Search NASA⌕ Search

SEARCH · Search NASA

Results for “partial reflections”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ~ 82%) and physiological information (i.e. physiological SIF yield, Φ F , 17% ~ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ F responded to the proxy for physiological stress (partial correlation coefficient, r p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ F was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r = 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed Φ F was more strongly correlated spatially with air temperature and VPD ( r = 0.23 and 0.39) than SIF ( r = 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that Φ F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ F is spatially scalable. Utilizing Φ F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Electrical breakdown dynamics in an argon bubble submerged in conductive liquid for nanosecond pulsed discharges

Abstract This study delves into the dynamics of cold atmospheric plasma and their interaction within conductive solutions under the unique conditions of nanosecond pulsed discharges (22 kV peak voltage, 10 ns FWHM, 4.5 kV ns −1 rate-of-rise). The research focuses on the electrical response, breakdown, and discharge propagation in an argon bubble, submerged in a NaCl solution of varying conductivity. Full or partial discharges were observed at conductivities of 1.5 µ S cm −1 (deionized water) to 1.6 mS cm −1 , but no breakdown was observed at 11.0 mS cm −1 when reducing the electrode gap. It is demonstrated that at higher conductivity electric breakdown is observed only when the gas bubble comes into direct contact with the electrode and multiple emission nodes were observed at different timescales. These nodes expanded in the central region of the bubble over timescales longer than the initial high-voltage pulse. This work offers a temporal resolution of 2 ns exposure times over the first 30 ns of the initial voltage pulse, and insight into plasma formation over decaying reflected voltage oscillations over 200 ns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Transition metal elements as donor dopants in CdO

CdO has been shown to achieve a high electron concentration N (> 10 21 cm -3 ) and at the same time a high mobility μ (> 100cm 2 /V s) when doped with conventional shallow dopants (In or Ga), and consequently making it a transparent conducting oxide with very low resistivity ρ <10 -4 $\Omega$ cm. In this work, the properties of CdO thin films doped with a series of transition metal elements (CdO:TM) with partially filled 3d and 4d shells, including Sc, Ti, V, Cr, Fe, Y, Mo, and W, were investigated. We find that doping with these TM elements can effectively increase the N in CdO to a maximum N (N max ) of ~(7-12)×10 20 cm -3 with a dopant concentration x max of 4-7 %. However, unlike CdO:In, the μ of CdO:TM films drops rapidly from > 100 to < 10cm 2 /Vs as the dopant concentration x increases, so that they can only achieve a minimum ρ of ~ (1-2) × 10 -4 $\Omega$ cm, ~ a factor of 2-3 higher than that in CdO:In. As a result, free-carrier absorption and plasma reflection effects limit their optical transparency to <1200 nm. For most 3d TM dopants, a qualitatively higher d-donor level E d,donor gives rise to higher E F,max or a higher N max . Although at low x, the optical band gap E opt of CdO:TM follows the calculated values due to free-carrier effects, as x increases, E opt values are significantly higher than the calculated values. This is believed to be an effect of the anticrossing interaction of the localized d-levels and the extended CdO conduction-band (CB) states, giving rise to a lower occupied E - and an upper unoccupied E + subband. In conclusion, the restructured CBs have much flatter dispersion, which also results in a much higher effective mass $m^{\star}_{e}$, hence it can also explain the much lower μ of CdO:TM films with high N.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bayesian Approach to Estimation of Water Table Elevations Using Historical Rasters as Prior Information 2019 - 20430

In cases of complex but only partially known geology and a lack of spatial control in observation well locations, water table elevation estimation is very challenging. In some cases, auxiliary information, such as observations of the movement of tracers, operation of injection and extraction wells, and calibration of groundwater models against the historical elevation data, can be combined with expert judgement to estimate flow directions in areas of sparse data and to aid in the production of more reliable contour maps (and associated rasters) than could be produced by relying on sparse well elevation data alone. Given a historical sequence of these raster maps, the question arises how to automate, to the extent possible, the process of producing new raster maps to reflect data from previous times, the current data and the operation of expert judgement. One solution is to adopt a Bayesian point of view and to regard the historical well elevation data, auxiliary information and historical raster maps as prior information. The well elevations for water table wells, as well as those for injection/extraction wells and the data associated with other relevant variables, can be viewed as predictors for the raster surface. From this prior information, we can, conditional on the values of these predictors for a new time period, compute an expected value map and a standard deviation map for the new raster. These then can be taken to specify a prior predictive distribution for the pixels in the new raster map. Then we condition the pixels, corresponding to water level observation wells within the raster, on the observed values in those wells (which in general will differ from the regression estimate) for the new time period. Given the smoothness of the water table surface, we then smooth the surface of deviations from the mean surface, based on the variograms of the historical rasters, and add this smoothed surface to the regression mean surface. The error structure of the produced raster map is defined by the regression error structure and the error due to smoothing based on the estimated variograms. This methodology has been developed and is being further refined for groundwater monitoring and remediation at LANL. It is a very flexible method that can also be applied with a variety of other predictors applied to model the water level wells in the area of interest over the historical record. The smoothness of the spatial process and its possible evolution over time can then be estimated from the residuals from this regression. This can be augmented by expert hydrogeological opinion based on site topography and hydrogeology. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Raman Microscopy Analysis of Wyoming CarbonSAFE Pilot Well Thin Sections for Mineralogy and Organic Matter Characterization

Scanning confocal Raman microspectroscopy (RMS) was used to analyze thin sections for inorganic mineral content and for evaluation of organic material (OM). Thin sections were selected from a stratigraphic test well core in the Powder River Basin, Wyoming under the Wyoming CarbonSAFE project. The well was analyzed with a variety of rock and fluid characterization techniques to determine the feasibility of a commercial-scale CO2 storage site. RMS complements other analyses, including traditional petrography, SEM, porosity and permeability, and XRD For this aspect of the study, RMS is especially important in the evaluation of OM in sealing lithologies. At prospective geologic CO2 storage sites, it is imperative to assess the unconventional oil and gas potential of seals to ensure that any future development would not compromise the integrity of the seals. Whole-slide mineralogical surveys were performed on thin sections from various shale and sandstone formations. Surveys were analyzed with Direct Classical Least Squares to identify and quantify minerals. The location and concentration of minerals was color-coded and overlaid on optical images for visualization of the distribution of minerals. Dense hyperspectral Raman mapping of OM was performed on five thin sections. Eleven spectral parameters diagnostic of organic type and thermal maturity were used to train a Partial Least Squares (PLS) calibration against a set of artificially matured samples spanning the pre- to mid-oil window. The PLS was applied to the study set and a post-mature set. Additionally, the PLS was applied to each point in hyperspectral maps for visualization of trends in maturity across sample sets and discrimination of organic matter types within a given map. In inorganic surveys on thin sections, a total of 14 unique inorganic minerals were identified in Raman spectra including quartz, dolomite, calcite, hematite and anhydrite. Shale thin sections tended to be dominated by organic material. OM was often observed mixed with inorganic minerals. Sand- and mudstones were dominated by inorganic minerals. The PLS extrapolated the post-mature set to reflectances >1.2%. The study set ranged from very immature to postmature in the median of map fit-peak parameters. However, point maturity maps indicate that matrix OM in all study samples is immature and that discreet organic particles selected for mapping, which may be inertinites, bias medians towards more-mature. The work demonstrates the capabilities of RMS to perform both whole-slide mineralogy and OM analysis with applications to formation evaluation in oil & gas, carbon sequestration and mining. Here, analysis of sealing formations in the well indicates high levels of immature organic matter that would not be a viable target for future oil production that could compromise the CO2 storage site. The work brings together diverse disciplines from geology and petrography to analytical chemistry, big data and microscopy.

Myers, Grant↗
DOE OSTI2025

Wind Turbine Sound Setbacks and Supply Curves: Ordinances and Extrapolated Trends, 110 Hub Height, 130 Rotor Diameter

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

Array↗
DOE OSTI2021

A Fundamental Economic Assessment of Recovering Rare Earth Elements and Critical Minerals from Acid Mine Drainage Using a Network Sourcing Strategy

In recent years, acid mine drainage (AMD) has emerged as a promising unconventional source of rare earth elements (REEs) and other critical minerals (CMs) such as cobalt and manganese. In this regard, AMD provides a natural heap leaching effect that extracts and concentrates REE/CM from the host strata creating a partially enriched feedstock suitable for downstream extraction, separation, and recovery. While several prior studies have described processes and approaches for the valorization of AMD, very few have described the supply chain and infrastructure requirements as well as the associated economic assessment. To that end, this paper provides a fundamental economic assessment of REE/CM recovery from AMD using a network sourcing strategy in addition to a robust, flexible feedstock separations and refining facility. The methodology of this paper follows that of a typical techno-economic analysis with capital and operating costs estimated using AACE Class IV (FEL-2) guidelines. To demonstrate the range of possible outcomes, four pricing scenarios were modeled including contemporary prices (September, 2021) as well as the minimum and maximum prices over the last decade. In addition, five production scenarios were considered reflecting variations in the product suite, ranging from full elemental separation to magnet REE and CM production only (i.e., Pr, Nd, Tb, Dy, Y, Sc, Co, and Mn). The results of this analysis show that, with the exception of the minimum price scenario, all operational configurations have positive economic indicators with rates of return varying from 25% to 32% for the contemporary price scenario. The optimal configuration was determined to be production of Co, Mn, and all REEs except for mischmetal, which is not recovered. Sensitivity analysis and Monte Carlo simulation show that capital cost and HCl consumption are the two major factors influencing rate of return, thus indicating opportunities for future technology development and cost optimization. Implications of the study and a cooperative profit-sharing model for sourcing are also described.

42 ENGINEERING↗
DOE OSTI2021

Dioxygen Activation Kinetics over Distinct Cu Site Types in Cu-Chabazite Zeolites

Cu-exchanged zeolites activate dioxygen to form active sites for partial methane oxidation (PMO), nitrogen oxide decomposition, and carbon monoxide oxidation. Apparent rates of O 2 activation depend both on the intrinsic kinetics of distinct Cu site types and the distributions of such sites within a given zeolite, which depend on the density and arrangement of the framework Al atoms. In this work, we use hydrothermal synthesis methods to control the arrangement of framework Al sites in chabazite (CHA) zeolites and, in turn, the distinct Cu site types formed. Time-resolved in situ Raman spectroscopy reveals the kinetics of O 2 adsorption and activation within these well-defined Cu-CHA materials and the concomitant structural evolution of copper–oxygen (Cu x O y ) complexes, which are interpreted alongside Cu(I) oxidation kinetics extracted from in situ X-ray absorption spectroscopy (XAS). Raman spectra of several plausible Cu x O y species simulated using density functional theory suggest that experimental spectra (λ ex = 532 nm) capture the formation of mono(μ-oxo)dicopper species (ZCuOCuZ). Transient experiments show that the timescales required to form Cu x O y structures that no longer change in Raman spectra correspond to the durations of oxidative treatments that maximize CH 3 OH yields in stoichiometric PMO cycles (approximately 2 h). Yet, these periods extend well beyond the timescales for the complete conversion of the initial Cu(I) intermediates to their Cu(II) states (<0.3 h, reflected in X-ray absorption near edge spectroscopy spectra), which demonstrates that Cu x O y complexes continue to evolve structurally following rapid Cu(I) oxidation. The dependence of ZCuOCuZ formation rates on O 2 pressure, H 2 O pressure, and temperature is consistent with a mechanism in which ZCuOH reduces to form ZCu + sites that bind molecular oxygen and form ZCu-O 2 intermediates. Subsequent reaction with proximate ZCu + forms bridging peroxo dicopper complexes that cleave O–O bonds to form ZCuOCuZ in steps facilitated by water. These data and interpretations provide evidence for the chemical processes that link rapid and kinetically irrelevant Cu oxidation steps (frequently probed by XAS and UV–vis spectroscopy) to the relatively slow genesis of reactive Cu complexes that form CH 3 OH during PMO. In doing so, we reveal previously unrecognized complexities in the processes by which Cu ions in zeolites activate O 2 to form active Cu x O y complexes, which underscore the insight afforded by judicious combinations of experimental and theoretical techniques.

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

Specular-reflection contributions to static and dynamic radiation forces on circular cylinders

Interest in the response of highly reflecting objects in water to modulated acoustical radiation forces makes it appropriate to consider contributions to such forces from perfectly reflecting objects to provide insight into radiation forces. The acoustic illumination can have wavelengths much smaller than the object's size, and objects of interest may have complicated shapes. Here, the specular contribution to the oscillating radiation force on an infinite circular cylinder at normal incidence is considered for double-sideband-suppressed carrier-modulated acoustic illumination. The oscillatory magnitude of the specular force decreases monotonically with increasing modulation frequency, and the phase of the oscillating force depends on the relative phase of the sidebands. The phase dependence on the modulation frequency can be reduced with the appropriate selection of a sideband relative-phase parameter. That is a consequence of the significance of rays that are incident on the cylinder having small impact parameters that are nearly backscattered. For one choice of a relative sideband phase, a prior partial wave series (PWS) solution is available, which supports the specular analysis when the PWS is evaluated for a rigid cylinder. The importance of specular contributions for aluminum cylinders in water is noted. A specular analysis for an analogous spherical reflector is also summarized.

Marston, Philip L. (ORCID:0000000306778537)↗
DOE OSTI2020

IAEA Photonuclear Data Library 2019

Here, photo-induced reaction cross section data are of importance for a variety of current or emerging applications, such as radiation shielding design and radiation transport analyses, calculations of absorbed dose in the human body during radiotherapy, physics and technology of fission reactors (influence of photo-reactions on neutron balance) and fusion reactors (plasma diagnostics and shielding), activation analyses, safeguards and inspection technologies, nuclear waste transmutation, medical isotope production and astrophysical applications. To address these data needs the IAEA Photonuclear Data library was produced in 1999, containing evaluated photo-induced cross sections and neutron spectra for 164 nuclides which were deemed relevant for the applications. Since the release of the IAEA Photonuclear Data Library however, new experimental data as well as new methods to assess the reliability of experimental cross sections have become available. Theoretical models and input parameters used to evaluate photo-induced reactions have improved significantly over the years. In addition, new measurements of partial photoneutron cross sections using mono-energetic photon beams and advanced neutron detection systems have been performed allowing for the validation of the evaluations and assessments of the experimental data. Furthermore, technological advances have led to the construction of new and more powerful gamma-beam facilities, therefore new data needs are emerging. We report our coordinated efforts to address these data needs and present the results of the new up-to-date evaluations included in the new updated IAEA Photonuclear Data Library consisting of 219 nuclides. The new library includes 188 new evaluations produced by the CRP evaluators, and one evaluation taken from the JENDL/PD-2016 library, while 20 evaluations were retained from the previous 1999 IAEA Photonuclear Data Library. In most of the cases, the photon energy goes up to 200 MeV. A total of 55 nuclides are new in this library reflecting the progress in measurements but also the developing data needs. In this paper we discuss the new assessment method and make recommendations to the user community in cases where the experimental data are discrepant and the assessments disagree. In addition, in the absence of experimental data, we present model predictions for photo-induced reaction cross section on nuclides of potential interest to medical radioisotope production.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

The role of oxygen in automotive grade lithium-ion battery cathodes: an atomistic survey of ageing

The rising demand for high-performance lithium-ion batteries, pivotal to electric transportation, hinges on key materials like the Ni-rich layered oxide LiNi x Co y Al z O 2 (NCA) used in cathodes. The present study investigates the redox mechanisms, with particular focus on the role of oxygen in commercial NCA electrodes, both fresh and aged under various conditions (aged cells have performed >900 cycles until a cathode capacity retention of ~80%). Our findings reveal that oxygen participates in charge compensation during NCA delithiation, both through changes in transition metal (TM)–O bond hybridization and formation of partially reversible O 2 , the latter occurs already below 3.8 V vs. Li/Li + . Aged NCA material undergoes more significant changes in TM–O bond hybridization when cycling above 50% SoC, while reversible O 2 formation is maintained. Nickel is found to be redox active throughout the entire delithiation and shows a more classical oxidation state change during cycling with smaller changes in the Ni–O hybridization. By contrast, Co redox activity relies on a stronger change in Co–O hybridization, with only smaller Co oxidation state changes. The Ni–O bond displays an almost twice as large change in its bond length on cycling as the Co–O bond. The Ni–O 6 octahedra are similar in size to the Co–O 6 octahedra in the delithiated state, but are larger in the lithiated state, a size difference that increases with battery ageing. These contrasting redox activities are reflected directly in structural changes. The NCA material exhibits the formation of nanopores upon ageing, and a possible connection to oxygen redox activity is discussed. The difference in interaction of Ni and Co with oxygen provides a key understanding of the mechanism and the electrochemical instability of Ni-rich layered transition metal oxide electrodes. Our research specifically highlights the significance of the role of oxygen in the electrochemical performance of electric-vehicle-grade NCA electrodes, offering important insights for the creation of next-generation long-lived lithium-ion batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Fortran mimetic abstraction language (Formal) v0.1.

The Fortran mimetic abstraction language ("Formal") is a domain-specific language (DSL) embedded in Fortran 202Y [1]. Formal provides novel software abstractions for simulating phenomena governed by the partial differential equations (PDEs) of vector and tensor calculus. Such equations model an extremely broad set of physical phenomena, ranging from atmospheric winds to light propagation. Formal's data structures and algorithms mimic in form and behavior continuous functions and operators. Formal supports these mathematical constructs using mimetic discretizations that define a discrete calculus satisfying various tensor calculus theorems, thereby ensuring high-fidelity representations of the physics being modeled. [2] Formal 0.1.0 also lays a foundation for the future use of Fortran 202Y type-safe templates to facilitate the formal verification of tensor contractions in computational physics and artificial intelligence [3]. [1] "Fortran 202Y" is Fortran standard committee's informal designation for the next Fortran revision, which will likely be "Fortran 2028". [2] Corbino, J. and Castillo, J. (2020) Journal of Computational and Applied Mathematics, https://doi.org/10.1016/j.cam.2019.06.042. [3] Haveraaen, M., Järvi, J., & Rouson, D. (2019). Reflecting on Generics for Fortran. https://j3-fortran.org/doc/year/19/19-188.pdf.

Rouson, Damian [Lawrence Berkeley National Laborat↗
DOE OSTI2022

Supported molybdenum oxides for the aldol condensation reaction of acetaldehyde

The (retro-)aldol condensation reaction is an important chemical transformation in the upgrading of biomass-derived compounds into fuels and valuable specialty chemicals. In this study, we found that supported molybdenum oxide (MoO x ) catalysts were active and selective for the aldol condensation of acetaldehyde to crotonaldehyde under steady-state reactor conditions. Through a combination of transmission electron microscopy (TEM), ultraviolet–visible (UV–VIS) diffuse reflectance spectroscopy, Fourier transform infrared (FTIR) spectroscopy of adsorbed pyridine, and steady-state reactor testing, we determined that highly dispersed MoO x has a strong interaction with a γ-Al 2 O 3 support resulting in optimal catalyst performance at low weight loadings. In contrast, MoO x particles supported on SiO 2 have a weaker interaction with the support, resulting in a monotonic relationship between Mo loading and aldol condensation activity. The Lewis acid site density and strength are important parameters for predicting aldol condensation activity across all samples. The concentration of weak acid sites had a poor correlation with aldol condensation activity, most likely because these sites are too weak to activate acetaldehyde for the reaction. Medium and strong acid sites both had good correlations to aldol condensation activity. Results from X-ray absorption near edge structure (XANES) and acetaldehyde temperature programmed desorption (TPD) indicated that partially reduced MoO x was more active for aldol condensation, but pretreatment in reducing or oxidizing environments had no significant effect on steady-state catalytic activity. Finally, characterization of spent catalyst samples through temperature programmed oxidation (TPO) and thermogravimetric analysis (TGA) revealed that catalysts with high densities of strong acid sites tended to form more carbonaceous deposits on the surface over the course of the reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Monitoring Fracture Saturation With Internal Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply scattered or reflected waves. However, a challenge arises when multiply scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural or induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (TNN also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The TNN with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. Furthermore, these results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2020

O 2 -Oxidation of Individual Graphite and Graphene Nanoparticles in the 1200 – 2200 K Range: Particle-to-Particle Variations and the Evolution of the Reaction Rates and Optical Properties

The kinetics for O 2 oxidation of individual graphite and graphene platelet nanoparticles (NPs) were studied as a function of temperature (1200 to 2200 K) at varying oxygen partial pressures, using a single nanoparticle mass spectrometry method. NP temperature (TNP) was measured by measuring the NP thermal emission spectra during the kinetics studies. The initial oxidation efficiency is found to peak in the 1200 to 1500 K range, dropping by an order of magnitude as TNP was increased above 2000 K. There were large NP-to-NP variations in the oxidation rates, attributed to variations in the NP surface structure. In addition, the oxidation efficiencies evolved, non-monotonically, as the NPs reacted, decreasing by factors of between 10 and 300. This evolution of reactivity is attributed to changes in the NP surface structure due to the combination of oxidation and annealing. In conclusion, the optical properties, including wavelength dependence of the emissivity and the absorption cross section for the 532 nm heating laser, also tended to evolve as the NPs oxidized, but differently for each individual NP, presumably reflecting differences in the initial structures and their evolution.

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

Influence of soil depth, irrigation, and plant genotype on the soil microbiome, metaphenome, and carbon chemistry

ABSTRACT Climate change is causing an increase in drought in many soil ecosystems and a loss of soil organic carbon. Calcareous soils may partially mitigate these losses via carbon capture and storage. Here, we aimed to determine how irrigation-supplied soil moisture and perennial plants impact biotic and abiotic soil properties that underpin deep soil carbon chemistry in an unfertilized calcareous soil. Soil was sampled up to 1 m in depth from irrigated and planted field treatments and was analyzed using a suite of omics and chemical analyses. The soil microbial community composition was impacted more by irrigation and plant cover treatments than by soil depth. By contrast, metabolomes, lipidomes, and proteomes differed more with soil depth than treatments. Deep soil (>50 cm) had higher soil pH and calcium concentrations and higher levels of organic acids, bicarbonate, and triacylglycerides. By contrast, surface soil (0–5 cm) had higher concentrations of soil organic matter, organic carbon, oxidizable carbon, and total nitrogen. Surface soils also had higher amounts of sugars, sugar alcohols, phosphocholines, and proteins that reflect osmotic and oxidative stress responses. The lipidome was more responsive to perennial tall wheatgrass treatments compared to the metabolome or proteome, with a striking change in diacylglyceride composition. Permanganate oxidizable carbon was more consistently correlated to metabolites and proteins than soil organic and inorganic carbon and soil organic matter. This study reveals specific compounds that reflect differences in organic, inorganic, and oxidizable soil carbon fractions that are impacted by interactions between irrigation-supplied moisture and plant cover in calcareous soil profiles. IMPORTANCE Carbon is cycled through the air, plants, and belowground environment. Understanding soil carbon cycling in deep soil profiles will be important to mitigate climate change. Soil carbon cycling is impacted by water, plants, and soil microorganisms, in addition to soil mineralogy. Measuring biotic and abiotic soil properties provides a perspective of how soil microorganisms interact with the surrounding chemical environment. This study emphasizes the importance of considering biotic interactions with inorganic and oxidizable soil carbon in addition to total organic carbon in carbonate-containing soils for better informing soil carbon management decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

59 BASIC BIOLOGICAL SCIENCES↗