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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using low volume eDNA methods to sample pelagic marine animal assemblages

Environmental DNA (eDNA) is an increasingly useful method for detecting pelagic animals in the ocean but typically requires large water volumes to sample diverse assemblages. Ship-based pelagic sampling programs that could implement eDNA methods generally have restrictive water budgets. Studies that quantify how eDNA methods perform on low water volumes in the ocean are limited, especially in deep-sea habitats with low animal biomass and poorly described species assemblages. Using 12S rRNA and COI gene primers, we quantified assemblages comprised of micronekton, coastal forage fishes, and zooplankton from low volume eDNA seawater samples (n = 436, 380–1800 mL) collected at depths of 0–2200 m in the southern California Current. We compared diversity in eDNA samples to concurrently collected pelagic trawl samples (n = 27), detecting a higher diversity of vertebrate and invertebrate groups in the eDNA samples. Differences in assemblage composition could be explained by variability in size-selectivity among methods and DNA primer suitability across taxonomic groups. The number of reads and amplicon sequences variants (ASVs) did not vary substantially among shallow (<200 m) and deep samples (>600 m), but the proportion of invertebrate ASVs that could be assigned a species-level identification decreased with sampling depth. Using hierarchical clustering, we resolved horizontal and vertical variability in marine animal assemblages from samples characterized by a relatively low diversity of ecologically important species. Low volume eDNA samples will quantify greater taxonomic diversity as reference libraries, especially for deep-dwelling invertebrate species, continue to expand.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Production and Evaluation of Fluorophore-Doped Polymer Substrates to Screen for Plastic-Degrading Enzymes

Fast and sensitive analytical methods are the key to efficient screening of plastic-degrading enzymes. Here, we present a streamlined and affordable approach to assess the enzymatic deconstruction of insoluble synthetic polymers by blending them with a fluorescent dye, rhodamine 6G, and we evaluate this screening method using poly(ethylene terephthalate) (PET) as a model material. Our results indicate that enzymatic depolymerization of the rhodamine-doped PET can be observed in a high-throughput fashion by following release of the fluorophore. The fluorescence data obtained during the hydrolysis of rhodamine-doped PET by 14 PET hydrolases, produced with a robotic platform, correlated with the quantitative chromatographic analysis of PET degradation products. Remarkably, the use of the rhodamine-loaded PET substrate resulted in negligibly low background signals even when detecting PETase activity in crude cell lysates, suggesting suitability for screening of a wide variety of samples. Encouraged by these results, we next produced a selection of polyethylene- and nylon-based materials loaded with rhodamine 6G. While rapid leaching of fluorophore observed with nylon substrates limits the utility of the method for detecting nylonase activity, the rhodamine-loaded polyethylene showed promising performance in passive diffusion tests, indicating that this latter substrate may be used to screen for polyolefin-degrading enzymes.

09 BIOMASS FUELS↗
DOE OSTI2025

Mechanistic insights into rubidium ion adsorption at the quartz (101) surface from quantum chemical metadynamics

Ion sorption extent and mechanism depend in part on the mineral surface termination, which can be highly complex. Variations in surface functional groups, particularly with defect density and surface roughness, influence mineral reactivity towards solutes. In this work, we investigate the adsorption of a rubidium cation (Rb + ) at pristine and defect quartz (101) surface sites using well-tempered metadynamics, based on simulations with the quantum chemical density-functional tight-binding (DFTB) method. We compare the relative energetics of Rb + adsorption across selected sites for each surface, with nanosecond-level sampling, highlighting similarities between vicinal and geminal silanol sites. We find that the positive Rb + partial atomic charge can increase by as much as 0.5e as it approaches the surface, with implications for modulation of ion adsorption strength and extent at the quartz (101) surface with surface vacancies, silanol coverage, and charge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Cobalt promotion of the InO x –TiO 2 heterojunction for dual photothermal reduction of CO 2

A series of cobalt-promoted indium–titanium composite oxides was synthesized using a microemulsion method. Their functional properties were investigated for the photothermal reduction of carbon dioxide. In this series, the indium content varied between 2.5 and 20%, while the cobalt percentage was kept constant at 4% throughout the series. In all cases, carbon monoxide formation occurred selectively through the reverse water gas shift reaction. The sample with 2.5% In maximized the synergistic use of the two energy sources, while the sample with 10% In showed the highest catalytic activity under both thermal and dual photo-thermo conditions. A physicochemical characterization was performed for all samples. The use of microscopy and X-ray absorption spectroscopy demonstrated that sub-nanometric indium entities, in combination with atomically dispersed cobalt oxide entities, achieved a balance between high thermal activity and significant synergy between light and heat in a catalytic process. The system with 10% indium is thus able to improve the thermal catalytic process through the use of light, providing an intensification procedure for the classic process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

DESI Massive Poststarburst Galaxies at z ~ 1.2 Have Compact Structures and Dense Cores

Poststarburst galaxies (PSBs) are young quiescent galaxies that have recently experienced a rapid decrease in star formation, allowing us to probe the fast-quenching period of galaxy evolution. In this work, we obtained Hubble Space Telescope (HST)/WFC3 F110W imaging to measure the sizes of 171 massive (log(M $\ast$ /M ⊙ ) ~ 11) spectroscopically identified PSBs at 1 < z 1.3 selected from the DESI Survey Validation luminous red galaxy sample. This statistical sample constitutes an order of magnitude increase from the ~20 PSBs with space-based imaging and deep spectroscopy. We perform structural fitting of the target galaxies with pysersic and compare them to quiescent and star-forming galaxies in the 3D-HST survey. We find that these PSBs are more compact than the general population of quiescent galaxies, lying systematically ~0.1 dex below the established size–mass relation. However, their central surface mass densities are similar to those of their quiescent counterparts (log(Σ 1kpc /(M ⊙ kpc -2 ))~10.1). These findings are easily reconciled by later ex situ growth via minor mergers or a slight progenitor bias. These PSBs are round in projection (b/a median ~ 0.8), suggesting that they are primarily spheroids, not disks, in 3D. We find no correlation between the time since quenching and light-weighted PSB sizes or central densities. This disfavors apparent structural growth due to the fading of centralized starbursts in this galaxy population. Instead, we posit that the fast quenching of massive galaxies at this epoch occurs preferentially in galaxies with preexisting compact structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system

Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.

Additive manufacturing↗
DOE OSTI2024

Ensemble variational Fokker-Planck methods for data assimilation

Particle flow filters solve Bayesian inference problems by smoothly transforming a set of particles into samples from the posterior distribution. Particles move in state space under the flow of an McKean-Vlasov-Itˆo process. This work introduces the Variational Fokker-Planck (VFP) framework for data assimilation, a general approach that includes previously known particle flow filters as special cases. The McKean-Vlasov-Itˆo process that transforms particles is defined via an optimal drift that depends on the selected diffusion term. It is established that the underlying probability density - sampled by the ensemble of particles - converges to the Bayesian posterior probability density. For a finite number of particles the optimal drift contains a regularization term that nudges particles toward becoming independent random variables. Based on this analysis, we derive computationally-feasible approximate regularization approaches that penalize the mutual information between pairs of particles, and avoid particle collapse. Moreover, the diffusion plays a role akin to a particle rejuvenation approach that aims to alleviate particle collapse. The VFP framework is very flexible. Different assumptions on prior and intermediate probability distributions can be used to implement the optimal drift, and localization and covariance shrinkage can be applied to alleviate the curse of dimensionality. A robust implicit-explicit method is discussed for the efficient integration of stiff McKean- Vlasov-Itˆo processes. Here, the effectiveness of the VFP framework is demonstrated on three progressively more challenging test problems, namely the Lorenz ’63, Lorenz ’96 and the quasi-geostrophic equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

New NDA Methods for Thorium Fuel Cycle Safeguards (Final Report)

This project developed portable Neutron Resonance Transmission Analysis (pNRTA) as a new non-destructive assay (NDA) method for thorium fuel cycles safeguards and other applications where multiple isotopes must be measured when present together. pNRTA leverages epithermal neutron resonances to assay multiple safeguards-relevant isotopes (e.g., 233 U and 235 U) when they are present together in a sample. Existing techniques are challenged by this task, driving the need for new active interrogation methods. With selected detectors, pNRTA works in high gamma-ray backgrounds from fission and activation products and 232 U progeny expected in thorium fuel cycle samples. This project leveraged a pNRTA system developed at Pacific Northwest National Laboratory (PNNL) and collaboration with the Massachusetts Institute of Technology (MIT). The system uses a commercially available deuterium-tritium (DT) neutron generator at short standoff (2 m). Key achievements in this project included: first-of-a-kind pNRTA quantitative measurements of 233 U oxide samples, an assessment of neutron detector technologies suitable for pNRTA in high gamma-ray background environments, experimentally demonstrating quantitative assay of samples containing 233 U and 235 U, and modeling studies showing the applicability of pNRTA to a wide range of material forms. Further, a custom algorithm was developed at MIT, which provided mean bias of 9% and relative standard deviation of 36% in assaying 233 U, 235 U, 238 U, and 232 Th content in eight measured samples. These outcomes form a solid technical basis for pNRTA as a new promising capability for international safeguards verification that is portable, non-destructive, quantitative, and isotopic specific.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Broadband 920-nm mirror thin film damage competition

The 2023 Laser Damage conference thin-film damage competition was devoted to a survey on the state-of-the-art broadband near-IR multilayer dielectric (MLD) mirrors designed for ultra-short pulsed laser applications. The requirements for the coatings were a minimum reflection of 99.5% at 45-deg incidence angle for S-polarization from 830 nm to 1010 nm and group delay dispersion (GDD) < ± 50fs 2 . The participants were allowed to select the coating materials, coating design, and coating deposition method. Samples were damage tested at a single testing facility to enable direct comparison among the participants using a 25 ± 5 fs optical parametric chirped-pulse amplification (OPCPA) laser system operating at 5 Hz. The testing results from this set of 37 samples showed that dense coatings by ion-beam sputtering (IBS), magnetron sputtering (MS), and electron-beam ion assisted deposition (e-beam IAD) exhibited highest damage initiation onset (laser-induced damage threshold or LIDT) while e-beam coatings were low performers. In addition, multilayer coatings using tantala and/or hafnia as high index materials were top performers. Furthermore, this competition included for the first time the measurement of the damage growth onset (laser-induced damage growth threshold or LDGT). This latter performance metric plays an important role in establishing the safe operational conditions for larger aperture ultrashort pulsed lasers. Information pertaining to the morphology of the damage sites and their evolution under subsequent exposure to different laser fluences leading to damage growth is presented. Finally, not all coating samples in the survey met the GDD requirements stated above and associated measurements are discussed in the context of the present and past thin-film damage competitions focused on similar broadband near-IR MLD coatings.

23 broadband high reflectors↗
DOE OSTI2025

Improving cellulose attribution by selectively removing yeast glucans from grain fermentation intermediates

Abstract This article presents an industry-relevant method for quantifying cellulose in mixed substrate samples. We built upon the cellulosic glucan measurement proposed by Sluiter et al. (2021) to investigate significant cellulose loss under cold caustic conditions, which has hindered the establishment of an industrially relevant method. To overcome this issue, we used dimethyl sulfoxide (DMSO) as an alternative solvent, which avoids mercerization reactions (Budtova and Navard 2015) but may leave some resistant starch in the sample. Treatment via an enzyme mixture removed starch and conformed to US Environmental Protection Agency (EPA) guidance on cellulose measurement by establishing quantitative de-starching via nuclear magnetic resonance (EPA 2022). The selective removal of yeast beta-glucan was accomplished using a commercially available Zymolyase. Our results demonstrated excellent reproducibility, with coefficients of variance of 7.14% or less, when measuring cellulose in low cellulose content samples. The method was tested on relevant lab and plant samples, showing an average 0.9% ethanol derived from the conversion of cellulose when cellulase was added to the process and zero response when no cellulase was added. These findings indicate that a cellulose method using DMSO and pullulanase provides a complete and accurate view of cellulose content, composition, and conversion in industrial fermentation processes.

Sluiter, Justin [National Renewable Energy Laborat↗
DOE OSTI2024

3D Printing of Functional Hydrogel Devices for Screenings of Membrane Permeability and Selectivity

Developing a fundamental understanding of the effects of varying ligand chemistries on mass transport rates is key to designing membranes with solute-specific selectivity. While permeation cells offer a robust method to characterize membrane performance, they are limited to assessing a single membrane chemistry or salt solution per test. As a result, investigating the effects of varying ligand chemistries on membrane performance can be a tedious process, involving both the preparation of multiple samples and numerous, time-consuming permeation tests. This study uses digital light processing (DLP) 3D printing to fabricate a millifluidic flow-based permeation device made from a hydrogel active ester network that can be easily functionalized with ion-selective ligands. Without the need for bonding or assembly steps, ligands can be introduced and tested in the permeation device by simply injecting a small volume of a ligand solution. Various salt concentrations and molecular species can be cycled through a single device by switching the solution feeding into the salt reservoir, thereby reducing the number of samples needed for permeability and selectivity screenings. This research sets the groundwork for formulation development and postprocessing methods to 3D-print functional millifluidic devices capable of assessing solute selectivity in membranes and polymer adsorbents for aqueous separations. In this work, comparable salt permeability trends were observed with both 3D-printed devices and traditional assays. Devices were functionalized with an imidazole ligand to investigate salt permeability and selectivity of monovalent and divalent salts. Measurements showed increasing permeability for monovalent salts (NaCl) relative to divalent salts (MgCl 2 , CuCl 2 ) in functionalized membranes, with higher monovalent/divalent selectivity at increasing imidazole grafting densities. Here, the methods and findings described here represent a step toward developing higher-throughput methods with 3D-printed devices for screening the effects of ligand chemistry on mass transport rates in membrane materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sampling Size Optimization for Bioburden Density Estimation in Planetary Protection

Planetary protection (PP) is a discipline that focuses on minimizing the biological contamination of spacecraft to ensure compliance with international policy. Precise estimation of bioburden - the total number of microbes in or on spacecraft hardware – and the bioburden density are of utmost importance for PP. Such estimation is the way concordance with requirements is demonstrated, and it is critical for quantifying the potential risk of inadvertently contaminating other planetary bodies. Although a suite of molecular techniques have been used to thoroughly characterize and profile the microbiome of various cleanroom environments and spacecraft, the gold standard remains the physical enumeration of microbes via culturing of samples directly taken from spacecraft and associated surfaces. However, due to technical, budgetary, and programmatic constraints, only a manageable portion (around 10%) of the entire spacecraft surface is directly sampled with cotton swabs or wipes. To generate the bioburden current best estimate (CBE) for components not directly verifiable, the accepted approach is to apply a NASA-defined bioburden estimate based on the components’ manufacturing or assembly environment. This approach utilizes a prespecified bioburden density estimation that applies a maximum value across the total surface area of the specified component. For hardware components that underwent similar assembly processes, an implied bioburden is adopted for all components, based on a direct verification of a representative component within the same lot. Once all components have a CBE, the bioburden estimates are generated. In previous publication [ 1], we have shown that statistical risks quantifying the accuracy of the estimates for sampled, prespecified, and implied components can be derived and ranked. For mean squared error (MSE) function, the risks are available analytically and hence a cost function can be obtained to optimize the risks with respect to the sampling area and sampling cost. Since the sampling area and sampling cost are two complimentary variables, their sum will have a well-defined minimum. This paper presents the multivariate optimization of the integrated risk of an empirical Bayes estimator to determine the optimal sampling schedule for a given number of components. It is assumed that given a number of components, N, the bioburden density for each component can either be sampled, implied, or prespecified. The multivariate optimization searches through different options to sample, imply or prespecify the bioburden density for a component, and account for the component’s surface area and cost of sampling. The idea of the optimization is based on the observation that the statistical risk of using an estimator is a monotonically decreasing function of the sampled area. The larger the sampled area, the lower the risk of using the estimator as the estimator becomes more and more accurate as the sampling area increases. On the other hand, the cost of sampling is monotonically increasing as the sampled surface grows. This makes the risk and total cost of sampling complimentary variables which can be counterbalanced to achieve an optimal overall value with respect to the sampled surface. In this paper, the integrated risk has been used to quantify the accuracy of the estimator. This risk has been selected because it depends on neither the true value of the parameter nor on the collected data. The cost of each sample was also available to obtain the total cost of sampling of N components. The paper will present the results based on computer-simulated data as well as the data collected during the InSight mission. The computer-simulated data have N components with randomly generated total areas and each component assigned to one of the three categories according to the method of estimating of bioburden density: sampled, implied, or prespecified. The cost of sampling is also available. The cost of sampling is estimated based on a cost model provided by the planetary protection group at JPL. For this paper, the overall cost was assumed to be a linear function of exposure. The optimization process finds the allocation of the components to the three categories that minimizes the tradeoff between integrated risk and total cost. For the InSight data, a set of components is selected representing all three categories, and optimization is performed to determine if the performed allocation was optimal or if a better allocation could have been obtained. To the best of our knowledge, this work is the first attempt not only perform an accurate estimation of bioburden density but also do it in an optimal way.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Forward modeling fluctuations in the DESI LRGs target sample using image simulations

We use the forward modeling pipeline, Obiwan, to study the imaging systematics of the Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI). Imaging systematics refers to the false fluctuation of galaxy densities due to varying observing conditions and astrophysical foregrounds corresponding to the imaging surveys from which DESI LRG target galaxies are selected. We update the Obiwan pipeline, which we previously developed to simulate the optical images used to target DESI data, to further simulate WISE images in the infrared. This addition allows simulating the DESI LRGs sample, which utilizes WISE data in the target selection. Deep DESI imaging data combined with a method to account for biases in their shapes is used to define a truth sample of potential LRG targets. We inject these data evenly throughout the DESI Legacy Imaging Survey footprint at declinations between -30 and 32.375 degrees. We simulate a total of 15 million galaxies to obtain a simulated LRG sample (Obiwan LRGs) that predicts the variations in target density due to imaging properties. We find that the simulations predict the trends with depth observed in the data, including how they depend on the intrinsic brightness of the galaxies. We observe that faint LRGs are the main contributing source of the imaging systematics trend induced by depth. We also find significant trends in the data against Galactic extinction that are not predicted by Obiwan. These trends depend strongly on the particular map of Galactic extinction chosen to test against, implying systematic contamination in the Galactic extinction maps is a likely root cause (e.g., Cosmic-Infrared Background, dust temperature correction). We additionally observe a morphological change of the DESI LRGs population evidenced by a correlation between OII emission line average intensity and the size of the z-band PSF. This effect most likely results from uncertainties in background subtraction. The detailed findings we present should be used to guide any observational systematics mitigation treatment for the clustering of the DESI LRGs sample.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Energy Survey: Galaxy sample for the baryonic acoustic oscillation measurement from the final dataset

In this paper, we present and validate the galaxy sample used for the analysis of the baryon acoustic oscillation (BAO) signal in the Dark Energy Survey (DES) Y6 data. The definition is based on a color and redshift-dependent magnitude cut optimized to select galaxies at redshifts higher than 0.6, while ensuring a high-quality photo- z determination. The optimization is performed using a Fisher forecast algorithm, finding the optimal i -magnitude cut to be given by i < 19.64 + 2.894 z ph . For the optimal sample, we forecast an increase in precision in the BAO measurement of ∼ 25 % with respect to the Y3 analysis. Our BAO sample has a total of 15,937,556 galaxies in the redshift range 0.6 < z ph < 1.2 , and its angular mask covers 4 , 273.42 deg 2 to a depth of i = 22.5 . We validate its redshift distributions with three different methods: directional neighborhood fitting algorithm (DNF), which is our primary photo- z estimation; direct calibration with spectroscopic redshifts from VIPERS, which is a spectroscopic galaxy sample that overlaps with our BAO sample and is complete within our selection cuts; and clustering redshift using SDSS galaxies. The fiducial redshift distribution is a combination of these three techniques performed by modifying the mean and width of the DNF distributions to match those of VIPERS and clustering redshift. In this paper, we also describe the methodology used to mitigate the effect of observational systematics, which is analogous to the one used in the Y3 analysis. This paper is one of the two dedicated to the analysis of the BAO signal in DES Y6. In its companion paper, we present the angular diameter distance constraints obtained through the fitting to the BAO scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Clustering analysis of medium-band selected high-redshift galaxies

Next-generation large-scale structure spectroscopic surveys will probe cosmology at high redshifts (2.3 < z < 3.5), relying on abundant galaxy tracers such as Lyα emitters (LAEs) and Lyman break galaxies (LBGs). Medium-band photometry has emerged as a potential technique for efficiently selecting these high-redshift galaxies. In this work, we present clustering analysis of medium-band selected galaxies at high redshift, utilizing photometric data from the Intermediate Band Imaging Survey (IBIS) and spectroscopic data from the Dark Energy Spectroscopic Instrument (DESI). We interpret the clustering of such samples using both Halo Occupation Distribution (HOD) modeling and a perturbation theory description of large-scale structure. Our modeling indicates that the current target sample is composed from an overlapping mixture of LAEs and LBGs with emission lines. Despite differences in target selection, we find that the clustering properties are consistent with previous studies, with correlation lengths r 0 ≃ 3-4 h -1 Mpc and a linear bias of b ∼ 1.8-2.5. Finally, we discuss the simulation requirements implied by these measurements and demonstrate that the properties of the samples would make them excellent targets to enhance our understanding of the high-z universe.

79 ASTRONOMY AND ASTROPHYSICS↗