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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

In this paper, we developed a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) simulations for bubbly flows. The proposed approach is supported by high-resolution two-phase flow measurements, including those by double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distributions of key physical quantities of interest (QoIs), including the void fraction and phasic velocities, are obtained to support the Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified while the aleatory uncertainties from stochastic fluctuations of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of the QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of an open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainties of MCFD simulations can be significantly reduced. The proposed approach has the potential for other applications that involve numerical models with empirical parameters.

42 ENGINEERING↗
DOE OSTI2024

Direct Air Capture of CO 2 via Reactive Crystallization

Atmospheric CO 2 removal using engineered chemical processes, aka direct air capture (DAC), has become an essential component of our available portfolio for mitigating climate change. Here we describe a promising approach to DAC based on reactive crystallization of atmospheric CO 2 (RC-DAC) with aqueous guanidine and amino acid. Compared to the previously studied phase-changing DAC processes involving initial CO 2 absorption by an aqueous alkaline solvent followed by carbonate crystallization in a second step, RC-DAC combines the CO 2 absorption and carbonate crystallization into a single step. Thus, as the insoluble carbonate salts are removed from solution by crystallization, more CO 2 is pulled from the air into solution, further driving the DAC process. The RC-DAC was performed in a household humidifier as the air–liquid contactor, which can handle solid–liquid slurries effectively. The crystallization was monitored in situ by pH measurements, real-time imaging with a microscope probe, and by Raman spectroscopy, and ex situ by NMR spectroscopy, powder X-ray diffraction, and total inorganic carbonate analysis. Further, the investigation provided a detailed mechanistic picture of the RC-DAC process, involving formation of carbamate and carbonate anions in solution, followed by sequential crystallization of different guanidinium carbonate phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Highly Bright and Photostable Two-Dimensional Nanomaterials Assembled from Sequence-Defined Peptoids

Free-standing two-dimensional (2D) organic nanomaterials are highly attractive for biological applications because of their unique structural properties and high biocompatibility. Herein, we designed and synthesized a new class of highly bright and photostable membrane-mimetic 2D nanosheets from sequence-defined peptoids. These nanosheets exhibited the high quantum yield and photostability as a result of the precise placement and ordering of dansyl dye molecules within crystalline nanosheets. We further showed the use of these nanosheets as biocompatible and programmable probes for live cell imaging and cell labeling. Furthermore, by programing these nanosheets with different surface charges, we achieved the enhanced lysosome escape of these nanosheets, showing their great potential as nanocarriers for the efficient intracellular delivery of macromolecular drugs.

2D nanomaterials↗
DOE OSTI2020

Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom

The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. In this work, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Crystal structure of a far-red–sensing cyanobacteriochrome reveals an atypical bilin conformation and spectral tuning mechanism

Significance Phytochromes are well-known far-red-light sensors found in plants that trigger adaptive responses to facilitate competition for light capture with neighboring plants. Red- and far-red sensing are also critical for cyanobacteria living in the far-red–enriched shade of plants. This work reports the crystal structure of a far-red–sensing cyanobacteriochrome, a distant cyanobacterial relative of phytochrome. These studies shed insights into the molecular basis of far-red-sensing by phycobilin-based photoreceptors. Owing to the deep tissue penetration of far-red light, far-red–sensing cyanobacteriochromes are promising protein scaffolds for developing genetically encoded photoswitches, optoacoustic contrast agents, and fluorescent probes for in situ imaging and optogenetic applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Strongly intermittent far scrape-off layer fluctuations in Alcator C-Mod plasmas close to the empirical discharge density limit

Abstract Intermittent plasma fluctuations in the boundary region of the Alcator C-Mod device were comprehensively investigated using data time-series from gas puff imaging and mirror Langmuir probe diagnostics. Fluctuations were sampled during stationary plasma conditions in ohmically heated, lower single null diverted configurations with scans in both line-averaged density and plasma current, with Greenwald density fractions up to 0.85. Utilizing a stochastic model, we describe the plasma fluctuations as a super-position of uncorrelated pulses, with large-amplitude events corresponding to blob-like filaments moving through the scrape-off layer (SOL). A deconvolution method is used to estimate the pulse arrival times and amplitudes. The analysis reveals a significant increase of pulse amplitudes and waiting times as the line-averaged density approaches the empirical discharge density limit. Broadened and flattened average radial profiles are thus accompanied by strongly intermittent and large-amplitude fluctuations. Although these filaments are arriving less frequently at high line-averaged densities, we show that there are significant increases in radial far-SOL particle and heat fluxes which will further enhance plasma–wall interactions. The stochastic model has been used as a framework for study of the scalings in the intermittency parameter, flux and mean amplitude and waiting times, and is being used to inform predictive capability for the effects of filamentary transport as a function of Greenwald fraction.

Physics↗
DOE OSTI2024

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Unusual Breathing Behavior of Optically Excited Barium Titanate Nanocrystals

Coherent X-ray diffraction patterns were recorded by using an X-ray free-electron laser to illuminate barium titanate nanocrystals as a function of time delay after laser excitation. Rather than seeing any significant thermal expansion effects, the diffraction peaks were found to move perpendicular to the momentum transfer direction. This suggests a laser driven rotation of the crystal lattice, which is delayed by the aggregated state of the crystals. Internal deformations associated with crystal contacts were also observed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Effective Polarizability in Near-Field Microscopy of Phonon-Polariton Resonances

We investigate the resonant characteristics of planar surfaces and distinct edges of structures with the excitation of phonon-polaritons. We analyze two materials supporting phonon-polariton excitations in the mid-infrared spectrum: silicon carbide, characterized by an almost isotropic dielectric constant, and hexagonal boron nitride, notable for its pronounced anisotropy in a spectral region exhibiting hyperbolic dispersion. We formulate a theoretical framework that accurately captures the excitations of the structure involving phonon-polaritons, predicts the response in scattering-type near-field optical microscopy, and is effective for complex resonant geometries where the locations of hot spots are uncertain. We account for the tapping motion of the probe, perform analysis for different heights of the probe, and demodulate the signal using a fast Fourier transform. Using this Fourier demodulation analysis, we show that light enhancement across the entire apex is the most accurate characteristic for describing the response of all resonant excitations and hot spots. We demonstrate that computing the demodulation orders of light enhancement in the microscope probe accurately predicts its imaging.

Chemistry↗
DOE OSTI2022

The Compact Structures of Massive z ~ 0.7 Post-starburst Galaxies in the ${\bf{SQuIGG}}\vec{{\boldsymbol{L}}}{\bf{E}}$ Sample

We present structural measurements of 145 spectroscopically selected intermediate-redshift (z ~ 0.7), massive (M $\star$ ~ 10 11 M ⊙ ) post-starburst galaxies from the ${\bf{SQuIGG}}\vec{{\boldsymbol{L}}}{\bf{E}}$ sample measured using wide-depth Hyper Suprime-Cam i-band imaging. This deep imaging allows us to probe the sizes and structures of these galaxies, which we compare to a control sample of star-forming and quiescent galaxies drawn from the LEGA-C Survey. We find that post-starburst galaxies systematically lie ~0.1 dex below the quiescent mass–size (half-light radius) relation, with a scatter of ~0.2 dex. This finding is bolstered by nonparametric measures, such as the Gini coefficient and the concentration, which also reveal these galaxies to have more compact light profiles than both quiescent and star-forming populations at similar mass and redshift. The sizes of post-starburst galaxies show either negative or no correlation with the time since quenching, such that more recently quenched galaxies are larger or similarly sized. This empirical finding disfavors the formation of post-starburst galaxies via a purely central burst of star formation that simultaneously shrinks the galaxy and shuts off star formation. We show that the central densities of post-starburst and quiescent galaxies at this epoch are very similar, in contrast with their effective radii. The structural properties of z ~ 0.7 post-starburst galaxies match those of quiescent galaxies that formed in the early universe, suggesting that rapid quenching in the present epoch is driven by a similar mechanism to the one at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Early Results from GLASS-JWST. V: The First Rest-frame Optical Size–Luminosity Relation of Galaxies at z > 7

We present the first rest-frame optical size–luminosity relation of galaxies at z > 7, using the NIRCam imaging data obtained by the GLASS James Webb Space Telescope Early Release Science (GLASS-JWST-ERS) program, providing the deepest extragalactic data of the ERS campaign. Our sample consists of 19 photometrically selected bright galaxies with m F444W ≤ 27.8 at 7 < z < 9 and m F444W < 28.2 at z ~ 9–15. We measure the size of the galaxies in five bands, from rest-frame optical (~4800 Å) to the UV (~1600 Å) based on the Sérsic model, and analyse the size–luminosity relation as a function of wavelength. Remarkably, the data quality of the NIRCam imaging is sufficient to probe the half-light radius r e down to ~100 pc at z > 7. Given the limited sample size and magnitude range, we first fix the slope to that observed for larger samples in rest-frame UV using Hubble Space Telescope samples. The median size r 0 at the reference luminosity M = –21 decreases slightly from rest-frame optical (600 ± 80 pc) to UV (450 ± 130 pc). We then refit the size–luminosity relation allowing the slope to vary. The slope is consistent with β ~ 0.2 for all bands except F150W, where we find a marginally steeper slope of β = 0.53 ± 0.15. The steep UV slope is mainly driven by the smallest and faintest galaxies. If confirmed by larger samples, it implies that the UV size–luminosity relation breaks toward the faint end, as suggested by lensing studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Nanometer-scale electrical potential imaging on absorber of CdSeTe solar cells

Here, we report on nm-scale electrical potential imaging throughout As-doped and Cu-doped CdSeTe absorbers using Kelvin probe force microscopy (KPFM). The potential imaging was conducted both laterally and vertically on beveled films using ion milling at small glancing angle. KPFM images electrical potential on the beveled surface and assesses defect charging in the subsurface region within a screening length from the beveled surface. We found that the grain boundaries were positively charged and that there were significant potential fluctuations in both grain boundary versus grain interior and intragrain. We further found that these potential fluctuations decreased significantly toward the front interface. Time of flight secondary ion mass spectrometry imaging shows that Se content increased toward the front interface, consistent with Se passivation of defects. The potential fluctuation was induced by defect charging, and the results elucidate different details of the defect configurations and grain structures of the films with different CdCl 2 treatment temperatures in the As-doped CdSeTe. The defect configurations in the region near the front interface can be a main factor contributing to the device performance difference. Our potential imaging provides insights about the defects throughout the absorber films, and shows that the potential fluctuation has a direct correlation to the V oc deficit.

14 SOLAR ENERGY↗
DOE OSTI2019

Introducing the Planar Laser-Induced Fluorescence Technique (PLIF) to Measure Mixing Time in Gas-Stirred Ladles

The planar laser-induced fluorescence (PLIF) technique was implemented to measure mixing time in a 1/17 water model of a 200-ton ladle furnace. The results were compared to those obtained using the conventional method of pH probes. PLIF determinations were done at two different planes, and pH probe determinations were performed at two different locations. The results suggest that mixing times measured by PLIF are similar to those obtained under optimal conditions by the pH probe and that PLIF technique is more accurate and less sensitive to the location of the measurement than the pH probe method. In addition, the particle image velocimetry (PIV) technique was used to measure the effect of the immersed probe on the fluid-dynamic structure of the system. The presence of the probe affects greatly fluid dynamics and consequently the mixing behavior, which could explain the differences found in its mixing time measurements at different probe locations. This study shows the feasibility of the PLIF technique used to measure mixing time in physical models of gas-stirred ladles; it is not intrusive and allows the visualization of the mixing phenomena in a complete plane of the system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Autonomous Molecular Structure Imaging with High-Resolution Atomic Force Microscopy for Molecular Mixture Discovery

Due to its single-molecule sensitivity, high-resolution atomic force microscopy (HR-AFM) has proved to be a valuable and uniquely advantageous tool to study complex molecular mixtures, which hold promise for developing clean energy and achieving environmental sustainability. However, significant challenges remain to achieve the full potential of the sophisticated and time-consuming experiments. Automation combined with machine learning (ML) and artificial intelligence (AI) is key to overcoming these challenges. Here we present Auto-HR-AFM, an AI tool to automatically collect HR-AFM images of petroleum-based mixtures. In this study, we trained an instance segmentation model to teach Auto-HR-AFM how to recognize features in HR-AFM images. Auto-HR-AFM then uses that information to optimize the imaging by adjusting the probe-molecule distance for each molecule in the run. Auto-HR-AFM is the initial tool that will lead to fully automated scanning probe microscopy (SPM) experiments, from start to finish. This automation will allow SPM to become a mainstream characterization technique for complex mixtures, an otherwise unattainable target.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hyperspectral imaging of exciton confinement within a moiré unit cell with a subnanometer electron probe

Electronic and optical excitations in two-dimensional systems are distinctly sensitive to the presence of a moiré superlattice. Here we used cryogenic transmission electron microscopy and spectroscopy to simultaneously image the structural reconstruction and associated localization of the lowest-energy intralayer exciton in a rotationally aligned WS 2 -WSe 2 moiré superlattice. In conjunction with optical spectroscopy and ab initio calculations, we determined that the exciton center-of-mass wave function is confined to a radius of approximately 2 nanometers around the highest-energy stacking site in the moiré unit cell. Our results provide direct evidence that atomic reconstructions lead to the strongly confining moiré potentials and that engineering strain at the nanoscale will enable new types of excitonic lattices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-Energy Differential Evolution Reconstruction (MEnDER 1D) for Proton Deflectometry

This code is designed to reconstruct magnetic field deflections, and thus the path-integrated magnetic field, from sets of proton images at two distinct proton probe energies. A differential evolution (DE) algorithm is used to iteratively update a population of solution candidates of the magnetic deflections of the protons for reconstructing the input images, selecting improved candidates as they are discovered. This algorithm was written using MATLAB (R2019a) and makes use of the Image Processing and Parallel Processing Toolboxes.

Levesque, Joseph↗
DOE OSTI2025

Direct Observations of Solute Dispersion in Rocks With Distinct Degree of Sub‐Micron Porosity

Abstract The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X‐ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four‐dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore‐scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub‐micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi‐dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one‐dimensional transport models have limited predictive ability toward the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The data set generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Kurotori, Takeshi [Department of Chemical Engineer↗