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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 OSTI2024

Point-of-use filtration units as drinking water distribution system sentinels

Abstract Municipal drinking water distribution systems (DWDSs) and associated premise plumbing (PP) systems are vulnerable to proliferation of opportunistic pathogens, even when chemical disinfection residuals are present, thus presenting a public health risk. Monitoring the structure of microbial communities of drinking water is challenging because of limited continuous access to faucets, pipes, and storage tanks. We propose a scalable household sampling method, which uses spent activated carbon and reverse osmosis (RO) membrane point-of-use (POU) filters to evaluate mid- to long-term occurrence of microorganisms in PP systems that are relevant to consumer exposure. As a proof of concept, POU filter microbiomes were collected from four different locations and analyzed with 16S rRNA gene amplicon sequencing. The analyses revealed distinct microbial communities, with occasional detection of potential pathogens. The findings highlight the importance of local, and if possible, continuous monitoring within and across distribution systems. The continuous operation of POU filters offers an advantage in capturing species that may be missed by instantaneous sampling methods. We suggest that water utilities, public institutions, and regulatory agencies take advantage of end-of-life POU filters for microbial monitoring. This approach can be easily implemented to ensure drinking water safety, especially from microbes of emerging concerns; e.g., pathogenic Legionella and Mycobacterium species.

42 ENGINEERING↗
DOE OSTI2025

Round Robin Analysis of Uranium Isotopics on Cotton Swipes Measured Using Microextraction-Based Sampling Versus Conventional Bulk Analysis

A comparison of microextraction sampling methods to directly analyze uranium isotopics on cotton swipes was performed concurrently with traditional bulk-processing methods. For the microextraction sampling approach, two different detection platforms were evaluated, a quadrupole-based inductively coupled plasma mass spectrometer (ICP-MS) and the liquid sampling-atmospheric pressure glow discharge (LS-APGD) coupled to an Orbitrap mass spectrometer. Results presented from this innovative sampling approach (i.e., microextraction) are compared with a more traditional approach employed for analysis of cotton-based environmental swipes, namely bulk ashing/digestion, separation, and subsequent analysis by high-precision multi-collector ICP-MS. Overall, the microextraction approach proved to be a reliable and accurate means to determine isotopic ratios of uranium collected on cotton swipes. The ICP-MS-based detection had relative standard deviations of <0.65% for the major isotopic determinations, whereas the LS-APGD-Orbitrap method had relative standard deviations of <3%. The percent relative difference for the 235 U/ 238 U ratios, in comparison to the expected values, was <1% for ICP-MS and <4% for the microplasma-Orbitrap method. Additionally, the microextraction ICP-MS accurately (<2%) and precisely (<5%) determined the minor isotopic compositions (i.e., 234 U/ 238 U and 236 U/ 238 U).

ICP-MS↗
DOE OSTI2024

Apparatus and methods for sample analysis with multi-gradient microfluidics

A device for analyzing biological samples comprises first, second, third, and fourth layers. The first layer comprises a sample chamber in which a sample is positioned. The second layer comprises first, second, and third channels. A third, porous layer is positioned between the first layer and the second layer. A fourth layer composed of a substantially liquid-impermeable material is positioned between the second layer and the third layer. The fourth layer includes first and second pass-through channels that are aligned with the first and second channel, respectively. Fluids that flow in the first and second channels pass through the pass-through channels and diffuse into the sample chamber, establishing a chemical concentration gradient therein. A gas in the sample chamber can diffuse through the third and fourth layers and interact with a fluid flowing in the third channel, establishing a gas concentration gradient in the sample chamber.

Kim, Peter Wonhee↗
DOE OSTI2023

nautilus : boosting Bayesian importance nested sampling with deep learning

ABSTRACT We introduce a novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Unlike rejection-based sampling methods such as vanilla nested sampling (NS) or Markov chain Monte Carlo (MCMC) algorithms, importance sampling techniques can use all likelihood evaluations for posterior and evidence estimation. However, for efficient importance sampling, one needs proposal distributions that closely mimic the posterior distributions. We show how to combine INS with deep learning via neural network regression to accomplish this task. We also introduce nautilus, a reference open-source python implementation of this technique for Bayesian posterior and evidence estimation. We compare nautilus against popular NS and MCMC packages, including emcee, dynesty, ultranest, and pocomc, on a variety of challenging synthetic problems and real-world applications in exoplanet detection, galaxy SED fitting and cosmology. In all applications, the sampling efficiency of nautilus is substantially higher than that of all other samplers, often by more than an order of magnitude. Simultaneously, nautilus delivers highly accurate results and needs fewer likelihood evaluations than all other samplers tested. We also show that nautilus has good scaling with the dimensionality of the likelihood and is easily parallelizable to many CPUs.

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

Structure-aware methods for expensive derivative-free nonsmooth composite optimization

We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some known mapping of outputs from a computationally expensive function. We provide accompanying implementations of these methods: in particular, a novel manifold sampling algorithm (MS-P) with subproblems that are in a sense primal versions of the dual problems solved by previous manifold sampling methods and a method (GOOMBAH) that employs more difficult optimization subproblems. For these two methods, we provide rigorous convergence analysis and guarantees. We demonstrate extensive testing of these methods. Open-source implementations of the methods developed in this manuscript can be found at https://github.com/POptUS/ IBCDFO/.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient learning of accurate surrogates for simulations of complex systems

Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. Here, we introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Mitigation of the Brighter-Fatter Effect in the LSST Camera

Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). This particularly impacts bright calibration stars, which appear larger than their intrinsic shape, creating a flux-dependent point-spread function (PSF) that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSST Camera before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new sampling method based on a physically motivated model to account these higher-order terms in the correction, and then we test the modified correction on both datasets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars which we conclude is the result of a unmodeled curl component of the deflection field by the correction. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Correlation function distributions for O ( N ) lattice field theories in the disordered phase

Numerical computations in strongly interacting quantum field theories are often performed using Monte Carlo sampling methods. A key task in these calculations is to estimate the value of a given physical quantity from the distribution of stochastic samples that are generated using the Monte Carlo method. Typically, the sample mean and sample variance are used to define the expectation values and uncertainties of computed quantities. However, the Monte Carlo sample distribution contains more information than these basic properties, and it is useful to investigate it more generally. In this work, the exact form of the probability distributions of two-point correlation functions at zero momentum in O ( N ) lattice field theories in the disordered phase and in infinite volume are determined. These distributions allow for a robust investigation of the efficacy of the Monte Carlo sampling procedure and are shown also to allow for improved estimators of the target physical quantity to be constructed. The theoretical expectations are shown to agree with numerical calculations in the O ( 2 ) model. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Field expedient stool collection methods for gut microbiome analysis in deployed military environments

ABSTRACT Field expedient devices and protocols for the collection, storage, and shipment of stool samples in deployed settings are needed for the advancement of microbiome research in military health. Relevant assessments include the evaluation of microbiome signatures associated with susceptibility to travelers’ diarrhea and recovery of gut function following infection. However, inherent biases in microbial measurements due to preservatives and sampling methods are unclear and should be assessed for an accurate evaluation of the microbiome. We performed shotgun metagenomic sequencing and compared the microbiome composition in paired fecal samples collected using Flinters Technology Associates (FTA) cards and OMNIgene (OG) Gut tubes, prior to and during international travel, from 49 adult participants, 39 of whom remained asymptomatic and 10 experienced travelers’ diarrhea. Higher concentrations of nucleic acid and sequencing libraries were observed in OG samples. A majority of genera (82.9%) were detected with both methods, and detections of genera limited to one collection method were not highly prevalent across samples and were present in extremely low relative abundances (<0.01%). Differences in beta diversity were largely explained by inter-individuality of microbiome composition, followed by the effect of collection method and timepoint-disease states. Differential abundance analysis indicated that Corynebacterium and Blautia were consistently higher in abundance across all groups with FTA and OG collection, respectively. The observed differences in microbiome composition between methods suggest the need for consistent and standardized protocols within a study. Overall, the data presented here could help guide the future design of fecal microbiome study protocols in field and military deployment settings. IMPORTANCE The assessment of field-deployable methods for fecal sample collection and storage is required to reliably capture samples collected in remote and austere locations. This study describes a comparative metagenomics analysis between samples collected by two different commercially available methods in a military-deployed setting. The results presented here are foundational for the future design of fecal microbiome study protocols in an operational context.

field study↗
DOE OSTI2025

Molten Salt Synthesis of Increased (100)-Facet and Polycrystalline Nickel Oxide Nanoparticles for the Oxygen Evolution Reaction: Impact of Facet and Crystallinity on Electrocatalysis

Nickel oxide nanocubes with increased (100) surface facet presence (NiO(100)) were synthesized through a molten salt synthesis procedure to probe their oxygen evolution reaction (OER) activity in order to investigate the relationship between the surface facet and OER performance. While altering the synthesis parameters to decrease NiO(100) particle sizes and agglomeration, a polycrystalline NiO nanoparticle system formed from using Li2O as a Lux-Flood base (labelled Li2O-MSS NiO, where MSS stands for molten salt synthesis). This novel synthesis was further elaborated and the obtained materials were also tested for OER activity. After thorough structural characterization to determine crystallinity, lattice spacings, and elemental distribution, their OER activity was compared versus high surface area NiO(111) nanosheets in a three-electrode rotating disk electrode (RDE) system. The activity trend of (111) > Li2O-MSS > (100) was observed. This decrease in activity of the nanocube and polycrystalline samples was explained by differences between theoretical and experimental conditions, differences in ink rheology and resulting catalyst layer properties, and significant agglomeration seen in the imaging of the sample. Methods for improving the OER activity of these samples are discussed in the conclusion of this study.

08 HYDROGEN↗
DOE OSTI2025

Efficient Monte Carlo event generation for neutrino-nucleus exclusive cross sections

Modern neutrino-nucleus cross section computations need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing normalizing flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than 98.4%, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Non-destructive, in-situ evaluation of water presence using thermal contrast and cooled detector

Exemplary systems for detecting water include: a light source positioned to transmit thermal radiation through a sample; a lens assembly positioned to: receive the thermal radiation transmitted through the sample; and focus the transmitted thermal radiation onto a filter positioned between the lens assembly and a detector; and a cooling subsystem for cooling the filter and the detector to a temperature below that of the sample. Methods for detecting presence of water in a sample are also disclosed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics

Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with machine learning, they often rely on pre-selected expert-based features. Here, in this work, we present a Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) framework, which combines graph neural networks and the state predictive information bottleneck to automatically learn low-dimensional representations directly from atomic coordinates. Tested on three benchmark systems, our approach predicts essential structural, thermodynamic and kinetic information for slow processes, demonstrating robustness across diverse systems. The method shows promise for complex systems, enabling effective enhanced sampling without requiring pre-defined reaction coordinates or input features.

Zou, Ziyue↗
DOE OSTI2023

Litterfall and Branchfall Mass Flux in Malaysia Lambir Hills GEM plots (2008-2010)

This data package includes two folders: Litterfall and Branchfall. The Litterfall folder includes three files: "GEM_Lambir_Hills_Litterfall_Mass_Flux_Data_2008_to_2010.csv" includes the litterfall data, the "GEM_Lambir_Hills_Litterfall_Metadata.xlx" includes the descripion of the columns in the data file, and the PDF file "GEM_Lambir_Hills_Litterfall_Methods_Description" includes a brief description of the litterfall sampling methods. The Branchfall Folder includes four files: the "GEM_Lambir_Hills_Branchfall_Data_2009_to_2010.csv" file includes the branchfall data, the "GEM_Lambir_Hills_Branchfall_Metadata_2009_to_2010.xlsx" includes the description of the columns in the csv data file, the "GEM_Lambir_Hills_Branchfall_Methods_Description.pdf" includes a brief description of the branchfall collection methods, and the "GEM_Lambir_Hills_Branchfall_Data_Metadata_Methods_TransectSums.xlx" includes data, metadata, methods description, and a 'TransectsSums' tab where the reader can find the sum of the branchfall data in each transect Litterfall and Branchfall data were collected to quantify canopy productivity and net primary productivity allocated to branch turnover, which are components of the total net primary productivity and ecosystem carbon budget. The methods follow the Global Ecosystems Monitoring (GEM) protocols (see gem.tropicalforests.ox.ac.uk). The data collection took place from 2008 to 2010 in two 1-ha GEM plots in Lambir Hills National Park, Sarawak, Malaysia, within the Lambir 52-ha ForestGEO plots. One of the plots was located on clay soil and the other on sandy loam. The site is an old-growth moist tropical forest dominated by Dipterocarpacea. The climate is moist tropical and aseasonal. Litterfall was collected biweekly from 25 litter traps per plot. Branchfall was estimated using four 100-m transects, which were surveyed every two months.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Calendar Year 2021 Underground Test Area Annual Sampling Letter Report Nevada National Security Site, Nevada (Rev. 1) WITH ROTC-1

This calendar year (CY) 2021 annual sampling letter report presents Plan implementation and a summary of field activities, sampling methods, well completion diagrams, and analytical laboratory results from sampling of wells on Pahute Mesa. The CY 2021 letter report also presents data from samples collected in CY 2019. These CY 2019 results were not reported in previous sampling reports because they were not available from the specialized laboratory (Lawrence Livermore National Laboratory [LLNL]) at the time of the previous reports’ publication.

54 ENVIRONMENTAL SCIENCES↗