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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effects of sample bias on adhesion of magnetron sputtered Cr coatings on SiC

Swelling of SiC at 300 C due to in-service neutron irradiation causes tensile residual stresses in coatings which are expected to adversely affect the performance of coated SiC composite fuel cladding for light water reactors. Matching the coating swelling with the substrate, a solution common for thermal expansion, is not practical in the case of neutron irradiation. Biasing samples during magnetron sputtering deposition induces compressive residual stress which may counteract this. In this study, chromium coatings were deposited on SiC by DC magnetron sputtering with no external heating at bias voltages of –50V, –75V, and –100V. The effects of the bias voltage on morphology, residual stress, microstrain, texture, and adhesion are shown. The low deposition temperature resulted in the coating microstructure evolution following an energetic particle bombardment dominated trend. Additionally, at the two lower bias voltages knock-on implantation dominated increasing the residual stress and microstrain while at the highest bias voltage, thermal spike migration allowed for defect relaxation. When the knock-on induced compressive residual stress exceeded 0.8 GPa microcrack formation in the SiC substrate decreased coating adhesion. While no microcracks formed at the lowest bias voltage, insufficient atomic mobility during coating growth lead to voids forming in the coating. A balance is needed to form void-free coatings that have high compressive residual stress.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Combination of Rejection Sampling with Biased Probability Density Sampling (with Special Comments on MCNP "Cookie Cutter" Rejection)

Sampling from biased probability densities and rejection sampling are both very common Monte Carlo techniques. As far as I know, the combination of sampling from a biased density and rejection sampling has not been developed. This note develops the theory for sampling from a biased density and then accepting or rejecting that sampling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Standard experimental paradigm designs and data exclusion practices in cognitive psychology can inadvertently introduce systematic “shadow” biases in participant samples

Abstract Standard cognitive psychology research practices can introduce inadvertent sampling biases that reduce the reliability and generalizability of the findings. Researchers commonly acknowledge and understand that any given study sample is not perfectly generalizable, especially when implementing typical experimental constraints (e.g., limiting recruitment to specific age ranges or to individuals with normal color vision). However, less obvious systematic sampling constraints, referred to here as “shadow” biases, can be unintentionally introduced and can easily go unnoticed. For example, many standard cognitive psychology study designs involve lengthy and tedious experiments with simple, repetitive stimuli. Such testing environments may 1) be aversive to some would-be participants (e.g., those high in certain neurodivergent symptoms) who may self-select not to enroll in such studies, or 2) contribute to participant attrition, both of which reduce the sample’s representativeness. Likewise, standard performance-based data exclusion efforts (e.g., minimum accuracy or response time) or attention checks can systematically remove data from participants from subsets of the population (e.g., those low in conscientiousness). This commentary focuses on the theoretical and practical issues behind these non-obvious and often unacknowledged “shadow” biases, offers a simple illustration with real data as a proof of concept of how applying attention checks can systematically skew latent/hidden variables in the included population, and then discusses the broader implications with suggestions for how to manage and reduce, or at a minimum acknowledge, the problem.

Siritzky, Emma M.↗
DOE OSTI2026

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

Materials science↗
DOE OSTI2021

Methods and devices configured to operated scanning tunneling microscopes using out-of-bandwidth frequency components added to bias voltage and related software

In the system and method disclosed, an ultrahigh vacuum (UHV) scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from the Si(100)-2X1:H surface by injecting electrons at a negative sample bias voltage. A new lithography method is disclosed that allows the STM to operate under imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative sample bias voltage to deliver the required energy for hydrogen removal. The resulted current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM's feedback loop. This approach offers a significant potential for controlled and precise removal of hydrogen atoms from a hydrogen-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

Moheimani, Seyed Omid Reza↗
DOE OSTI2023

Methods and devices configured to operated scanning tunneling microscopes using out-of-bandwidth frequency components added to bias voltage and related software

In the system and method disclosed, an ultrahigh vacuum (UHV) scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from the Si(100)-2X1:H surface by injecting electrons at a negative sample bias voltage. A new lithography method is disclosed that allows the STM to operate under imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative sample bias voltage to deliver the required energy for hydrogen removal. The resulted current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM's feedback loop. This approach offers a significant potential for controlled and precise removal of hydrogen atoms from a hydrogen-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

Moheimani, Seyed Omid Reza↗
DOE OSTI2025

A Novel Optical Instrument for On-Line Measurement of Particle Size Distribution—Application to Clean Coal Technologies

A flow cell is a critical measurement interface for many optical instruments. However, the flows are often sampled under harsh conditions, such as under high pressure and/or high temperature, in the presence of particles, moisture, vapors with high dew points or corrosive gases. Therefore, obtaining a high-optical-quality flow cell that does not perturb the measurement is a significant challenge. To address this challenge, we proposed a new flow cell that employs a unique laminar coaxial flow field (for the purge and sample flows). A test system was built to conduct particle size distribution (PSD) measurements with no sampling bias using a state-of-the-art analyzer (Malvern Panalytical Insitec). The results revealed that the measurement zone is well defined solely by the sample flow, and the optical windows are well protected by the purge flow, with minimal risk of any depositions from the sample flow. Using this flow cell, the Insitec can successfully measure PSD under high pressure and temperature under moist, corrosive conditions without generating any sampling bias. Importantly, we successfully applied this flow cell for on-line PSD measurement for the flue gas of a 100 kWth pressurized oxy-coal combustor operating at 15 bara.

Cheng, Mao (ORCID:0000000213273900)↗
DOE OSTI2022

J-PLUS: Uncovering a large population of extreme [OIII] emitters in the local Universe

Over the past decades, several studies have discovered a population of galaxies that undergo very strong star formation events. They are called extreme emission line galaxies (EELGs). We exploit the capabilities of the Javalambre Photometric Local Universe Survey (J-PLUS), a wide-field multifilter survey, with which 2000 square degrees of the northern sky are already observed. We use it to identify EELGs at low redshift by their [OIII]5007 emission line. We intend to provide a more complete, deep, and less biased sample of local EELGs. We selected objects with an excess of flux in the J-PLUS medium-band J0515 filter, which covers the [OIII] line at z < 0.06. We removed contaminants (stars and higher-redshift systems) using J-PLUS and WISE infrared photometry, with SDSS spectra as a benchmark. We performed spectral energy distribution fitting to estimate the physical properties of the galaxies: line fluxes, equivalent widths (EWs), masses, stellar population ages, and so on. We identify 466 EELGs at z < 0.06 with [OIII] EW over 300 Å and an r-band magnitude below 20, of which 411 were previously unknown. Most show compact morphologies, low stellar masses (log(M • /M ⊙ ) ~ 8.13 –0.58 +0.61 ), low dust extinction (E( B–V ) ~ 0.1 –0.1 +0.2 ), and very young bursts of star formation (3.0 –2.0 +2.7 Myr). Our method is up to ~20 times more efficient in detecting EELGs per Mpc 3 than broadband surveys, and it is as complete as magnitude-limited spectroscopic surveys (but reaches fainter objects). The sample is not directly biased against strong Hα emitters, in contrast with works using broadband surveys. We demonstrate that J-PLUS can identify a large sample of previously unknown EELGs showing unique properties following a clear selection process. A fraction of the EELGs are probably similar to the first galaxies in the Universe, but they are at a much lower redshift, which makes them ideal targets for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2022

Revisiting the role of bars in AGN fuelling with propensity score sample matching

The high luminosity displayed by an active galactic nucleus (AGN) requires that gas be transported to the centre of the galaxy by some mechanism. Bar-driven processes are often pointed out in this context and a number of studies have addressed the bar–AGN connection, but with conflicting results. Some of the inconsistencies can be explained by the different spatial- and timescales involved in bar-driven gas inflows, accretion by the central black hole, and AGN emission. However, the discrepant results could also be due to sample biases, because both the AGN activity determination and the bar detection are influenced by the method employed. We revisit the bar–AGN connection in a sample of galaxies from SDSS, looking for evidence of the influence of bars on AGN activity. We determine AGN activity by emission line diagnostics and the properties of the bar were previously estimated with BUDDA, which performs 2D bulge–bar–disk decomposition. Before comparing active and inactive galaxies, we made a careful selection of the sample to minimise selection biases. We created control samples by matching them with the AGN sample using propensity score matching. This technique offers an analytical approach for creating control samples given some object parameters. We find that AGN are preferentially found in barred galaxies and that the accretion rate is higher in barred galaxies, but only when different M–σ relations are used to estimate the black hole mass M• in barred and unbarred galaxies (from the central velocity dispersion σ). On the other hand, we find no correlation between activity level and bar strength. Altogether, our results strengthen theoretical predictions that the bar is an important mechanism in disc galaxies, creating a gas reservoir to feed AGN, but they also indicate that other mechanisms can play a major role, particularly at scales ≲100 pc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quashing a suspected selection bias in galaxy samples having dynamically measured supermassive black holes

Local early-type galaxies (ETGs) with directly measured black hole masses, M bh , have been reported to represent a biased sample relative to the ETG population at large. Such galaxies with Spitzer Space Telescope imaging have been purported to possess velocity dispersions, σ, at least ∼0.1 dex larger for a given galaxy stellar mass, M * , gal , than is typically observed among thousands of ETGs imaged by the Sloan Digital Sky Survey (SDSS). This apparent offset led Shankar et al. to reduce the normalisation of the observed M bh ∝ σ 5 relation by at least ∼0.5 dex to give their ‘intrinsic relations’, including σ-based modifications to the observed M bh –M * , gal relation. These modifications were based on the untested assumption that the stellar masses had been derived consistently between the two samples. Here, we provide the necessary check using galaxies common to the Spitzer Survey of Stellar Structure in Galaxies (S 4 G) and the SDSS. We find that the stellar masses of galaxies with and without directly measured black hole masses had appeared offset from each other due to the use of inconsistent stellar mass-to-light ratios, Υ * , for the optical and infrared data. We briefly discuss the ‘intrinsic relations’ and why some of these will at times appear to have had partial success when applied to data based on similarly inconsistent values ofΥ * . Finally, we reiterate the importance of the υ (lower-case Υ) term, which we previously introduced into the M bh –M * relations to help avoid Υ * -related mismatches.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Solute chemistry for streams draining geomorphic features and varying land cover gradients in the East River watershed, Colorado

This dataset contains solute chemistry data and GPS coordinates for surface water samples collected in the East River watershed in the Elk and West Elk Ranges of Colorado from August 3rd through August 11th, 2022. Samples were collected from streams and groundwater seeps as part of a comprehensive analysis within the watershed of how different geomorphic features (such as landslides and rock glaciers) along with variations in land cover (such as the presence of vegetation) affect stream water quality in this Rocky Mountain headwater environment. This dataset includes concentrations of the following in a single csv file: fluoride, chloride, sulfate, nitrate, calcium, potassium, magnesium, sodium, silicon, and total organic carbon (TOC). All concentrations are reported in mg/L. The sulfate data in this version have been updated to account for an earlier ion chromatograph instrument method error whereby sulfate and bromide peaks overlapped in calibration solution samples, biasing calculated sulfate concentrations low by 30-50%. Sulfate data reported herein are from a re-run of the samples performed in December 2024 using a corrected instrument method. All samples were stored under refrigerated conditions and filtered on initial collection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Experimental study on particle formation in a pressurized oxy-fuel combustor using a novel in-situ optical instrument

Particle formation in pressurized oxy-fuel combustion can be fundamentally different from oxy-fuel combustion at atmospheric pressure. Under high pressures, the rate of char gasification with CO2 and H2O increases significantly and dominates the char reactions particularly at high temperatures. Also, the extent of fragmentation varies. This could result in a very different particle size distribution from that of air-fired combustion. To accurately obtain the particle size distribution in a pressurized oxy-fuel combustor without sampling bias, the particle measurement should be made at the system pressure. Very few published studies have accomplished this, and never under the conditions reported herein. In this work, we fire PRB coal in a 100 kWth staged pressurized oxy-fuel combustor (SPOC) under two pressures, 15 and 2 bara. A sampling probe (oil-cooled) is installed at the combustor outlet and the aerosol flow are sampled isokinetically. We utilize a state-of-the-art optical analyzer (Malvern Insitec) with a novel high-pressure and high-temperature flow cell to measure the particle size distribution and concentration in-situ in a sampled flow, which contains moist/corrosive flue gas under the process pressure and a controlled temperature that is kept above the dew point. The results show that the pressure can significantly influence the particle size distribution and concentration from the oxy-fuel combustor.

Cheng, Mao↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2022

Disentangling jet modification in jet simulations and in Z+jet data

The selection of jets in heavy-ion collisions based on their p T after jet quenching is known to bias towards jets that lost little energy in the quark-gluon plasma. In this work, we study and quantify the impact of this selection bias on jet substructure observables so as to isolate effects caused by the modification of the substructure of jets by quenching. We do so at first in a simplified Monte Carlo study in which it is possible to identify the same jet before and after quenching. We show explicitly that jets selected based on their quenched (i.e. observable) p T have substantially smaller fractional energy loss than those selected based on the p T that they would have had in the absence of any quenching. This selection bias has a large impact on jet structure and substructure observables. As an example, we consider the angular separation ΔR of the hardest splitting in each jet, and find that the ΔR distribution of the (biased) sample of jets selected based upon their quenched p T is almost unmodified by quenching. In contrast, quenching causes dramatic modifications to the ΔR distribution of a sample of jets selected based upon their unquenched p T , with a significant enhancement at larger ΔR coming from the soft particles originating from the wake of the jet in the quark-gluon plasma. The jets which contribute to this enhancement are those which have lost the most energy and which were, therefore, left out of the sample selected after quenching. In a more realistic study, we then show that the same qualitative effects can all be observed in Z+jet events. Selecting jets in such events based on either the jet p T or the Z-boson p T provides an experimentally accessible way to quantify the effects of selection biases in jet observables and separate them from the modification of jet substructure caused by quenching. Selecting Z+jet events based upon the jet p T yields a ΔR distribution that appears almost unmodified whereas selecting Z+jet events based upon the Z-boson p T reveals a significant modification to the ΔR-distribution caused by quenching, once again arising from the wakes of those jets that lose more energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗