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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Physical Properties and Gas Hydrate at a Near-Seafloor Thrust Fault, Hikurangi Margin, New Zealand

The Papaku Fault Zone, drilled at International Ocean Discovery Program (IODP) Site U1518, is an active splay fault in the frontal accretionary wedge of the Hikurangi Margin. In logging-while-drilling data, the 33-m-thick fault zone exhibits mixed modes of deformation associated with a trend of downward decreasing density, P-wave velocity, and resistivity. Methane hydrate is observed from ~30 to 585 m below seafloor (mbsf), including within and surrounding the fault zone. Hydrate accumulations are vertically discontinuous and occur throughout the entire logged section at low to moderate saturation in silty and sandy centimeter-thick layers. In this paper, we argue that the hydrate distribution implies that the methane is not sourced from fluid flow along the fault but instead by local diffusion. This, combined with geophysical observations and geochemical measurements from Site U1518, suggests that the fault is not a focused migration pathway for deeply sourced fluids and that the near-seafloor Papaku Fault Zone has little to no active fluid flow.

58 GEOSCIENCES↗
DOE OSTI2023

Linking Stream Chemistry to Subsurface Redox Architecture

As drinking-water scarcity grows worldwide, we need to improve predictions of the quantity and quality of our water resources. An overarching problem for model improvement is that we do not know the geological structure of aquifers in sufficient detail. In this work, we demonstrate that mineral-water reactions imprint structure in the subsurface that impacts the flow and transport of some chemical species. Specifically, pyrite, a ubiquitous mineral, commonly oxidizes and depletes in the upper layers of the weathering profile in most humid watersheds, only remaining at depths of meters. We hypothesize that variations in concentrations (C) of pyrite-derived sulfate released into rivers as a function of discharge (q) reflect the rate-limiting step and depth of pyrite-oxidizing layers. We found that log C - log q behaviors thus differ in small and large watersheds in the Susquehanna River Basin as well as in selected watersheds in the Western United States. Although coal mining changes pyrite oxidation from closed to open system with respect to O 2 , patterns in stream chemistry as a function of discharge are consistent with deep and shallow pyrite oxidation zones in small and large watersheds respectively. Therefore, understanding the subsurface patterns of mineral reactions and how they affect the architecture of aquifers will elucidate patterns of changing river chemistry and our ability to manage water resources in the future under accelerated land use and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Surface Energy Dynamics and Canopy Structural Properties in Intact and Disturbed Forests in the Southern Amazon

The Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study's objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI-derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second-growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R 2 : 0.41–0.87) than in the wet season (R 2 : 0.18–0.49). Forest and cropland or pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R 2 : 0.01–0.43) than in the least disturbed forests (R 2 < 0.05). Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in Amazonian degraded forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $\mathcal{O}$(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}$(n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}$(n log n) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Data-driven organic solubility prediction at the limit of aleatoric uncertainty

Abstract Small molecule solubility is a critically important property which affects the efficiency, environmental impact, and phase behavior of synthetic processes. Experimental determination of solubility is a time- and resource-intensive process and existing methods for in silico estimation of solubility are limited by their generality, speed, and accuracy. This work presents two models derived from the FASTPROP and CHEMPROP architectures and trained on BigSolDB which are capable of predicting solubility at arbitrary temperatures for a wide range of small molecules in organic solvent. Both extrapolate to unseen solutes 2–3 times more accurately than the current state-of-the-art model and we demonstrate that they are approaching the aleatoric limit (0.5–1$$\log S$$ log S ) of available test data, suggesting that further improvements in prediction accuracy require more accurate datasets. The FASTPROP-derived model (called FASTSOLV) and the CHEMPROP-based model are open source, freely accessible via a Python package and web interface, highly reproducible, and up to 2 orders of magnitude faster than current alternatives.

Science & Technology - Other Topics↗
DOE OSTI2018

Impacts of glass composition, pH, and temperature on glass forward dissolution rate

Nuclear waste glasses dissolve at the forward dissolution rate (r f ) in very dilute aqueous solutions, which can isolate the impact of the glass composition from solution feedback and alteration product effects. While it has long been known that pH and temperature (T) strongly impact r f , the impacts of glass composition have remained uncertain. In this work, r f data from 19 nuclear waste glasses were used with the aim of identifying the effect of glass composition on r f . The r f values were modeled as: r f = k 0 10 –ηpH exp(-E a /RT), with k 0 , η, E a , and R, respectively, being the intrinsic rate constant, pH coefficient, apparent activation energy, and gas constant. However, no predictive correlation could be established between the individual model parameters (log[k 0 ], η, and E a ) and glass composition for the glasses considered in this study, an outcome that was attributed to the strong positive correlation between the log[$k_0$] and E a parameters. Therefore, a model was fitted directly to the combined r f from all 19 glasses. This approach showed that 90% of the variation in r f data could be accounted for solely by T and pH effects. Therefore, any composition effects must be relatively small. After normalizing for differences in pH and T, the only notable differences in r f between the glasses were found to correlate with variations in the fraction of glass forming tetrahedra contributed by tetrahedral boron, $f$([ 4 ]B), with an abrupt threshold at a high value of $f$([ 4 ]B) (~0.22), where higher r f are predicted with no discernable composition effects below the threshold.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tunable discrete scale invariance in transition-metal pentatelluride flakes

Log-periodic quantum oscillations discovered in transition-metal pentatelluride give a clear demonstration of discrete scale invariance (DSI) in solid-state materials. The peculiar phenomenon is convincingly interpreted as the presence of two-body quasi-bound states in a Coulomb potential. However, the modifications of the Coulomb interactions in many-body systems having a Dirac-like spectrum are not fully understood. Here, we report the observation of tunable log-periodic oscillations and DSI in ZrTe 5 and HfTe 5 flakes. By reducing the flakes thickness, the characteristic scale factor is tuned to a much smaller value due to the reduction of the vacuum polarization effect. The decreasing of the scale factor demonstrates the many-body effect on the DSI, which has rarely been discussed hitherto. Furthermore, the cut-offs of oscillations are quantitatively explained by considering the Thomas-Fermi screening effect. Finally, our work clarifies the many-body effect on DSI and paves a way to tune the DSI in quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shape-persistent ladder molecules exhibit nanogap-independent conductance in single-molecule junctions

Molecular electronic devices require precise control over the flow of current in single molecules. However, the electron transport properties of single molecules critically depend on dynamic molecular conformations in nanoscale junctions. Here, in this work, we report a unique strategy for controlling molecular conductance using shape-persistent molecules. Chemically diverse, charged ladder molecules, synthesized via a one-pot multicomponent ladderization strategy, show a molecular conductance (d[log( G/G 0 )]/d x ≈ -0.1 nm -1 ) that is nearly independent of junction displacement, in stark contrast to the nanogap-dependent conductance (d[log( G/G 0 )]/d x ≈ -7 nm -1 ) observed for non-ladder analogues. Ladder molecules show an unusually narrow distribution of molecular conductance during dynamic junction displacement, which is attributed to the shape-persistent backbone and restricted rotation of terminal anchor groups. These principles are further extended to a butterfly-like molecule, thereby demonstrating the strategy's generality for achieving gap-independent conductance. Overall, our work provides important avenues for controlling molecular conductance using shape-persistent molecules. Achieving robust and controllable conductance in single-molecule junctions is challenging due to the dynamic nature of molecular conformations that fluctuate over operational timescales. A strategy using shape-persistent molecules has now been developed that demonstrates nearly junction-displacement-independent conductance, providing a stable solution for single-molecule electronic properties.

molecular electronics↗
DOE OSTI2020

A microbiological survey of handwashing sinks in the hospital built environment reveals differences in patient room and healthcare personnel sinks

Handwashing sinks and their associated premise plumbing are an ideal environment for pathogen-harboring biofilms to grow and spread throughout facilities due to the connected system of wastewater plumbing. This study was designed to understand the distribution of pathogens and antibiotic resistant organisms (ARO) within and among handwashing sinks in healthcare settings, using culture-dependent methods to quantify Pseudomonas aeruginosa, opportunistic pathogens capable of growth on a cefotaxime-containing medium (OPP-C), and carbapenem-resistant Enterobacteriaceae (CRE). Isolates from each medium identified as P. aeruginosa or Enterobacteriaceae were tested for susceptibility to aztreonam, ceftazidime, and meropenem; Enterobacteriaceae were also tested against ertapenem and cefotaxime. Isolates exhibiting resistance or intermediate resistance were designated ARO. Pathogens were quantified at different locations within handwashing sinks and compared in quantity and distribution between healthcare personnel (HCP) and patient room (PR) sinks. ARO were compared between samples within a sink (biofilm vs planktonic samples) and between sink types (HCP vs. PR). The drain cover was identified as a reservoir within multiple sinks that was often colonized by pathogens despite daily sink cleaning. P. aeruginosa and OPP-C mean log 10 CFU/cm 2 counts were higher in p-trap and tail pipe biofilm samples from HCP compared to PR sinks (2.77 ± 2.39 vs. 1.23 ± 1.62 and 5.27 ± 1.10 vs. 4.74 ± 1.06) for P. aeruginosa and OPP-C, respectively. P. aeruginosa and OPP-C mean log 10 CFU/ml counts were also higher (p < 0.05) in HCP compared to PR sinks p-trap water (2.21 ± 1.52 vs. 0.89 ± 1.44 and 3.87 ± 0.78 vs. 3.21 ± 1.11) for P. aeruginosa and OPP-C, respectively. However, a greater percentage of ARO were recovered from PR sinks compared to HCP sinks (p < 0.05) for Enterobacteriaceae (76.4 vs. 32.9%) and P. aeruginosa (25.6 vs. 0.3%). This study supports previous work citing that handwashing sinks are reservoirs for pathogens and ARO and identifies differences in pathogen and ARO quantities between HCP and PR sinks, despite the interconnected premise plumbing.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Antimicrobial properties of a multi-component alloy

High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates metallic alloys comprised of several metals which individually possess antimicrobial properties, with the target of achieving broad-spectrum, rapid sanitation through synergistic activity. An entropy-motivated stabilization paradigm is proposed to prepare scalable alloys of copper, silver, nickel and cobalt. Using combinatorial sputtering, thin-film alloys were prepared on 100 mm wafers with ≈50% compositional grading of each element across the wafer. The films were then annealed and investigated for alloy stability. Antimicrobial activity testing was performed on both the as-grown alloys and the annealed films using four microorganisms—Phi6, MS2, Bacillus subtilis and Escherichia coli —as surrogates for human viral and bacterial pathogens. Testing showed that after 30 s of contact with some of the test alloys, Phi6, an enveloped, single-stranded RNA bacteriophage that serves as a SARS-CoV-2 surrogate, was reduced up to 6.9 orders of magnitude (> 99.9999%). Additionally, the non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E. coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7 log reduction in activity after 30, 20 and 10 min, respectively. Antimicrobial activity in the alloy samples showed a strong dependence on the composition, with the log reduction scaling directly with the Cu content. Concentration of Cu by phase separation after annealing improved activity in some of the samples. The results motivate a variety of themes which can be leveraged to design ideal antimicrobial surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum discriminator for binary classification

Abstract Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-dimensional spaces. The quantum discriminator is trained using a quantum-classical hybrid algorithm in $$\mathcal {O}(N\log N)$$ O ( N log N ) time, and inferencing is performed on a universal quantum computer in $$\mathcal {O}(N)$$ O ( N ) time. The quantum discriminator takes as input the binary features extracted from a given datum along with a prediction qubit, and outputs the predicted label. We analyze its performance on the Iris and Bars and Stripes data sets, and show that it can attain 99% accuracy in simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Effects of Eucalyptus wood and leaf litter on saproxylic insects in the southeastern United States

Although Eucalyptus is widely planted outside its native range for timber and pulp production, the effects of these exotic plantations on biodiversity relative to native semi-natural forests or plantations of native tree species remain incompletely understood. Here, we compare the diversity of saproxylic beetles (Coleoptera) and true bugs (Hemiptera) between non-native Eucalyptus benthamii Maiden and Cambage (Camden white gum) and native Pinus taeda L. (loblolly pine) stands on the upper Coastal Plain of South Carolina, U.S.A. We sampled insects emerging from logs of both species placed in both stand types after 1, 2, 6, and 12 months in the field. Beetle and true bug richness and diversity were both significantly lower from eucalypt than from pine wood. Moreover, the two communities were compositionally distinct. Whereas pine supported many species of host-specific phloeoxylophagous beetles, most species collected from eucalypts were mycophagous or predatory taxa capable of utilizing a wide range of hosts. Species richness did not differ between logs placed in eucalypt vs. pine stands but Shannon’s diversity was significantly higher in the eucalypt stands, possibly due to greater sun exposure in the latter. Contrary to a previous study, we found no support for the idea that eucalypt litter reduces the diversity of saproxylic insects. Our findings add to the growing body of evidence that non-native plantations are less favorable to biodiversity than those consisting of native tree species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

405 nm light microbicidal efficacy on Treponema pallidum spiked in ex vivo human platelets

Abstract Pathogen reduction technologies using chemicals and or UV light have been demonstrated to improve the safety of ex vivo platelets from infectious diseases. However, UV light exposure also may affect the treated products, depending on wavelength and exposure. Alternatively, visible spectra 405 nm violet-blue light has broad-spectrum microbicidal activity. Here we tested the effect of 405 nm light onTreponema pallidum, the bacterium that causes syphilis. We contaminated platelets stored in plasma with two treponemal concentrations (low and high titers) and treated an aliquot with 270 J/cm 2 dose (irradiance = 15 mW/cm 2 ) of 405 nm light while another aliquot remained untreated. Next, we inoculated intradermally an aliquot of both samples into rabbits. Rabbits inoculated with untreated samples developed syphilis while animals inoculated with light-treated samples did not. Thus, inactivation was demonstrated to the limit of detection of the bioassay. We estimated > 2 log 10 and > 4 log 10 reduction in the low and high dose studies, respectively. These results provide proof-of-concept that 405 nm light is effective in reducing syphilis risk in ex vivo platelets.

Science & Technology - Other Topics↗
DOE OSTI2018

Emerging investigator series: methylmercury speciation and dimethylmercury production in sulfidic solutions

Alkylated mercury species (monomethylmercury, MeHg, and dimethylmercury, DMeHg) exhibit significant bioaccumulation, and pose significant risks to ecosystems and human health. Although decades of research have been devoted to understanding MeHg formation and degradation, little is known about the DMeHg formation in aquatic systems. Here, we combine complementary experimental and computational approaches to examine MeHg speciation and DMeHg formation in sulfidic aqueous solutions, with an emphasis on the formation and decomposition of the binuclear bis(methylmercuric(II)) sulfide complex (CH 3 Hg) 2 S. Experimental data indicate that the reaction 2CH 3 Hg + + HS- $→\atop{←}$ (CH 3 Hg) 2 S + H + has a log K = 26.0 ± 0.2. Thus, the binuclear (CH 3 Hg) 2 S complex is likely to be the dominant MeHg species under high MeHg concentrations typically used in experimental investigations of MeHg degradation by sulfate-reducing bacteria (SRB). Our finding of a significant abiotic removal mechanism for MeHg in sulfidic solutions through the formation of relatively insoluble (CH 3 Hg) 2 S suggests careful reexamination of reported “oxidative demethylation” of MeHg by SRB and perhaps other obligate anaerobes. We provide evidence for slow decomposition of (CH 3 Hg) 2 S to DMeHg and HgS, with a first-order rate constant k = 1.5 ± 0.4 × 10 -6 h -1 . Quantum chemical calculations suggest that the reaction proceeds by a novel mechanism involving rearrangement of the (CH 3 Hg) 2 S complex facilitated by strong Hg–Hg interactions that activate a methyl group for intramolecular transfer. Predictions of DMeHg formation rates under a variety of field and laboratory conditions indicate that this pathway for DMeHg formation will be significant in laboratory experiments utilizing high MeHg concentrations, favoring (CH 3 Hg) 2 S formation. Finally, in natural systems with relatively high MeHg/[H 2 S] T ratios (the oxic/anoxic interface, for example), DMeHg production may be observed, and warrants further investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Impact of dissolved organic matter on mercury and methylmercury sorption to activated carbon in soils: implications for remediation

Activated carbon (AC) amendments have shown promise in reducing inorganic mercury (Hg(II) complexes, “Hg”) and methylmercury (MeHg) risk in contaminated soils. However, the effectiveness of AC in Hg and MeHg immobilization has varied among studies, suggesting that site biogeochemistry might dictate efficacy. In this study, we examined the effect of dissolved organic matter (DOM) on MeHg and Hg sorption to AC. We evaluated the impact of Suwannee River Humic Acid (SRHA) on sorption to AC directly using an isotherm approach and in a soil/AC mixture using slurry microcosms. Aqueous sorption coefficients to AC (log K AC ) for Hg-SRHA and MeHg-SRHA complexes were one to two orders of magnitude lower (Hg-SRHA = 4.53, MeHgSRHA = 4.35) than those for chloride complexes (HgCl 2 = 6.55, MeHgCl = 4.90) and more closely resembled the log K AC of SRHA (3.64). In anoxic, sulfidic soil slurries, the K AC for sulfide species appeared stronger than for chloride or SRHA species for both Hg and MeHg. AC significantly reduced porewater concentrations of both ambient MeHg and a fresh Me 199 Hg spike, and the addition of up to 60 mg L -1 SRHA did not reduce sorption to AC. The AC also reduced ambient Hg and 201 Hg porewater concentrations, but as SRHA concentration increased, the magnitude of solid phase sorption decreased. Speciation modeling revealed that SRHA may have impacted Hg distribution to the solid phase by reducing HgS precipitation. In conclusion, this study highlights the need for site-specific evaluation of AC efficacy and the value in developing biogeochemical models of AC performance for Hg control.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Coupling of an atmospheric pressure microplasma ionization source with an Orbitrap Fusion Lumos Tribrid 1M mass analyzer for ultra-high resolution isotopic analysis of uranium

The coupling of a combined atomic and molecular (CAM) ionization source, the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma to an Orbitrap Fusion Lumos Tribrid 1M mass spectrometer is described as a significant step towards the elimination of isobaric interferences in elemental/isotopic mass spectrometry. The developed setup permits broadband, elemental mass spectra to be easily acquired at a mass resolution (m/Δm) of 500 000. A new ion source housing and integrated control system provide enhanced stability and sensitivity of the microplasma ion source in comparison to the previous designs. Detailed evaluation of the ion sampling parameters provided insight into differences with other Orbitrap platforms leading to a 5-fold increase in resolution in elemental/isotopic analysis. The roles of data acquisition/ion processing parameters were evaluated with regards to the precision and accuracy of 235 U/ 238 U isotope ratios. Significantly, the precision of those measurements improves as a function of pre-set mass resolution up to a value of 1 000 000, where a precision of 0.086% RSD is obtained for uranium concentrations of 100 ng mL− 1 . Analytical response curves (log–log), acquired for the two uranium isotopes at a resolution of 120 000 were linear (R 2 = 0.9969) over more than 5 orders of magnitude, with limits of detection of 1 pg mL −1 for U. Based on 60 μL injection volumes, this represents a mass of 70 fg 235 U. These figures of merit satisfy International Target Values (ITVs) for Measurement Uncertainties in Safeguarding Nuclear Materials set by the International Atomic Energy Agency (IAEA). Ultimately, and totally unique from beam-type instruments, the move to the higher-resolution Orbitrap platform provides all of the benefits of high mass resolution, with no penalties in sensitivity, precision, or dynamic range for uranium analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗