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At least 307 records · Page 17

All sources NASA NTRS DOE OSTI

DOE OSTI2021

All-sky aerosol direct radiative effects at the ARM SGP site

All-sky aerosol direct radiative effect (DRE) was estimated for the first time at the Atmospheric Radiation Measurement Southern Great Plains site using multi-year ground-based observations. The NASA Langley Fu-Liou radiation model was employed. Observed inputs for the radiation model include aerosol and cloud vertical extinction profile from Raman lidar; spectral aerosol optical depth, single-scattering albedo, and asymmetry factor from Aerosol Robotic Network; cloud water content profiles from radars; temperature and water vapor profiles from radiosondes; and surface shortwave spectral albedo from radiometers. A cloudy-sky radiative closure experiment was performed. The relative mean differences between modeled and observed surface downwelling shortwave total fluxes were 6% (7%) for transparent (opaque) cloudy-skies. The estimated annual mean all-sky aerosol DRE is -2.13±0.54W m -2 at the top of atmosphere (TOA) and -5.95±0.87 W m -2 at the surface, compared to -3.00±0.58 W m -2 AND -6.85±1.00 W m -2 , respectively, under clear-sky conditions. The seasonal cycle of all-sky aerosol DRE is similar to that of the clear-sky, except with secondary influences of the clouds: The cloud radiative effect is strongest (most negative) in the spring, which reduces the all-sky aerosol DRE. The relative uncertainties in all-sky aerosol DRE due to measurement errors are generally comparable to those in clear-sky conditions except for the aerosol single-scattering albedo. Finally, the TOA all-sky aerosol DRE relative uncertainty due to aerosol single-scattering albedo uncertainty is larger than that in clear-sky, leading to a larger total relative uncertainty. The measurement errors in cloud properties have small effects on the all-sky aerosol DRE.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Visualizing moiré ferroelectricity via plasmons and nano-photocurrent in graphene/twisted-WSe2 structures

Abstract Ferroelectricity, a spontaneous and reversible electric polarization, is found in certain classes of van der Waals (vdW) materials. The discovery of ferroelectricity in twisted vdW layers provides new opportunities to engineer spatially dependent electric and optical properties associated with the configuration of moiré superlattice domains and the network of domain walls. Here, we employ near-field infrared nano-imaging and nano-photocurrent measurements to study ferroelectricity in minimally twisted WSe 2 . The ferroelectric domains are visualized through the imaging of the plasmonic response in a graphene monolayer adjacent to the moiré WSe 2 bilayers. Specifically, we find that the ferroelectric polarization in moiré domains is imprinted on the plasmonic response of the graphene. Complementary nano-photocurrent measurements demonstrate that the optoelectronic properties of graphene are also modulated by the proximal ferroelectric domains. Our approach represents an alternative strategy for studying moiré ferroelectricity at native length scales and opens promising prospects for (opto)electronic devices.

42 ENGINEERING↗

DOE OSTI2024

Deep-learning optical flow for measuring velocity fields from experimental data

Deep learning-based optical flow (DLOF) extracts features in video frames with deep convolutional neural networks to estimate the inter-frame motions of objects. DLOF computes velocity fields more accurately than PIV for densely labeled systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Ab initio molecular dynamics simulation of Na-doped aluminosilicate glasses and glass-water interaction

The structure and properties of sodium aluminosilicate (NAS) glasses are investigated using ab initio molecular dynamics and density functional calculations. Four NAS glass models of about 700 atoms with composition (SiO 2 ) 0.6 (Al 2 O 3 ) 0.4-x (Na 2 O) x with Na/Al ratio R = 0.0, 0.5, 1.0 and 1.5 are constructed corresponding to x = 0, 0.135, 0.20 and 0.24. Detailed information on network coordination, electronic structure, interatomic bonding and partial charge distribution, mechanical and optical properties of these models are presented and fully analyzed. The structural details for each R are discussed in terms of short- and intermediate-range order manifested in the coordination number, atomic pair and bond angle distributions. It is shown that the mechanical strength of NAS glasses decreases with increasing Na content, indicating that pure aluminosilicate glass is stronger than the alkali-doped glasses. We use the novel concept of total bond order density to characterize the internal cohesion of the NAS glasses. In the case of R = 1 NAS model, 12 water molecules are added to investigate the solvation effect and hydrolysis in NAS glass.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Spin-Photon Entanglement of a Single Er 3 + Ion in the Telecom Band

Entanglement between photons and a quantum memory is a key component of quantum repeaters, which allow long-distance quantum entanglement distribution in the presence of fiber losses. Spin-photon entanglement has been implemented with a number of different atomic and solid-state qubits with long spin coherence times, but none directly emit photons into the 1.5 − μ m telecom band where losses in optical fibers are minimized. Here, we demonstrate spin-photon entanglement using a single rare earth ion in the solid-state Er 3 + coupled to a silicon nanophotonic cavity, which directly emits photons at 1532.6 nm. We infer an entanglement fidelity of 73(3)% after propagating through 15.6 km of optical fiber. This work opens the door to large-scale quantum networks based Er 3 + ions, leveraging scalable silicon device fabrication and spectral multiplexing. Published by the American Physical Society 2025

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗

DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

03 NATURAL GAS↗

DOE OSTI2024

ARC-SAFE: Accelerated Response semiconducting Contactors and Surge Attenuation For DC Electrical systems (Final Scientific/Technical Report)

The landscape of power transmission and distribution is quickly evolving as more power conversion is done through power electronics and transmitted or distributed at medium voltage direct current (MVDC). As power electronics tend to store less energy and be less resilient to faults than conventional power transformers, a reliable and fast protection against faults is critical to protect power electronics (PE) based infrastructure, especially for medium- and high-voltage applications. This motivates the development of PE based protection circuits to replace slower contemporary electromechanical breakers. In this project a novel PE based MVDC circuit breaker is developed; the new design is comprised of 1) a normally-on leg made of commercially-available, cascaded SiC junction field effect transistors (JFETs) with a passive balancing network, and 2) a normally-off leg based on an optically-triggered gallium nitride (GaN) photoconductive semiconductor switch (PCSS). The normally-off leg was designed to be quickly turned on, to divert current to an auxiliary dissipative circuit, as the normally-on leg is turned off. This approach, using solid-state devices, was selected for a high-performance, fast-switching operation for the DC circuit breaker as compared to approaches that use slower mechanical switches. To be practical, the circuit breaker must have low conduction loss (low Ron) in the normally-on leg and fast coordinated triggering of the normally-off leg to avoid damage from inductive flyback, which could be considerable for long lengths of cable.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Gravitational Lenses in UNIONS and Euclid (GLUE). I. A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data

We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train a deep residual convolutional neural network based on CMU-Deeplens architecture, which is designed to detect strong lenses in ground-based imaging surveys. We train on images of real strong lenses and deploy on a sample of 8 million galaxies in areas with full coverage in the g, r, and i filters—the first multiband search for strong gravitational lenses in UNIONS. Following human inspection and grading, we report the discovery of a total of 1346 new strong-lens candidates, of which 146 are Grade A, 199 are Grade B, and 1001 are Grade C. Of these candidates, 283 have lens galaxy spectroscopic redshifts from the Sloan Digital Sky Survey, and an additional 297 have them from the Dark Energy Spectroscopic Instrument Data Release 1. We find 15 of these systems display evidence of both lens and source galaxy redshifts in spectral superposition. We also report the spectroscopic confirmation of seven lensed sources in high-quality systems, all with z > 2.1, using the Keck Near Infrared Echellette Spectrograph and the Gemini Near-Infrared Spectrograph.

Storfer, Christopher J. [University of Hawaii, Hon↗

DOE OSTI2021

Two-Dimensional Copper Iodide-Based Inorganic–Organic Hybrid Semiconductors: Synthesis, Structures, and Optical and Transport Properties

A group of copper iodide-based hybrid semiconductors with the general formula of 2D-CuI(L) 0.5 (L = organic ligands) are synthesized and structurally characterized. All compounds are two-dimensional (2D) networks made of one-dimensional (1D) copper iodide staircase chains that are interconnected by bidentate nitrogen-containing ligands. Results from optical absorption and emission experiments and density functional theory (DFT) calculations reveal that their photoluminescence (PL) can be systematically tuned by adjusting the lowest unoccupied molecular orbital (LUMO) energies of the organic ligands. Charge carrier transport measurements were carried out for the first time on single crystals of selected 2D-CuI(L) 0.5 structures, and the results show that they possess p-type conductivity with a Hall mobility of ~1 cm 2 V -1 s -1 for 2D-CuI(pm) 0.5 and 0.13 cm 2 V -1 s -1 for 2D-CuI(pz) 0.5 , respectively. These values are comparable to or higher than the mobilities of typical highly luminescent organic semiconductors. Finally, this work suggests that robust, high-dimensional copper iodide hybrid semiconductors are promising candidates to be considered as a new type of emissive layer for light-emitting diode (LED) devices.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2024

Nonlinear encoding in diffractive information processing using linear optical materials

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

42 ENGINEERING↗

DOE OSTI2025

Atomic Structure, Dynamics, Changes in Chemical Bonding and Semiconductor-Metal Transition in Sb 2 Se 3 : A Remarkable Material for Quantum Networks and Energy Applications

Antimony sesquiselenide has become an outstanding functional material for photovoltaics, energy storage and transformation, memory and photonic applications. Sb 2 Se 3 is one of the most successful emerging solar light absorbers and has also been identified as a highly promising ultralow-loss phase-change material (PCM) for next-generation coherent nanophotonic processors, photonic tensor cores, quantum and neuromorphic networks. Unlike benchmark telluride PCMs, Sb 2 Se 3 features a quasi-one-dimensional (1D) crystalline structure consisting of (Sb 4 Se 6 ) ∞ ribbons, lacks the typical PCM chemical bonding, and undergoes an extended semiconductor-metal transition above the melting point. Consequently, the origin of high optical contrast between crystalline (SET) and amorphous (RESET) logic states remains elusive and presents a significant challenge. Using high-energy X-ray diffraction and Raman spectroscopy over a wide temperature range, supported by first-principles simulations and complemented by thermal, optical and electrical measurements, as well as by 121 Sb-Mossbauer spectroscopy, the quasi-1D network of orthorhombic antimony sesquiselenide was found to undergo significant evolution in amorphous and supercooled Sb 2 Se 3 , leading to lower coordination, shorter interatomic distances and a higher p-electron density on antimony, indicating changes in chemical bonding. The observed novel Sb 2 Se 3 nanocrystalline polymorph, characterized by trigonal antimony coordination and more isolated Sb-Se ribbons, could help reduce multiple trapping defect states in the bandgap, which are typical of orthorhombic Sb 2 Se 3 , thereby enhancing the power-conversion efficiency of photovoltaic devices. Semimetallic and metallic liquid Sb 2 Se 3 exhibit a gradual transformation into a denser 2D and/or 3D network with higher antimony coordination. Localized electron states in the pseudogap are becoming extended, leading to an increase in electronic conductivity σ following the relationship σ ∝ N(E F ) 2 . Liquid Sb 2 Se 3 also appears to be strongly fragile, with a nonmonotonic change in viscosity and higher atomic mobility in the metallic liquid. Furthermore, these results explain extraordinary functionalities of Sb 2 Se 3 for photonic and energy applications.

antimony↗

DOE OSTI2023

Correlating structural changes in thermoresponsive hydrogels to the optical response of embedded plasmonic nanoparticles

Stimuli-responsive microgels, composed of small beads with soft, deformable polymer networks swollen through a combination of synthetic control over the polymer and its interaction with water, form a versatile platform for development of multifunctional and biocompatible sensors. The interfacial structural variation of such materials at a nanometer length scale is essential to their function, but not yet fully comprehended. Here, we take advantage of the plasmonic response of a gold nanorod embedded in a thermoresponsive microgel (AuNR@PNIPMAm) to monitor structural changes in the hydrogel directly near the nanorod surface. By direct comparison of the plasmon response against measurements of the hydrogel structure from dynamic light scattering and nuclear magnetic resonance, we find that the microgel shell of batch-polymerized AuNR@PNIPMAm exhibits a heterogeneous volume phase transition reflected by different onset temperatures for changes in the hydrodyanmic radius (R H ) and plasmon resonance, respectively. The new approach of contrasting plasmonic response (a measure of local surface hydrogel structure) with R H and relaxation times paves a new path to gain valuable insight for the design of plasmonic sensors based on stimuli-responsive hydrogels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Improved heavy-ion PID using scintillation light detector with neural network analysis: a Monte Carlo simulation study

The photon collection efficiency of gaseous scintillator detectors varies according to the position of the impinging charged particles in the medium that generates scintillation light. Thus, when impinging particles are distributed over a large area, the intrinsic photon-number resolution of the system is affected by a large variation. This work presents and discusses a method for adjusting the total number of detected photons to account for variation in the photon collection efficiency as a function of the position of the light source within the scintillating medium. The method was developed and validated by processing data from systematic simulation studies based on GEANT4 that model the response of the Energy Loss Optical Scintillation System (ELOSS) detector. The position of the charged particle is calculated using a deep neural network algorithm. This is accomplished by analyzing the distribution of scintillation light recorded by the array of photosensors. The estimated particle position is then used to calculate the correction factor and adjust the amount of captured light to account for variations in the photon collection efficiency. The neural network algorithm provides excellent tracking capabilities, achieving sub-millimeter position resolution and an angular resolution of 12 mrad, approaching the performance of traditional tracking detectors (e.g., drift chambers). The present method can be generalized to any optical scintillation system where the photon collection efficiency depends on the position of the impinging particle.

Heavy-ion detectors↗

DOE OSTI2020

Plasmonic gold-cellulose nanofiber aerogels

Assembly of plasmonic nanomaterials into a low refractive index medium, such as an aerogel, holds a great promise for optical metamaterials, optical sensors, and photothermal energy converters. However, conventional plasmonic aerogels are opaque and optically isotropic composites, impeding them from being used as low-loss or polarization-dependent optical materials. Here we demonstrate a plasmonic-cellulose nanofiber composite aerogel that comprises of well-dispersed gold nanorods within a cellulose nanofiber network. The cellulose aerogel host is highly transparent owing to the small scattering cross-section of the nanofibers and forms a nematic liquid crystalline medium with strong optical birefringence. We find that the longitudinal surface plasmon resonance peak of gold nanorods shows a dramatic shift when probed for the cellulose aerogel compared with the wet gels. Simulations reveal the shift of surface plasmon resonance peak with gel drying can be attributed to the change of the effective refractive index of the gels. This composite material may provide a platform for three- dimensional plasmonic devices ranging from optical sensors to metamaterials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Two–Photon Printing of Shape–Memory Microstructures and Metasurfaces via Radical–Mediated Thiol–Vinyl Hydrothiolation

Shape-memory resists capable of high-resolution curing into arbitrarily designed structures are increasingly demanded for soft robotics, optical sensors, microscale manufacturing, and biomedicine. Amorphous, shape-memory thiol-vinyl networks were printed using two-photon polymerization (2PP) curing of a simple resin formulated with commercially available reagents. The ability to print high-resolution feature sizes down to 200 nm is attributed to the use of radicalmediated, thiol-vinyl step-growth polymerization that quickly crosslinks the resin, limiting diffusive transport. The thermomechanical behavior of the 2PP-cured material analyzed in compression, tension and three-point bending is similar to the behavior of the UV-polymerized samples. To demonstrate the ability to design, field, and test 4D responsive microstructures, an array of nine springs with coil diameters of 330 μm was printed. Following compressive shapefixing, printed arrays can release 11 μJ of stored elastic strain energy when reheated. Further, a new concept of dichroic-memory of a metamaterial device is demonstrated by printing a twisted woodpile structure with circular dichroism as characterized by Mueller Matrix ellipsometry. Furthermore, our results demonstrate how combining high-resolution 2PP curing with stimuli-responsive molecular architectures can further the engineering of responsive microstructures and metamaterials.

36 MATERIALS SCIENCE↗