch, independent techniques to measure H 0 , such as strong lensing time delays, are required. Notably, the sample size of such systems is key to minimising the statistical uncertainties and cosmic variance, which can be improved by exploring the datasets of large-scale sky surveys such as Dark Energy Spectroscopic Instrument (DESI). We identify possible strong lensing time-delay systems within DESI by selecting candidate multiply imaged lensed quasars from a catalogue of 24 440 816 candidate QSOs contained in the ninth data release of the DESI Legacy Imaging Surveys (DESI-LS). Using a friend-of-friends-like algorithm on spatial co-ordinates, our method generates an initial list of compact quasar groups. This list is subsequently filtered using a measure of the similarity of colours among a group’s members and the likelihood that they are quasars. A visual inspection finally selects candidate strong lensing systems based on the spatial configuration of the group members. We identified 620 new candidate multiply imaged lensed quasars (101 grade-A, 214 grade-B, 305 grade-C). This number excludes 53 known spectroscopically confirmed systems and existing candidate systems identified in other similar catalogues. When available, these new candidates will be further checked by combining the spectroscopic and photometric data from DESI.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Feature identification or classification using task-specific metadata

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

Teuton, Jeremy R.↗

DOE OSTI2022

Advancing Geophysical Techniques to Image a Stratigraphic Hydrothermal Resource

Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this resource and analogous ones. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data products, in conjunction with new and preexisting geoscientific measurements and observations, are being integrated and evaluated for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings, and whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs, will also be evaluated.

Geothermal, Sedimentary Heat, Geophysics, Seismic,↗

DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Quantum Imaging with X-rays

Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it is necessary to generate specific quantum states of light and detect photons at the single-photon level. While this method has been successfully demonstrated in the visible electromagnetic spectrum, extending it to X-rays has faced significant challenges due to the difficulties in producing a sufficient rate of X-ray photon pairs and detecting them with adequate resolution. Here, we demonstrate record high rates of correlated X-ray photon pairs produced via a spontaneous parametric down-conversion process and we employ these photons to perform quantum correlation imaging of several objects, including a biological sample (E. cardamomum seedpod). Notably, we report an unprecedented detection rate of about 6,300 pairs per hour and the observation of energy anti-correlation for the X-ray photon pairs. We also present a detailed analysis of the properties of the down-converted X-ray photons, as well as a comprehensive study of the correlation imaging formation, including a study of distortions and corrections. These results mark a substantial advancement in X-ray quantum imaging, expanding the possibilities of X-ray quantum optical technologies, and illustrating the pathway towards enhancing biological imaging with reduced radiation doses.

Gofron, Kaz [ORNL] (ORCID:0000000314415736)↗

DOE OSTI2026

Gas-driven short disconnection mitigates thermal runaway in Li-ion batteries under mechanical abuse

Mechanical abuse poses a critical safety risk to Li-ion batteries by inducing internal short circuits that initiate thermal runaway. Remarkably, voltage recovery frequently emerges during thermal runaway initiation, a phenomenon that conventional theories fail to explain. Here, our study develops a new multiphysics mechanism to explain voltage recovery, termed gas-driven short disconnection, whereby internal gas pressure mechanically disengages short-circuit contacts and causes the cell voltage to rebound. This mechanism incorporates gas generation and its structural impact on the short circuit. Real-time optical and thermal imaging and X-ray computed tomography reveal fluid-structure interaction between internal gas flow and adjacent shorting contacts. We establish a mechanistic framework linking gas-driven short disconnection to cell-level voltage and temperature responses, elucidating the extension–truncation pattern of voltage recovery. Furthermore, thermal regime maps show that a voltage recovery duration exceeding 5 s correlates with limited temperature rise below 150 °C, indicating that sustained short-circuit disconnection suppresses Joule heating. Additionally, a dimensionless criterion is deduced from scaling analysis for physical plausibility of gas-driven short disconnection in mechanically abused cells. This finding inspires smart venting control, which regulates gas release to maintain the internal pressure while dissipating gas enthalpy, thereby providing a device-level strategy for thermal runaway mitigation.

Gas-driven short disconnection↗

DOE OSTI2023

Erasure conversion in a high-fidelity Rydberg quantum simulator

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices and for the quest towards fault-tolerant quantum computation. Rydberg arrays have emerged as a prominent platform in this context with impressive system sizes and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution, a form of erasure error conversion. However, two-qubit entanglement fidelities in Rydberg atom arrays have lagged behind competitors and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator. When excising data with erasure errors observed via fast imaging of alkaline-earth atoms, we achieve a Bell state fidelity of $\ge 0.997{1}_{-13}^{+10}$ , which improves to $\ge 0.998{5}_{-12}^{+7}$ when correcting for remaining state-preparation errors. We further apply erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, and reveal the otherwise hidden impact of these errors on the simulation outcome. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the 0.999 regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices. These techniques could be translated directly to quantum-error-correction codes with the addition of long-lived qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Bulk halide perovskites as triplet sensitizers: progress and prospects in photon upconversion

Triplet–triplet annihilation-based photon upconversion (TTA-UC) is a promising mechanism for harvesting lower energy photons by converting them to a higher energy. Photons generated from this process can be used for numerous applications, including photovoltaics, infrared sensing and imaging, biomedicine and photochemical reactions. Recently, bulk metal halide perovskite semiconductors have been introduced as triplet sensitizers for the TTA-UC process. While relatively efficient upconversion has been achieved at low fluences, the full potential of these materials as triplet sensitizers has not been unlocked. In this study, we examine four pathways for device optimization and improvements, while discussing relevant works and potential further improvements. Finally, we discuss the outlook and bright future of such perovskite materials as triplet sensitizers and their important role in solid-state upconversion applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

High spatial resolution direct conversion amorphous selenium X-ray detectors with monolithically integrated CMOS readout

Recent progress in the field of micron-scale spatial resolution direct conversion X-ray detectors for high-energy synchrotron light sources serve applications ranging from nondestructive and noninvasive microscopy techniques which provide insight into the structure and morphology of crystals, to medical diagnostic measurement devices. Amorphous selenium ( a -Se) as a wide-bandgap thermally evaporated photoconductor exhibits ultra-low thermal generation rates for dark carriers and has been extensively used in X-ray medical imaging. Being an amorphous material, it can further be deposited over large areas at room temperatures and at substantially lower costs as compared to crystalline semiconductors. Here, to address the demands for a high-energy and high spatial resolution X-ray detector for synchrotron light source applications, we have thermally evaporated a -Se on a Mixed-Mode Pixel Array Detector (MM-PAD) Application Specific Integrated Circuit (ASIC). The ASIC format consists of 128 × 128 square pixels each 150 μm on a side. A 200 μm a -Se layer was directly deposited on the ASIC followed by a metal top electrode. The completed detector assembly was tested with 45 kV Ag and 23 kV Cu X-ray tube sources. The detector fabrication, performances, Modulation Transfer Function (MTF) measurements, and simulations are reported.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the Bandwidth and Beam Profile Characteristics of a Simple Low-Frequency Collimated Ultrasound Beam Source

We numerically investigate the bandwidth and collimation characteristics of ultrasound beams generated by a simple collimated ultrasound beam source that consists of a piezoelectric disk operated near its radial mode resonances. We simulate the ultrasound beam generated in a fluid medium as a function of the excitation frequency for two cases: (1) free piezoelectric disk that corresponds to zero-traction along the lateral edge and (2) fixed piezoelectric disk that corresponds to zero-displacement along the lateral edge. We present and discuss the physical mechanism underpinning the frequency-dependent collimation and bandwidth properties of the ultrasound beams. We observe that the collimated beam generated by the free disk repeatedly lengthens/shortens and also extends/retracts sidelobes with increasing frequency. Alternatively, fixing the piezoelectric disk results in a consistent beam profile shape across a broad range of frequencies. This facilitates generating broadband signals such as a Gaussian pulse or chirp, which are common in ultrasound imaging. Thus, the fixed piezoelectric disk finds application as a collimated ultrasound beam source in a wide range of applications including medical ultrasound imaging, scanning acoustic microscopy, sonar detection, and other nondestructive ultrasound inspection techniques.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.

Rasheed, Fayaz↗

DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Deep ML for inference from indirect measurements

The magic of deep machine learning (DML): Neural networks that can beat Go grandmasters. Networks that have better than human accuracy at classifying images in over 100 categories. Making still pictures move realistically. Super-resolution versions of very old film clips. Realistic, high-resolution, images of people that have never existed. Realistic insertion of people into video clips. Network generated music and writing that passes for human created. How can this technology be leveraged for physics and engineering?

97 MATHEMATICS AND COMPUTING↗