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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Tribology behavior on scratch tests: Effects of yield strength

In this paper, a three-dimensional (3D) scratch model is proposed to investigate the effects of yield strength of both coatings and substrates. With the help of combined Coulomb and plastic friction, the obtained results comprehensively interpret the experimental phenomena in most metals that with the growth of hardness after heat treatment the scratch friction coefficient (SFC) increases. This interpretation could not be done before. Scratch tests on the surface with or without the coating are discussed. Without the coating the SFC increases due to the decrease of the area with plastic slippage and/or the increase of friction stress during the increase of the yield strength in the material. With a softer substrate the friction stress decreases but the SFC increases, which is caused by the growth of the entire contact area and surface deformation. Conversely, with a stronger substrate the SFC decreases due to an intensified plastic slippage In conclusion, the obtained results pave a new way to understanding the effects of yield strength on scratch tests, interpret experimental phenomena, and should be helpful for an optimum design in experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Dependence of nuclear quadrupole resonance transitions on the electric field gradient asymmetry parameter for nuclides with half-integer spins

Allowed transition energies and eigenstate expansions have been calculated and tabulated in numerical form as functions of the electric field gradient asymmetry parameter for the zero field Hamiltonian of quadrupolar nuclides with I=3/2,5/2,7/2, and 9/2. These results are essential to interpret nuclear quadrupole resonance (NQR) spectra and extract accurate values of the electric field gradient tensors. Applications of NQR methods to studies of electronic structure in heavy element systems are proposed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions

The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A five-dimensional perspective on the Klein–Gordon equation

We discuss the Klein–Gordon (KG) equation using a path-integral approach in 5D space–time. We explicitly show that the KG equation in flat space–time admits a consistent probabilistic interpretation with positively defined probability density. However, the probabilistic interpretation is not covariant. In the non-relativistic limit, the formalism reduces naturally to that of the Schrödinger equation. We further discuss other interpretations of the KG equation (and their non-relativistic limits) resulting from the 5D space–time picture. Finally, we apply our results to the problem of hydrogenic spectra and calculate the canonical sum of the hydrogenic atom.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2018

Ectopic serotonin production in β-cell specific transgenic mice

Highlights: • β-cell specific transgenic mice showed the ectopic serotonin production in β-cells. • hGH induced the ectopic serotonin production via PRLR-Stat5-Tph1 pathway in β-cell. • Transgenic mice containing the hGH cassette should be interpreted carefully. Genetically modified mice have been widely used in the field of β-cell research. However, analysis of results gathered using genetically modified organisms should be interpreted carefully as the results may be confounded by several factors. Here, we showed the ectopic serotonin (5-HT) production in β-cells of RIP-Cre{sup Mgn}, MIP-GFP, and MIP-Cre/ERT mice. These mice contained a human growth hormone (hGH) cassette to enhance transgene expression and hGH expression and Stat5 phosphorylation were detected in pancreatic islets of these mice. The expression level of tryptophan hydroxylase 1 (Tph1) was upregulated in pancreatic islets of transgenic mice with an hGH cassette but not in transgenic mice without an hGH cassette. Ectopic 5-HT production was not observed in β-cell-specific prolactin receptor (Prlr) knockout mice or Stat5 knockout mice crossed with RIP-Cre{sup Mgn}. We further confirmed that 5-HT production in β-cells of several transgenic mice was induced by hGH expression followed by the activation of the Prlr-Stat5-Tph1 pathway. These findings indicate that results obtained using transgenic mice containing the hGH cassette should be interpreted with care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

High-dimensional isotomics, part 2: Observations of over 100 constraints on methionine's isotome

The abundances of different isotopic forms of a compound, or isotopologues, will vary based on its physical and chemical history. The number of isotopologues increases combinatorically with the size of a molecule, and even small molecules such as amino acids have thousands of potentially observable isotopic variants. However, due to the analytical challenges of separating and observing isotopologues, only a few dimensions of isotopic diversity are routinely measured. Overcoming these challenges requires both an experimental method to observe many isotopic properties and a theoretical framework for interpreting these experiments. In Part 1, we presented such a theoretical framework; here, we demonstrate an experimental method, which we apply to methionine. Our approach uses a Q Exactive HF Orbitrap to perform several “M + N experiments”, where a sample is ionized, a subset of its isotopologues with cardinal mass N daltons greater than the unsubstituted isotopologue is selected and fragmented, and the proportions of all detectable isotopic forms of those fragment ions are quantified. We perform M + 1, M + 2, M + 3, and M + 4 experiments of a methionine sample and standard where the sample has a 100 ‰ enrichment of 13C at the methyl carbon relative to the natural 13C abundance at that position in the standard, and is otherwise identical to the standard. We observe isotopic forms of 8 fragment ion species for each version of the M + N experiment. With the assistance of a forward model of expected mass spectra, we identify isotopic peaks for each fragment ion based on observed mass and abundance, screen these for data quality, and quantify abundances for 146 unique isotopic peaks at precisions of ≈ 0.3–3 ‰. We present our direct observations and use them to reconstruct the concentrations of 19 individual singly, doubly, and triply-substituted isotopologues; doing so gives fewer constraints and broader error bars than working with the direct observations, but may be more interpretable for some applications. We also examine possibilities for measuring additional peaks, which are primarily limited by the detection limit of the Orbitrap-IRMS method. We then suggest some possible uses of our direct measurements for chemical forensics and hypothesis testing. Furthermore, our results demonstrate the diversity of isotopic constraints currently observable and interpretable for organic molecules.

58 GEOSCIENCES↗
DOE OSTI2018

A close look onto structural models and primary ligands of metallo-β-lactamases

β-Lactamases are hydrolytic enzymes capable of opening the β-lactam ring of antibiotics such as penicillin, thus endowing the bacteria that produce them with antibiotic resistance. Of particular medical concern are metallo-β-lactamases (MBLs), with an active site built around coordinated Zn cations. MBLs are pan-reactive enzymes that can break down almost all classes of β-lactams, including such last-resort antibiotics as carbapenems. They are not only broad-spectrum-reactive but are often plasmid-borne (e.g., the New Delhi enzyme, NDM), and can spread horizontally even among unrelated bacteria. Acquired MBLs are encoded by mobile genetic elements, which often include other resistance genes, making the microbiological situation particularly alarming. There is an urgent need to develop MBL inhibitors in order to rescue our antibiotic armory. A number of such efforts have been undertaken, most notably using the 3D structures of various MBLs as drug-design targets. Structure-guided drug discovery depends on the quality of the structures that are collected in the Protein Data Bank (PDB) and on the consistency of the information in dedicated β-lactamase databases. Here, we conducted a careful review of the crystal structures of class B β-lactamases, concluding that the quality of these structures varies widely, especially in the regions where small molecules interact with the macromolecules. In a number of examples the interpretation of the bound ligands (e.g., inhibitors, substrate/product analogs) is doubtful or even incorrect, and it appears that in some cases the modeling of ligands was not supported by electron density. For ten MBL structures, alternative interpretations of the original diffraction data could be proposed and the new models have been deposited in the PDB. In four cases, these models, prepared jointly with the authors of the original depositions, superseded the previous deposits. This review emphasizes the importance of critical assessment of structural models describing key drug design targets at the level of the raw experimental data. Since the structures reviewed here are the basis for ongoing design of new MBL inhibitors, it is important to identify and correct the problems with ambiguous crystallographic interpretations, thus enhancing reproducibility in this highly medically relevant area.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Socioeconomic and particulate air pollution correlates of heart disease risk

Highlights: • Heart disease risk varies with age, sex, smoking, income, education, and particulate (PM2.5) air pollution. • How these risk factors interact is unclear. • We examine correlates of self-reported heart disease risk in a large data set. • Income is negatively associated with both PM2.5 exposure and heart disease risk. • Income is an important confounder of air pollution-heart disease associations. • Nonparametric methods can quantify associations and suggest causal interpretations. How does risk of heart disease depend on age, sex, smoking, income, education, marital status, and outdoor concentrations of fine particulate matter (PM2.5)? We join data available from the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance (BRFSS) System for years 2008–2012 to US Environmental Protection Agency (EPA) data on county-specific concentrations of fine particulate matter (PM2.5) to quantify associations among these variables and to explore possible causal interpretations. Low income is identified as a direct cause of increased heart disease risk in this data set. The effect depends on age and sex: it is most pronounced for men under age 70 and for women under age 80. Income is significantly associated with all of the other variables examined and confounds the association between PM2.5 and heart disease risk. This association is significant in regression models that exclude income, but not in regression models that include it, both in the data set as a whole and in the subset of observations with PM2.5 < 15 μg/m{sup 3}. Causal directed acyclic graph (DAG) models and non-parametric model ensemble partial dependence plots confirm that higher incomes reduce heart disease risk, consistent with previous observations of socioeconomic gradients in health risks. They support interpretation of this as a robust causal relation apparent in non-parametric analyses, and hence independent of any specific parametric modeling assumptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Large isotopic variability at the micron-scale in ‘Shuram’ excursion carbonates from South Australia

Ediacaran-aged (635–541 million years ago) marine sediments contain a large negative carbon isotope (δ 13 C) excursion, in which carbonate δ 13 C values reach -12‰ (VPDB). Known as the ‘Shuram’ excursion, many workers have interpreted this δ 13 C record as an unprecedented perturbation to the global carbon cycle, leading to speculation about a causal connection to the broadly contemporaneous rise of animal life. Others have interpreted the δ 13 C signal as a product of diagenesis, thereby minimizing its relevance for understanding the evolution of metazoans. In this work, we present SEM imaging and in-situ δ 13 C and δ 18 C values measured by secondary ion mass spectrometry (SIMS) to assess these competing hypotheses in the Wonoka Formation of South Australia. Our results from the minimum of the excursion show that rounded sedimentary grains of calcite have δ 13 C values between -12.8 to -10.6‰ and δ 18 C values between 17.8 to 15.5‰ (VPDB). Euhedral dolomite that appears to have grown unimpeded in open sedimentary pore spaces also is present. These early-stage dolomites are interpreted as early authigenic in origin and have δ 13 C values that reach +5‰, requiring a formation fluid with a substantially different δ 13 C composition from basin waters or bulk sediment. Together, these results provide little evidence for the hypothesis that a late diagenetic overprint has generated the ‘Shuram’ excursion in the Wonoka. Instead, they suggest the presence of a large carbon isotopic gradient in the surface environment, with shallow waters capable of precipitating carbonates with very low δ 13 C (down to 12‰) and deeper shelf and/or marine pore waters generating carbonates with positive carbon isotope values (up to +5‰). Because negative isotope excursions of similar magnitude are found in widely dispersed Ediacaran basins, it is likely that this gradient was characteristic of shelf environments of this period and that a still-unknown global process led to the ‘Shuram’ excursion in shallow water carbonates.

58 GEOSCIENCES↗
DOE OSTI2021

Inter-well connectivity detection in CO 2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. This study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

42 ENGINEERING↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2020

Influence of physical and chemical hydrology on bioremediation of a U-contaminated aquifer informed by reactive transport modeling incorporating 238 U/ 235 U ratios

Microbially-catalyzed reductive immobilization of aqueous uranium (U) as a solid phase has been proposed as a U remediation technique. Both laboratory and field experiments have demonstrated that this reduction reaction alters the 238 U/ 235 U ratio, producing a 238 U-enriched U(IV) solid. In contrast, other major U reactive transport processes fractionate these isotopes much less. This suggests the potential to quantify the extent of bioreduction occurring in groundwater containing U using the 238 U/ 235 U ratio, which would substantially improve upon current practices largely relying on U concentration measurements alone. Current reactive transport models for uranium dynamics include only concentration measurements, which are strongly influenced by highly coupled reactive transformation and aqueous transport processes. The complex physical and chemical behavior of U potentially compromises such quantitative analysis of U storage and release. Here we report the first numerical reactive transport model which explicitly incorporates variations in the 238 U/ 235 U ratio of U and demonstrates improved interpretation of the principal chemical reactions and groundwater transport processes affecting the subsurface mobility and distribution of this widespread contaminant. Recent U bioreduction studies performed in a contaminated aquifer in Rifle, Colorado, USA applied Rayleigh distillation models to interpret U stable isotope fractionation observed as a result of acetate amendment. These simplified models were unable to resolve the spatiotemporal pattern of U isotope fractionation recorded in the aqueous solutes. Here, we employ the multi-component, isotope-enabled CrunchTope reactive transport software to interpret these measured U isotope ratios, and demonstrate accurate reproduction of observed trends in both geochemistry and 238 U/ 235 U ratios for two consecutive years of field experiments. Overall, our results indicate that accurately modeling both the U concentration and isotope ratio distributions greatly constrains the parameter space of the model. We find that the transport properties of U in the Rifle aquifer are governed by the presence of low-permeability regions, which the isotopes are uniquely sensitive to. When U reduction is spatially constrained by these low-permeability regions, the shift in the 238 U/ 235 U ratio becomes more muted. Accurate modeling of observed U isotope ratios thus provides a powerful means to better understand bioremediation, and the current study serves to advance the application of this novel method.

238U/235U↗
DOE OSTI2017

Bayesian Inversion of Seismic and Electromagnetic Data for Marine Gas Reservoir Characterization Using Multi-chain Markov Chain Monte Carlo Sampling

In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic amplitude versus angle (AVA) and controlled source electromagnetic (CSEM) data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo (MCMC) sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis (DREAM) and Adaptive Metropolis (AM) samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and CSEM data. The multi-chain MCMC is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic AVA and CSEM joint inversion provides better estimation of reservoir saturations than the seismic AVA-only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated – reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.

Ren, Huiying↗
DOE OSTI2023

Numerical evaluation of photosensitive tracers as a strategy for separating surface and subsurface transient storage in streams

For this work, we numerically evaluated photosensitive tracers as a potential strategy for separating the effects of surface and hyporheic storage zones (SSZs and HSZs, respectively) on stream corridor transport. Correctly separating HSZ and SSZ effects is critical to estimating the hydro-biogeochemical function of a stream because HSZs and SSZs expose solutes to significantly different biogeochemical conditions, like sunlight exposure, microbial processes, and oxygen availability. Our numerical experiments used a multiscale river-corridor transport model implemented in the ATS code, which accommodates multiple storage zones with distinct travel time distributions and biogeochemical reactions. For parameter inferences, we used Bayesian inverse modeling. We found that breakthrough curves for photo-decaying tracers from day and night injection can delineate surface and hyporheic transient storage contributions, but only when interpreted jointly through a two-storage zone model. Numerical experiments that used only daytime injection or interpreted breakthrough curves with a single storage zone model yielded good fit to breakthrough curves, but parameter estimates were biased and controlling processes misattributed, examples of good model fits for the wrong reasons. Using those biased parameter estimates in reactive transport simulations resulted in significantly different projections of denitrification, which underscores the potential for stream function to be mischaracterized if tracer tests are interpreted through an inappropriately simplified model for transient storage. More generally, this study highlights the role of modeling in evaluating the experimental design and identifying the potential of system mischaracterization and its implications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

High frequency (4th order) sequence stratigraphy of Early Miocene deltaic shorelines, offshore Texas and Louisiana

Well logs and 3D seismic reflection data are integrated to image Lower Miocene depocenters of the upper Texas and westernmost Louisiana coastal and offshore areas. Although previously interpreted at a large scale (3rd order cycles), the detailed stratigraphy and depositional history of the early Miocene succession has not been fully developed. The upper lower Miocene interval from Robulus L (MFS10) to Amphistegina B (MFS9) has been further subdivided into five 4th order cycles (each 200 to 300 m thick) to provide finer scale stratigraphic detail and interpretation of depositional environments. Results of the finer scale correlations are presented in a series of sandstone percent maps. Overall, the maps display a strike-elongate (sub-parallel to the present day coastline) orientation for the sandstone bodies which thin to the southeast (basinward). Net sandstone thickness is relatively low in older cycles and increases in younger cycles suggesting an overall progradation of the deltaic system into the mapped area through time. Continued marine regression and southward movement of the shoreline are also confirmed by seismic amplitude 2 maps. After a major transgression associated with Robulus L deltaic progradation occurred under rising sea level conditions. Deltaic sediments prograded southward and sandstone brought to the upper Texas coast was distributed laterally by longshore currents to form strandplain coast lines. Maximum regression occurred during the interval MFS9_2 to MFS9_3 when a deltaic depocenter formed offshore Texas in east High Island area. However, sandstone thickness progressively decreased laterally to the east (offshore Louisiana) where more shale was present and marine processes reworked deltaic derived sandstones into shore parallel bars. There was a clear retreat of the shoreline during the youngest cycle marking the beginning of transgression associated with Amphistegina B . Detailed stratigraphic interpretation (at a 4th order scale) shows more variability in the dominant shoreline processes during early Miocene than previously thought. Recognition of sedimentary systems variability at a 4th order scale is critical for improved hydrocarbon exploration and in understanding potential future CO 2 storage in the area.

58 GEOSCIENCES↗
DOE OSTI2018

Nuclear-recoil energy scale in CDMS II silicon dark-matter detectors

The Cryogenic Dark Matter Search (CDMS II) experiment aims to detect dark matter particles that elastically scatter from nuclei in semiconductor detectors. The resulting nuclear-recoil energy depositions are detected by ionization and phonon sensors. Neutrons produce a similar spectrum of low-energy nuclear recoils in such detectors, while most other backgrounds produce electron recoils. The absolute energy scale for nuclear recoils is necessary to interpret results correctly. The energy scale can be determined in CDMS II silicon detectors using neutrons incident from a broad-spectrum 252Cf source, taking advantage of a prominent resonance in the neutron elastic scattering cross section of silicon at a recoil (neutron) energy near 20 (182) keV. Results indicate that the phonon collection efficiency for nuclear recoils is 4.8+0.7-0.9% lower than for electron recoils of the same energy. Comparisons of the ionization signals for nuclear recoils to those measured previously by other groups at higher electric fields indicate that the ionization collection efficiency for CDMS II silicon detectors operated at ~4 V/cm is consistent with 100% for nuclear recoils below 20 keV and gradually decreases for larger energies to ~75% at 100 keV. The impact of these measurements on previously published CDMS II silicon results is small.

dark matter, detector calibration, ionization yiel↗