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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Drone flight visible images, digital elevation maps, and geotiffs, Teller, Seward Peninsula, Alaska, USA, July 2017

Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along the flight path. This package includes data from a flight path over the NGEE Arctic Teller Site, west of mile marker 27 Bob Blodgett Highway in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Drone flight visible images, digital elevation maps, and geotiffs, Kougarok, Seward Peninsula, Alaska, USA, July 2017

Remote sensing data collected from Lawrence Berkeley National Laboratory’s (LBNL) unmanned aerial system (UAS) quadcopter platform – Inspire-1 – operated by the Ameriflux Management Project (ameriflux.lbl.gov) Technical Team. The Insprire-1 is a visible imagery UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) at regular intervals along each flight path. This package includes data from four flight paths over the NGEE Arctic Kougarok Site, west of mile marker 64 on Kuzitrin Road in the Seward Peninsula, Alaska in July 2017. Derived image products include ortho-mosaiced RGB, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, and a digital terrain model (DTM). Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. Unprocessed and processed data products are included in this package (processing levels 0-2). This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Dark Energy Survey Year 6 Results: Redshift Calibration of the Weak Lensing Source Galaxies

Determining the distribution of redshifts for galaxies in wide-field photometric surveys is essential for robust cosmological studies of weak gravitational lensing. We present the methodology, calibrated redshift distributions, and uncertainties of the final Dark Energy Survey Year 6 (Y6) weak lensing galaxy data, divided into four redshift bins centered at $\langle z \rangle = [0.414, 0.538, 0.846, 1.157]$. We combine independent information from two methods on the full shape of redshift distributions: optical and near-infrared photometry within an improved Self-Organizing Map $p(z)$ (SOMPZ) framework, and cross-correlations with spectroscopic galaxy clustering measurements (WZ), which we demonstrate to be consistent both in terms of the redshift calibration itself and in terms of resulting cosmological constraints within 0.1$σ$. We describe the process used to produce an ensemble of redshift distributions that account for several known sources of uncertainty. Among these, imperfection in the calibration sample due to the lack of faint, representative spectra is the dominant factor. The final uncertainty on mean redshift in each bin is $σ_{\langle z\rangle} = [0.012, 0.008,0.009, 0.024]$. We ensure the robustness of the redshift distributions by leveraging new image simulations and a cross-check with galaxy shape information via the shear ratio (SR) method.

Yin, B. [Duke U.] (ORCID:0009000656049980)↗
DOE OSTI2020

Correlation approach for quality assurance of additive manufactured parts based on optical metrology

Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Proton Transfer Dynamics in the Aprotic Proton Accepting Solvent 1-Methylimidazole

The dynamics of proton transfer to the aprotic solvent 1-methylimidazole (MeIm, proton acceptor) from the photoacid 8-hydroxypyrene-1,3,6-trisulfonic acid trisodium salt (HPTS) were investigated using fast fluorescence measurements. The closely related molecule, 8- methoxypyrene-1,3,6-trisulfonic acid trisodium salt (MPTS), which is not a photoacid, was also studied for comparison. Following optical excitation, the wavelength-dependent population dynamics of HPTS in MeIm resulting from the deprotonation process were collected over the entire fluorescence emission window. Analysis of the time-dependent fluorescence spectra revealed four distinct fluorescence bands that appear and decay on different time scales. We label these four states as: protonated (P), associated I (A I ), associated II (A II ), and deprotonated (D). We find that the simple kinetic scheme of P → A I → A II → D is not consistent with the data. Instead, the kinetic scheme that describes the data has P decaying into A I , which mainly goes on to deprotonation (D), but AI can also feed into A II . A II can return to A I or decay to the ground state, but does not deprotonate within experimental error. Finally, quantum chemistry and excitedstate QM/MM Born-Oppenheimer molecular dynamics simulations indicate that A I and A II are two H-bonding conformations of MeIm to the HPTS hydroxyl, axial and equatorial, respectively.

14 SOLAR ENERGY↗
DOE OSTI2024

Low‐Temperature Synthesis of Stable CaZn 2 P 2 Zintl Phosphide Thin Films as Candidate Top Absorbers

Abstract The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn 2 P 2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn 2 P 2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn 2 P 2 films exhibit an optical absorptivity of ≈10 4 cm −1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn 2 P 2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories

High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Searching for multimessenger transients and lensed supernovae with the dark energy survey

Advances in data collection technology for optical astronomy, such as the Dark Energy Camera, have facilitated the collection of some of the largest astronomical datasets in terms of sky area, depth, number of objects, number of observations, and frequency of observations. Consequentially, physical systems that have been historically rare become represented in small, but discoverable, numbers. I have developed techniques to identify rare physical systems (counterparts to high-energy neutrinos, counterparts to gravitational waves, and gravitationally lensed supernovae) that can shed new light on the physical processes that govern the Universe. For each system of interest, I collect new or search archival observations with the Dark Energy Camera, develop selection methodologies to find the system of interest in the observations, and quantify the efficiency of the search using simulations. Each of the three analyses has its own scientific implications: I constrain the observing program required to determine the contribution of supernovae to the diffuse high-energy neutrino flux; I set constraints on the physical properties of an optical counterpart to a compact object merger; and I identify candidate lensed supernovae using a novel machine learning technique. Looking forward, the work presented in this dissertation informs future rare object searches and facilitates measurements of the underlying properties of the Universe during the next era of optical survey astronomy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Remote quantification of Cm(III) and HNO 3 by fluorescence spectroscopy and chemometrics

A unique approach to remotely quantify Cm(III) (0–100 µg mL −1 ) in HNO 3 (1–12 M) using steady-state laser fluorescence spectroscopy and multivariate regression models was developed. Photoluminescence is amenable to remote measurements using fiber-optic cables and is sensitive to numerous lanthanide and actinide species. In-line measurements can provide feedback to support complex processing in harsh environments (e.g., hot cells) to help guide and optimize radiochemical separations. In this work, Cm(III) spectra were acquired remotely in a glove box as a function of HNO 3 concentration to better understand spectral characteristics and evaluate the utility of multivariate regression models in this system. Furthermore, the Cm(III) fluorescence peak shape, width, position, and intensity changed significantly as a function of HNO 3 concentration, likely because of the displacement of emission quenching inner-sphere water molecules and complexation with nitrate ions. Despite significant covariance and nonlinearity in the data, a D-optimal design strategy successfully minimized training set sample size and was used to build effective partial least squares regression models for Cm(III) and HNO 3 concentrations without a priori knowledge of solution conditions. Chemometrics for modeling complex fluorescence spectra are promising and may find widespread applicability for online analysis in numerous chemical systems found in the nuclear field.

Actinide↗
DOE OSTI2025

Distributed Fiber Optic Sensing for in-well hydraulic fracture monitoring

This study presents the results from in-well hydraulic fracture monitoring within a horizontal well in an unconventional reservoir utilizing Distributed Fiber Optic Sensing (DFOS). An in-house-developed Brillouin-based Distributed Strain Sensing (DSS) interrogator was deployed to obtain strain measurements, complemented by a commercial Raman-based Distributed Temperature Sensing (DTS) interrogator for temperature measurements and a commercial Rayleigh-based Low-Frequency Distributed Acoustic Sensing (LF-DAS) interrogator for strain-rate measurements. Examined over a ten-day period, the spatio-temporal distribution of temperature-compensated strain obtained from DSS and DTS revealed distinct signatures of the multi-stage hydraulic fracturing process. These signatures were analyzed with respect to fracture width growth and closure, residual strain effects, and fracture conductivity near the wellbore. Fracture widths within the fracture zone were estimated for individual stages. The findings were assessed with LF-DAS measurements for further evaluation. This work integrates DFOS-measured strain, temperature, and strain-rate data for monitoring in-well hydraulic fracturing, with the goal of supporting future studies in interpreting DFOS measurements for improved understanding of hydraulic fracturing in unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2020

Role of shell composition and morphology in achieving single-emitter photostability for green-emitting “giant” quantum dots

The use of the varied chemical reactivity of precursors to drive the production of a desired nanocrystal architecture has become a common method to grow thick-shell graded alloy quantum dots (QDs) with robust optical properties. Conclusions on their behavior assume the ideal chemical gradation and uniform particle composition. In this work, advanced analytical electron microscopy (high-resolution scanning transmission electron microscopy coupled with energy dispersive spectroscopy) is used to confirm the nature and extent of compositional gradation and these data are compared with performance behavior obtained from single-nanocrystal spectroscopy to elucidate structure, chemical-composition, and optical-property correlations. In particular, the evolution of the chemical structure and single-nanocrystal luminescence was determined for a time-series of graded-alloy “CdZnSSe/ZnS” core/shell QDs prepared in a single-pot reaction. In a separate step, thick (~6 monolayers) to giant (>14 monolayers) shells of ZnS were added to the alloyed QDs via a successive ionic layer adsorption and reaction (SILAR) process, and the impact of this shell on the optical performance was also assessed. By determining the degree of alloying for each component element on a per-particle basis, we observe that the actual product from the single-pot reaction is less “graded” in Cd and more so in Se than anticipated, with Se extending throughout the structure. The latter suggests much slower Se reaction kinetics than expected or an ability of Se to diffuse away from the initially nucleated core. It was also found that the subsequent growth of thick phase-pure ZnS shells by the SILAR method was required to significantly reduce blinking and photobleaching. However, correlated single-nanocrystal optical characterization and electron microscopy further revealed that these beneficial properties are only achieved if the thick ZnS shell is complete and without large lattice discontinuities. Thus, we identify the necessary structural design features that are required for ideal light emission properties in these green-visible emitting QDs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep inverse photonic design: A tutorial

Deep learning (DL) has been used to design deep neural networks (DNNs) which have recently been applied to solving inverse problems in artificial electromagnetic materials (AEMs). Although inverse problems are often ill-posed, and therefore are difficult to solve, deep inverse models (DIMs) have achieved impressive results often surpassing capabilities possible with other approaches. We overview the process of deep inverse learning applied to AEM problems, including the building of data sets, design of a forward model, and comparison of inverse approaches including limitations. We conclude by detailing some important outstanding issues of deep inverse design of AEMs, and present an outlook of this exciting field.

42 ENGINEERING↗
DOE OSTI2021

Nitride-Oxide-Metal Heterostructure with Self-Assembled Core–Shell Nanopillar Arrays: Effect of Ordering on Magneto-Optical Properties

Magneto-optical (MO) coupling incorporates photon-induced change of magnetic polarization that can be adopted in ultrafast switching, optical isolators, mode convertors, and optical data storage components for advanced optical integrated circuits. However, integrating plasmonic, magnetic, and dielectric properties in one single material system poses challenges since one natural material can hardly possess all these functionalities. Here, co-deposition of a three-phase heterostructure composed of a durable conductive nitride matrix with embedded core–shell vertically aligned nanopillars, is demonstrated. Additionally, the unique coupling between ferromagnetic NiO core and atomically sharp plasmonic Au shell enables strong MO activity out-of-plane at room temperature. Further, a template growth process is applied, which significantly enhances the ordering of the nanopillar array. The ordered nanostructure offers two schemes of spin polarization which result in stronger antisymmetry of Kerr rotation. The presented complex hybrid metamaterial platform with strong magnetic and optical anisotropies is promising for tunable and modulated all-optical-based nanodevices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chemical and Optical Control of Spin Crossover: Ultrafast XUV Spectroscopy of Molecular Magnets in Native Solvation Environments

With support from the US Department of Energy Office of Science, we have developed and utilized extreme ultraviolet (XUV) spectroscopy and sum frequency generation vibrational spectroscopy as probes of charge, spin, and solvation structure and dynamics in molecules and at interfaces. This work is crucial to advancing fundamental understanding of the processes that control the efficiency and speed of energy conversion and information processing in molecules and at interfaces. Accordingly, it has important applications for developing next generation technologies for information storage and processing with increasing data storage density and processing rates as well as developing new methods for efficient energy conversion and storage.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

High-sensitivity electronic Stark spectrometer featuring a laser-driven light source

We report developmental details of a high-sensitivity Stark absorption spectrometer featuring a laser-driven light source. The light source exhibits intensity fluctuations of ~0.3% over timescales ranging from 1 min to 12 h, minimal drift (≤0.1%/h), and very little 1/f noise at frequencies greater than 200 Hz, which are comparable to or better than an arc-driven light source. Additional features of the spectrometer include balanced detection with multiplex sampling, which yielded lower noise in A, and constant wavelength or wavenumber (energy) spectral bandpass modes. We achieve noise amplitudes of ~7 × 10 –4 and ~6 × 10 –6 in measurements of single A and ΔA spectra (with 92 data points) taking ~7 and ~19 min, respectively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Laminography as a tool for imaging large-size samples with high resolution

Despite the increased brilliance of the new generation synchrotron sources, there is still a challenge with high-resolution scanning of very thick and absorbing samples, such as a whole mouse brain stained with heavy elements, and, extending further, brains of primates. Samples are typically cut into smaller parts, to ensure a sufficient X-ray transmission, and scanned separately. Compared with the standard tomography setup where the sample would be cut into many pillars, the laminographic geometry operates with slab-shaped sections significantly reducing the number of sample parts to be prepared, the cutting damage and data stitching problems. In this work, a laminography pipeline for imaging large samples (>1 cm) at micrometre resolution is presented. The implementation includes a low-cost instrument setup installed at the 2-BM micro-CT beamline of the Advanced Photon Source. Additionally, sample mounting, scanning techniques, data stitching procedures, a fast reconstruction algorithm with low computational complexity, and accelerated reconstruction on multi-GPU systems for processing large-scale datasets are presented. The applicability of the whole laminography pipeline was demonstrated by imaging four sequential slabs throughout an entire mouse brain sample stained with osmium, in total generating approximately 12 TB of raw data for reconstruction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Deep learning-assisted modeling for χ (2) nonlinear optics

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present a long short-term memory-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems.

Accelerator Physics (physics.acc-ph)↗