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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Galvanostatic Intermittent Titration Technique Reinvented: Part I. A Critical Review

The galvanostatic intermittent titration technique (GITT), introduced in 1977 by Weppner and Huggins, provided a readily accessible means to measuring the chemical diffusion coefficient of electrochemical electrode materials. The method continues to be widely used today, but the reported diffusivity values are highly inconsistent, ranging as much as four orders of magnitude for some Li layered oxide compositions. Even qualitative trends of diffusivity are inconsistent, suggesting significant flaws in the implementation of the method. Other variants of the GITT method also suffer from similar inconsistency problems. Here we identify numerous sources of significant error including composition-dependent reaction overpotentials, mathematical flaws in the relaxation analysis methods, finite-size and non-planar geometry effects, inter-particle inhomogeneity issues, early transient effects, and surface area uncertainties. We propose a simple relaxation analysis scheme using the time variable t relax + τ - t relax , where t relax is relaxation time and τ is the galvanostatic pulse duration. We also propose to use dense diffusion-limited samples to isolate the bulk-diffusion process in the time domain. Chemical diffusivity can be extracted much more reliably with this improved implementation of the GITT method.

25 ENERGY STORAGE↗

DOE OSTI2021

Latent map Gaussian processes for mixed variable metamodeling

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. Here we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.

42 ENGINEERING↗

DOE OSTI2025

Review of High-Throughput Surface Treatments for Microlens Arrays

Microlenses are increasingly being integrated into modern manufactured devices. From printed security devices and screens to solar panels and microscopes, these optical materials offer high control over light focusing. Thus, understanding how to treat the surfaces of these fragile, transparent devices on an integrated manufacturing line is essential. Here, in this study, we review the surface treatments for the following application categories: cleaning, increasing surface energy, decreasing surface energy, and tunable surface modifications. This overview describes methods available for the large-scale manufacturing of microlens arrays and the potential impact of those treatments on common optical surfaces. Objectives and qualitative compatibility parameters are compared, and outlooks are provided for further study to aid in streamlining the method selection and process optimization for microlenses and similar optical components.

lens manufacturing↗

NASA NTRSDate not supplied

Particle Seeding Method for Small-Scale, High-Pressure Nozzles

NASA tests new launch and reentry vehicle configurations in wind tunnels, where flow visualizations and quantitative flowfield measurements are often desired. Some of these vehicles have rocket motors for propulsion, retro-propulsion, or reaction control. High-pressure air is used to supply these rocket motor plumes. However, it is difficult to make off-body measurements in these regions, for several reasons. First, the plumes themselves lack seeding particles for flow diagnostics, and at the low pressures after expansion, Rayleigh scattering or other molecular techniques yield insufficient signal for flow velocity measurements. Second, the plumes displace particle-seeded tunnel air, preventing measurements in the vicinity of the plume. Third, the plumes force shock waves ahead of the vehicle, which melts the ice crystal fog commonly used for visualization and measurement techniques in certain facilities. In the current work, a novel method for seeding the flow in these small-scale, high-pressure nozzles has been devised and initially demonstrated, potentially enabling quantitative and qualitative measurements with particle-based instruments such as Doppler global velocimetry or particle image velocimetry. The method involves a Venturi contraction to draw the seed liquid out of a reservoir and into the nozzle channel, wherein shearing forces atomize the seed into particles. The concept was tested with a laser sheet visualization, which demonstrated that the flow rate of liquid spray was controllable; a valve could be adjusted to drop the flow rate by up to 65%. This relatively inexpensive and simple technique may prove useful in wind tunnel experiments involving particle-based laser diagnostics and small, high-pressure nozzles.

Aditya S. Acharya↗

NASA NTRSDate not supplied

Particle Seeding Method for Small-Scale, High-Pressure Nozzles

lasers↗

DOE OSTI2024

What to expect when you're expecting engagement: Delivering procedural justice in large-scale solar energy deployment

Community engagement in the planning process to build large-scale solar (LSS) projects can win local support and advance procedural justice. However, an understanding of community engagement in current LSS development is lacking. Using responses from a U.S. nationwide survey (n = 979) of residential neighbors living within 3 miles (4.8 km) of completed LSS projects (i.e. “solar neighbors”) and project details from the U.S. Large-Scale Solar Photovoltaic Database (USPVDB), this study seeks to answer the following questions: How are solar neighbors' perceptions of community engagement associated with their attitudes toward their LSS projects? How do solar neighbors' perceptions of community engagement compare to their expectations? And, how do neighbors explain what they perceived about the planning process? We answer these questions using mixed methods, including regression modeling, a new gap analysis technique, and qualitative coding. We find that higher perceived engagement is associated with more positive attitudes toward the project, even when controlling for respondents who acted in opposition. Supporters and opponents alike expect more engagement than they perceived and information about projects both before construction and after operation is lacking. Awareness and engagement expectations increase at certain project size and proximity thresholds. However, most neighbors expect the public to offer input during engagement, but not make decisions. We contextualize these findings with explanatory comments from respondents.

14 SOLAR ENERGY↗

DOE OSTI2025

Online collection of LC effluent as droplets for automatic injection into ICP-MS

A sample introduction technique was developed to allow for online, dropwise injection of effluent into a mass spectrometer (MS). This allowed for the coupling of chromatography systems that were not driven by pumps without altering the set-up or separation performance. This capability combined the benefits of coupling separation and measurement while also enabling accurate chromatographic evaluation that would generally be performed offline. The incorporation of a flowing rinse into the system made dropwise resolution possible for any liquid capable of forming drops under ambient conditions. The technique was demonstrated utilizing gas pressurized extraction chromatography (GPEC) and inductively coupled plasma-time-of-flight mass spectrometry (ICP-TOFMS) to illustrate the qualitative applications for rapid separation development and procedure evaluation. This method's quantitative applications were evaluated using single and double isotope dilution mass spectrometry (IDMS) with an external mass bias correction to measure analytes across entire elutions including volumes as small as single drops. In conclusion, the total neodymium values afforded by both single and double IDMS were within the uncertainty of the calculated value for evaluation of full elution peaks and for most of the single drops analyzed that reached above 10k cps Nd-144.

GPEC↗

DOE OSTI2020

Modern Senicide in the Face of a Pandemic: An Examination of Public Discourse and Sentiment About Older Adults and COVID-19 Using Machine Learning

Objectives This study examined public discourse and sentiment regarding older adults and COVID-19 on social media and assessed the extent of ageism in public discourse. Methods Twitter data (N = 82,893) related to both older adults and COVID-19 and dated from January 23 to May 20, 2020, were analyzed. We used a combination of data science methods (including supervised machine learning, topic modeling, and sentiment analysis), qualitative thematic analysis, and conventional statistics. Results The most common category in the coded tweets was “personal opinions” (66.2%), followed by “informative” (24.7%), “jokes/ridicule” (4.8%), and “personal experiences” (4.3%). The daily average of ageist content was 18%, with the highest of 52.8% on March 11, 2020. Specifically, more than 1 in 10 (11.5%) tweets implied that the life of older adults is less valuable or downplayed the pandemic because it mostly harms older adults. A small proportion (4.6%) explicitly supported the idea of just isolating older adults. Almost three-quarters (72.9%) within “jokes/ridicule” targeted older adults, half of which were “death jokes.” Also, 14 themes were extracted, such as perceptions of lockdown and risk. A bivariate Granger causality test suggested that informative tweets regarding at-risk populations increased the prevalence of tweets that downplayed the pandemic. Discussion Ageist content in the context of COVID-19 was prevalent on Twitter. Information about COVID-19 on Twitter influenced public perceptions of risk and acceptable ways of controlling the pandemic. Finaly, public education on the risk of severe illness is needed to correct misperceptions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Manipulation and statistical analysis of the fluid flow of polymer semiconductor solutions during meniscus-guided coating

Recent work in structure–processing relationships of polymer semiconductors have demonstrated the versatility and control of thin-film microstructure offered by meniscusguided coating (MGC) techniques. Here, we analyze the qualitative and quantitative aspects of solution shearing, a model MGC method, using coating blades augmented with arrays of pillars. The pillars induce local regions of high strain rates—both shear and extensional—not otherwise possible with unmodified blades, and we use fluid mechanical simulations to model and study a variety of pillar spacings and densities. We then perform a statistical analysis of 130 simulation variables to find correlations with three dependent variables of interest: thin-film degree of crystallinity and transistor field-effect mobilities for charge-transport parallel (Μ para ) and perpendicular (Μ perp ) to the coating direction. Our study suggests that simple fluid mechanical models can reproduce substantive correlations between the induced fluid flow and important performance metrics, providing a methodology for optimizing blade design.

36 MATERIALS SCIENCE↗

NASA NTRS1974

A quantitative study of the orientation bias of some edge detector schemes

The evaluation of a particular set of edge detection schemes is described. The results obtained are compared with those obtained from an edge detection scheme using a texture oriented approach. The orientational bias of these schemes is emphasized. Improved qualitative observations are reported and a comparison of the evaluation method with another edge detection evaluation method is presented.

Fram, J. R.↗

NASA NTRS1981

Ultrashort pulse generation in semiconductor lasers

Techniques to generate picosecond optical pulses from semiconductor lasers are reviewed. Experimental methods and results of theoretical analysis of active modelocking are presented. It is shown that modelocking will achieve the shortest pulses; but the use of a cumbersome external cavity will probably limit its practical use. Short pulses produced by direct modulation such as gain switching are considerably broader than those obtained by passive modelocking. However, no external cavity is needed; and the simplicity of this method makes it important to be explored further. Recent experimental results are discussed where picosecond pulses from a buried heterostructure laser diode with ultrashort current pulses obtained from a comb generator are generated. Also, 28 ps pulses were obtained at 2.5 GHz repetition frequency, using the gain switching method. An analytical analysis based on the rate equations shows qualitative agreement with our experimental results.

Auyeung, J.↗

NASA NTRS1986

A simple, objective analysis scheme for scatterometer data

A simple economical objective analysis scheme is devised and tested on real scatterometer data. It is designed to treat dense data such as those of the Seasat A Satellite Scatterometer (SASS) for individual or multiple passes, and preserves subsynoptic scale features. Errors are evaluated with the aid of sampling ('bootstrap') statistical methods. In addition, sensitivity tests have been performed which establish qualitative confidence in calculated fields of divergence and vorticity. The SASS wind algorithm could be improved; however, the data at this point are limited by instrument errors rather than analysis errors. The analysis error is typically negligible in comparison with the instrument error, but amounts to 30 percent of the instrument error in areas of strong wind shear. The scheme is very economical, and thus suitable for large volumes of dense data such as SASS data.

Levy, G.↗

NASA NTRS1993

Surface pressure measurement by oxygen quenching of luminescence

Methods and compositions for measuring the pressure of an oxygen-containing gas on an aerodynamic surface, by oxygen-quenching of luminescence of molecular sensors is disclosed. Objects are coated with luminescent films containing a first sensor and at least one of two additional sensors, each of the sensors having luminescences that have different dependencies on temperature and oxygen pressure. Methods and compositions are also provided for improving pressure measurements (qualitative or quantitive) on surfaces coated with a film having one or more types of sensor.

Gouterman, Martin P.↗

NASA NTRS1994

Surface pressure measurement by oxygen quenching of luminescence

Gouterman, Martin P.↗

NASA NTRS2015

KSWAGS: A Swift X-Ray and UV Survey of the Kepler Field 1

We introduce the first phase of the Kepler-Swift Active Galaxies and Stars survey (KSwAGS), a simultaneous X-ray and UV survey of approximately 6 square degrees of the Kepler field using the Swift XRT and UVOT. We detect 93 unique X-ray sources with S/N greater or equal to 3 with the XRT, of which 60 have UV counterparts. We use the Kepler Input Catalog (KIC) to obtain the optical counterparts of these sources, and construct the fX / fV ratio as a first approximation of the classification of the source. The survey produces a mixture of stellar sources, extragalactic sources, and sources which we are not able to classify with certainty. We have obtained optical spectra for thirty of these targets, and are conducting an ongoing observing campaign to fully identify the sample. For sources classified as stellar or AGN with certainty, we construct SEDs using the 2MASS, UBV and GALEX data supplied for their optical counterparts by the KIC, and show that the SEDs differ qualitatively between the source types, and so can offer a method of classification in absence of a spectrum. Future papers in this series will analyze the timing properties of the stars and AGN in our sample separately. Our survey provides the first X-ray and UV data for a number of known variable stellar sources, as well as a large number of new X-ray detections in this well-studied portion of the sky. The KSwAGS survey is currently ongoing in the K2 ecliptic plane fields.

KSWAGS: A SWIFT X-RAY AND UV SURVEY OF THE KEPLER ↗

DOE OSTI2025

Customizable wave tailoring nonlinear materials enabled by bilevel inverse design

Abstract Passive wave transformation via nonlinearity is ubiquitous in settings from acoustics to optics and electromagnetics. It is well known that different nonlinearities yield different effects on propagating signals, which raises the question of “what precise nonlinearity is the best for a given wave tailoring application?” In this work, considering a one-dimensional spring-mass chain connected by polynomial springs (a variant of the Fermi-Pasta-Ulam-Tsingou system), we introduce a bilevel inverse design method which couples the shape optimization of structures for tailored constitutive responses with reduced-order nonlinear dynamical inverse design. We apply it to two qualitatively distinct problems—minimization of peak transmitted kinetic energy from impact, and pulse shape transformation—demonstrating our method’s breadth of applicability. For the impact problem, we obtain two fundamental insights. First, small differences in nonlinearity can drastically change the dynamic response of the system, from severely under- to outperforming a comparative linear system. Second, the oft-used strategy of impact mitigation via “energy locking” bistability can be significantly outperformed by our optimal nonlinearity. We validate this case with impact experiments and find excellent agreement. This study establishes a framework for broader passive nonlinear mechanical wave tailoring material design, with applications to computing, signal processing, shock mitigation, and autonomous materials.

Science & Technology - Other Topics↗

DOE OSTI2026

Methods development towards automated, physics-informed, quantitative quality control of TRISO-SiC

Tristructural-isotropic (TRISO) fuel particles have been developed as a high-performance fuel for use in high-temperature gas-cooled reactor (HTGR) systems due to their high efficiency and stability under both normal and off-normal conditions. Broader deployment of this technology in advanced nuclear applications may benefit from quantitative quality assurance and quality control (QA/QC) methods that directly link TRISO properties to downstream performance. A key TRISO property is the SiC layer microstructure, which influences fission product retention during irradiation. However, existing QA/QC for the TRISO-SiC microstructure comprises only a qualitative visual inspection; therefore, there is a clear opportunity for the development of quantitative methods for TRISO QA/QC. Here, to this end, previous work has demonstrated an image processing approach to grain boundary (GB) identification and subsequent extraction of microstructural metrics; however, extensive twinning within the SiC layer complicates such analyses because twin GBs significantly influence microstructural metrics but are not expected to contribute to fission product transport. This study presents the initial development, training, and testing of an ML-based image segmentation algorithm designed to identify and remove twin GBs from standard backscattered electron micrographs, providing an industrially applicable, quantitative, and physically meaningful QA/QC approach for the TRISO-SiC microstructure. Although pixel-wise performance metrics for the twin predictions are low, the change in grain area and the number of GB pixels after twin removal predicted by the ML workflow are within 1% of the true values calculated using crystallographic data. This suggests that the model is well capable of predicting overall twin boundary structures and grain morphology, and continued advancement of this approach could enable automated, scalable, and physics-informed QA/QC for TRISO-SiC microstructures, supporting the reliable qualification of coated particle fuels for next-generation reactor systems.

Computer vision↗