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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Automated detection of photovoltaic cleaning events: A performance comparison of techniques as applied to a broad set of labeled photovoltaic data sets

Extracting accurate soiling loss information from photovoltaic (PV) production data first requires segmenting the time series data per natural or manually occurring cleaning events. Maintenance logs are often incomplete, rain data are often unavailable, and the debate on rain thresholds for cleaning and dew or wind cleanings is still ongoing. The present work aims to overtake these issues by improving automated methods to detect these cleaning events and therefore improve extraction of soiling loss information. Time series power production data from 22 PV inverters were labeled for natural or manually occurring cleaning events. The data sets were carefully selected to include varying degrees of soiling, cleaning events, and noise. Several algorithms, including filtering logic and change point detection, were examined for efficacy at detecting the labeled cleanings. All the methods introduced except for changepoint detection showed significant improvement at detecting the labeled cleaning events per the mean F 1 score. Furthermore, the highest performing cleaning detection algorithm achieved an absolute increase in the mean F 1 score of 43% over the default version of the RdTools stochastic rate and recovery (SRR) algorithm. The highest performing algorithm included irradiance filtering and a cleaning detection threshold, adjusted based on the 40-day centered rolling median of the absolute day-to-day deviations in the daily performance index (PI). Furthermore, these improvements are promising as cleaning detection is an essential step in the automated analysis of PV soiling.

14 SOLAR ENERGY↗
DOE OSTI2020

Colorimetric Detection and Surface Decontamination of Metal Ions by Using Strippable Polymers - 20024

Strippable coating is a low cost and efficient decontamination method far more compatible for the cleaning of surfaces. This method allows contaminant entrapment and removal from different types of surfaces and also characterized by low-secondary waste generation after decontamination. The present work deals with the elaboration and testing of a new composition of Polyvinyl alcohol (PVA) aqueous solutions utilize the TTA (Thenoyl trifluoroacetone) ligand, employed as a strippable coating for the removal of metal ions e.g., UO{sub 2}{sup +2}, Ni{sup +2}, Cu{sup +2}, Fe{sup +3}, V{sup +5}, Eu{sup +3} and Cs{sup +} from 'contaminated' surfaces. The study aims at developing contaminant-sensing and decontaminating coatings for the detection and removal of metal ions from non porous surfaces. This 'smart' coating is shown to exhibit a clear contaminant dependent response in the form of color variation, and a detection sensitivity less than 0.5 μg per cm{sup 2}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2023

Optimizing foreground mitigation for CMB lensing with combined multifrequency and geometric methods

Here, a key challenge for current and upcoming cosmic microwave background lensing measurements is their sensitivity to biases from extragalactic foregrounds, such as Sunyaev-Zel'dovich signals or cosmic infrared background emission. Several methods have been developed to mitigate these lensing foreground biases, dividing broadly into multifrequency cleaning approaches and modifications to the estimator geometry, but how to optimally combine these methods has not yet been explored in detail. In this paper, we examine which combination of lensing foreground mitigation strategies is best able to reduce the impact of foreground contamination for a Simons Observatory-like experiment while preserving maximal signal-to-noise. Although the optimal combination obtained depends on whether bias reduction or variance reduction is prioritized and on whether polarization data is used, generally, we find that combinations involving both geometric (profile hardening, source hardening, or shear) and multifrequency (symmetric cleaning) methods perform best. For lensing power spectrum measurements from temperature (polarization and temperature), our combined estimator methods are able to reduce the bias below σ/4 or 0.3% (0.1%), a factor of 16 (30) lower than the standard quadratic estimator bias, at a modest signal-to-noise cost of only 18% (12%). In contrast, single-method foreground-mitigation approaches struggle to reduce the bias to a negligible level below σ/2 without incurring a large noise penalty. For upcoming and current experiments, our combined methods therefore represent a promising approach for making lensing measurements with negligible foreground bias.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source-receiver observation geometry. Here, we develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.

58 GEOSCIENCES↗
DOE OSTI2024

In Situ Monitoring of Non-Thermal Plasma Cleaning of Surfactant Encapsulated Nanoparticles

Surfactants are widely used in the synthesis of nanoparticles, as they have a remarkable ability to direct their growth to obtain well-defined shapes and sizes. However, their post-synthesis removal is a challenge, and the methods used often result in morphological changes that defeat the purpose of the initial controlled growth. Moreover, after the removal of surfactants, the highly active surfaces of nanomaterials may undergo structural reconstruction by exposure to a different environment. Thus, ex situ characterization after air exposure may not reflect the effect of the cleaning methods. Here, combining X-ray photoelectron spectroscopy, in situ infrared reflection absorption spectroscopy, and environmental transmission electron microscopy measurements with CO probe experiments, we investigated different surfactant-removal methods to produce clean metallic Pt nanoparticles from surfactant-encapsulated ones. It was demonstrated that both ultraviolet-ozone (UV-ozone) treatment and room temperature O2 plasma treatment led to the formation of Pt oxides on the surface after the removal of the surfactant. On the other hand, when H2 was used for plasma treatment, both the Pt0 oxidation state and nanoparticle size distribution were preserved. In addition, H2 plasma treatment can reduce Pt oxides after O2-based treatments, resulting in metallic nanoparticles with clean surfaces. These findings provide a better understanding of the various options for surfactant removal from metal nanoparticles and point toward non-thermal plasmas as the best route if the integrity of the nanoparticle needs to be preserved.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Technological diffusion trends suggest a more equitable future for rooftop solar in the United States

Abstract Equity has become central in the academic and regulatory discourse shaping the future of residential-scale clean energy technologies in the United States, particularly rooftop solar. Here, we develop a holistic perspective on these issues by analyzing rooftop solar adoption trends using two alternative forecasting methods: an inside-view forecast based on historical solar adoption data, and an outside-view forecast based on adoption data for other emerging consumer technologies. We show how rooftop solar, like other emerging consumer technologies, has become more equitably adopted over time. We show that solar diffusion patterns are largely consistent with those of other technologies. Both forecasting methods suggest that clean energy technologies should be expected to become more equitably adopted over time. Policy could accelerate this process by supporting low-income adoption without unduly curbing overall diffusion.

14 SOLAR ENERGY↗
DOE OSTI2021

Investigating Effects of Operational Parameters on the Rate of Electrochemical Cleaning of Chromium Deposits on Strontium-Doped Lanthanum Manganite Cathodes in Solid Oxide Fuel Cells

Chromium poisoning of the cathode remains a significant obstacle to the stable long-term performance of solid oxide fuel cells. This study reports, a quick, in-situ mitigation method, called electrochemical cleaning. By applying a mild anodic bias, the electrochemical deposition reactions of chromium-containing species are reversed. Chromium vapor species are formed, freeing active electrochemical sites in the air electrode. An LSM/YSZ-based cell was exposed to Cr vapors at 800ºC and then subjected to electrochemical cleaning. Cell performance recovery was evidenced by current-voltage and EIS measurements. Chromium removal was verified using SEM and EDS analyses. The cyclability of the electrochemical cleaning was tested by repeated poisoning and cleaning of another cell. Investigation into the effect of cell operating parameters (current density and cell temperature) on the rate of cleaning is discussed.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

The Roles of Interface, Adherend, and Adhesive in Plasma- and Other-treated Joints of Metals and FRP Materials Under Shear Deformation

This work investigated the role of interface, adherend, and adhesive in adhesively-bonded metal-metal, metal-CFRTP, and CFRTP-CFRTP combinations with plasma-treated surfaces under shear deformation. To this end, aluminum alloys (AA5052 and AA6061) and short-carbon-fiber-reinforced polyamide 66 (CFRPA66) were used as examples and performed through single lap shear testing. The results showed that air plasma surface treatment can improve the shear behavior of adhesively-bonded AA5052-CFRPA66 and CFRPA66-CFRPA66 joints with about 20% enhanced lap shear strength compared to non-treated cases. Failure surface morphology of plasma-treated CFRPA66 adherend confirmed this improvement by showing an increased amount of adhesive failure than interfacial failure between CFRPA66 and adhesive. But this is not true for AA5052-AA6061 joints with plasma-treated surfaces exhibiting almost no enhanced lap shear strength. This study also showed the importance of selecting a proper surface modification method for the enhancement of adhesive-bonded structures under shear deformation through the analysis of the results in this study and in the literature. For fiber-reinforced polymers (FRPs), improving FRP/adhesive interface may be prioritized via different surface modification methods (e.g., plasma, chemical coating, etc.) than adhesive modification methods. However, for surface-cleaned metals, toughening adhesive via different enhancement methods (e.g., nano-particles, chemical enhancement, etc.) may be more important than improving metal/adhesive interface. These insightful results are valuable in the area of multi-materials joining.

Qiao, Yao↗
DOE OSTI2020

Search for new phenomena in final states with large jet multiplicities and missing transverse momentum using $\sqrt{s}$ = 13 TeV proton-proton collisions recorded by ATLAS in Run 2 of the LHC

Results of a search for new particles decaying into eight or more jets and moderate missing transverse momentum are presented. The analysis uses 139 fb - 1 of proton-proton collision data at s = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. The selection rejects events containing isolated electrons or muons, and makes requirements according to the number of b -tagged jets and the scalar sum of masses of large-radius jets. The search extends previous analyses both in using a larger dataset and by employing improved jet and missing transverse momentum reconstruction methods which more cleanly separate signal from background processes. No evidence for physics beyond the Standard Model is found. The results are interpreted in the context of supersymmetry-inspired simplified models, significantly extending the limits on the gluino mass in those models. In particular, limits on the gluino mass are set at 2 TeV when the lightest neutralino is nearly massless in a model assuming a two-step cascade decay via the lightest chargino and second-lightest neutralino.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Clustering of CODEX clusters

The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite All-Sky Survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey. We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r 0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ω m0 = $0.22_{-0.03}^{+0.04}$ and S 8 = σ 8 (Ω m0 /0.3)0.5 = $0.85_{-0.08}^{+0.10}$ with estimated additional systematic errors of σ Ω m0 = 0.02 and σS 8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ωm = 0.25 ± 0.01 and σ 8 = $0.81_{-0.02}^{+0.01}$ with an estimated additional systematic error of σ Ω m0 = 0.07 and σ σ8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Direct solar self-catalyzing pyrolysis of natural gas to hydrogen and high-quality graphite (Final Technical Report)

This project, led by researchers at UCLA in partnership with Southwest Solar Technology (SST) and SolGrapH, successfully demonstrated a new method for producing clean hydrogen fuel and high-quality graphite using concentrated solar energy. The primary goal was to develop a technology that converts natural gas (primarily methane) into two valuable products—hydrogen gas and solid carbon—without releasing carbon dioxide (CO2) into the atmosphere. This process, known as solar thermal pyrolysis, uses heat from the sun to split the methane molecules directly.

03 NATURAL GAS↗
DOE OSTI2025

Dynamic Low-Rank Training with Spectral Regularization: Achieving Robustness in Compressed Representations

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94\% compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2025

Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94 compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
DOE OSTI2022

Exploration of methods to remove implanted 210 Pb and 210 Po contamination from silicon surfaces

Radioactive contaminants on the surfaces of detector components can be a problematic source of background events for physics experiments searching for rare processes. Exposure to radon is a specific concern because it can result in the relatively long-lived 210 Pb (and progeny) being implanted to significant subsurface depths such that removal is challenging. Here, in this article, we present results from a broad exploration of cleaning treatments to remove implanted 210 Pb and 210 Po contamination from silicon, which is a material frequently used in rare-events searches. We demonstrate for the first time that heat treatments (“baking”) can effectively mitigate such surface contamination, with a 1200 °C bake resulting in a 97$^{+2}_{-3}$% reduction. We also report results using wet-chemistry and plasma-based methods, which show that etching can be highly effective provided the etch depth is sufficiently aggressive. Our survey of cleaning methods suggests consideration of multiple approaches during the different phases of detector construction to enable greater flexibility for efficient removal of 210 Pb and 210 Po surface contamination.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Computing partition functions in the one-clean-qubit model

We present a method to approximate partition functions of quantum systems using mixed-state quantum computation. For positive-semidefinite Hamiltonians, our method has an expected running-time that is almost linear in [M/( ε rel Z )] 2 , where M is the dimension of the quantum system, Z is the partition function, and ε rel is the relative precision. It is based on approximations of the exponential operator as linear combinations of certain operators related to block-encoding of Hamiltonians or Hamiltonian evolutions. The trace of each operator is estimated using a standard algorithm in the one-clean-qubit model. For large values of Z , our method may run faster than exact classical methods, whose complexities are polynomial in M . We also prove that a version of the partition function estimation problem within additive error is complete for the so-called DQC1 complexity class, suggesting that our method provides a superpolynomial speedup for certain parameter values. Overall, to attain a desired relative precision, we develop a classical procedure based on a sequence of approximations within predetermined additive errors that may be of independent interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Competition response of cloud supersaturation explains diminished Twomey effect for smoky aerosol in the tropical Atlantic

The Twomey effect brightens clouds by increasing aerosol concentrations, which activates more droplets and decreases cloud supersaturation in response to more competition for water vapor. To quantify this competition response, we used marine low cloud observations in clean and smoky conditions at Ascension Island in the tropical South Atlantic during the Layered Aerosol Smoke Interactions with Cloud (LASIC) campaign. These observations show similar increases in droplet number for increased accumulation-mode particles from surface-based and satellite cloud retrievals, demonstrating the importance of below-cloud aerosol measurements for retrieving aerosol–cloud interactions (ACI) in clean and smoky aerosol conditions. Four methods for estimating cloud supersaturation from aerosol–cloud measurements were compared, with cloud scene-based and parcel-based methods showing sufficient variability for a strong dependence on both aerosol accumulation number concentration and cloud-base updraft velocities. Decomposing aerosol-related changes in cloud albedo and optical depth shows the calculated competition response accounts for dampening the activation response by 12 to 35%, explaining the diminished Twomey effect at high aerosol concentrations observed for smoky conditions at LASIC and previously around the world. This result was consistent for independent supersaturation retrievals by cloud scene-based droplet number and cloud condensation nuclei and parcel-based multimode size-resolving Lagrangian methods. Translating aerosol effects to local radiative forcing with clean conditions as a proxy for preindustrial and smoky conditions for present-day showed that the competition response reduces cooling from the Twomey radiative forcing by 12 to 35%, providing an essential process-specific constraint for improving the representation of aerosol competition in climate model simulation of indirect aerosol forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Effect of Adsorbed Volatile Organic Compounds on an Ultrathin Water Film Measurement

Using surface plasmon resonance imaging (SPRi), we have recently shown for the first time the existence of a monolayer water film between droplets during dropwise condensation. This study examines the effect of adsorbed volatile organic compounds (VOCs) on the ultrathin film measurement using SPRi. Further, the work presents the proper surface-treatment process that enables measurements of the ultrathin water layer during high-speed imaging of dropwise condensation at 3000 frame per second. In this study, two methods were applied for cleaning the surface (gold-coated glass)—(1) standard cleaning procedure (SCP) using acetone, isopropyl alcohol, and deionized water and (2) SCP followed by air plasma cleaning. This work discusses the effect of the cleaning procedures on surface roughness, contact angle, and surface chemistry using atomic force microscopy, optical microscopy, and an X-ray photoelectron spectroscope meter. The results showed that SCP before the SPRi is a proper surface-treatment method. The effect of adsorbed VOCs during dropwise condensation on a surface treated with SCP was measured to be 0.0025 (reflectivity unit), which was 70% smaller than the reflectance associated with a monolayer water film. The results of this work confirm a monolayer water film observation during the dropwise condensation, which has been reported before.

36 MATERIALS SCIENCE↗