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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neutron-unbound states in 31 Ne

Background: The Island of Inversion near the N = 20 shell gap is home to nuclei with reordered single-particle energy levels compared to the spherical shell model. Studies of 31 Ne have revealed that its ground state has a halo component, characterized by a valence neutron orbiting a deformed 30 Ne core. This lightly-bound nucleus with a separation energy of only S n = 170 keV is expected to have excited states that are neutron unbound. Purpose: The purpose of this experiment was to investigate the low-lying excited states in 31 Ne that decay by the emission of a single neutron. Methods: An 89 MeV/u 33 Mg beam impinged on a segmented Be reaction target. Neutron unbound states in 31 Ne were populated via a two-proton knockout reaction. The 30 Ne fragment and associated neutron from the decay of 31 Ne were detected by the MoNA-LISA-Sweeper experimental setup at the National Superconducting Cyclotron Laboratory. Invariant mass spectroscopy was used to reconstruct the two-body decay energy ( 30 Ne + n). Results: The two-body decay energy spectrum exhibits two features: a low-lying peak at 0.30 ± 0.17 MeV and a broad enhancement at 1.50 ± 0.33 MeV, each fit with an energy-dependent asymmetric Breit-Wigner line shape representing a resonance in the continuum. Accompanying shell model calculations using the FSU interaction within NuShellX, combined with cross-section cal culations using the eikonal reaction theory, indicate that these peaks in the decay energy spectrum are caused by multiple resonant states in 31 Ne. Conclusions: In this study, excited states in 31 Ne were observed for the first time. Transitions from calculated shell model final states in 31 Ne to bound states in 30 Ne are in good agreement with the measured decay energy spectrum. Cross-section calculations for the two-proton knockout populating 31 Ne states as well as spectroscopic factors pertaining to the decay of 31 Ne into 30 Ne are used to examine the results within the context of the shell model expectations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Beam particle identification and tagging of incompletely stripped heavy beams with HEIST

A challenge preventing successful inverse kinematics measurements with heavy nuclei that are not fully stripped is identifying and tagging the beam particles. For this purpose, the HEavy ISotope Tagger (HEIST) has been developed. HEIST utilizes two micro-channel plate timing detectors to measure the time-of-flight, a multi-sampling ion chamber to measure energy loss, and a high-purity germanium detector to identify isomer decays and calibrate the isotope identification system. HEIST has successfully identified 198 Pb and other nearby nuclei at energies of about 75 MeV/A. In the experiment discussed, a typical cut containing 89% of all 198 Pb 80+ in the beam had a purity of 86%. We examine the issues of charge state contamination. Here, the observed charge state populations of these ions are presented and, using an adjusted beam energy, are well described by the charge state model GLOBAL.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Targeting the Y-box Binding Protein-1 Axis to Overcome Radiochemotherapy Resistance in Solid Tumors

Multifunctional Y-box binding protein-1 (YB-1) is highly expressed in different human solid tumors and is involved in various cellular processes. DNA damage is the major mechanism by which radiochemotherapy (RCT) induces cell death. On induction of DNA damage, a multicomponent signal transduction network, known as the DNA damage response, is activated to induce cell cycle arrest and initiate DNA repair, which protects cells against damage. YB-1 regulates nearly all cancer hallmarks described to date by participating in DNA damage response, gene transcription, mRNA splicing, translation, and tumor stemness. YB-1 lacks kinase activity, and p90 ribosomal S6 kinase and AKT are the key kinases within the RAS/mitogen-activated protein kinase and phosphoinositide 3-kinase pathways that directly activate YB-1. Thus, the molecular targeting of ribosomal S6 kinase and AKT is thought to be the most effective strategy for blocking the cellular function of YB-1 in human solid tumors. In this review, after describing the prosurvival effect of YB-1 with a focus on DNA damage repair and cancer cell stemness, clinical evidence will be provided indicating an inverse correlation between YB-1 expression and the treatment outcome of solid tumors after RCT. In the interest of being concise, YB-1 signaling cascades will be briefly discussed and the current literature on YB-1 posttranslational modifications will be summarized. Finally, the current status of targeting the YB-1 axis, especially in combination with RCT, will be highlighted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Development of a molecularly informed biogeochemical framework for reactive transport modeling of subsurface carbon inventories, transformations and fluxes (Final Report)

The overall objective of the project was to combine new molecular-level characterization strategies with soil carbon flux measurements to develop and evaluate model representations of subsurface carbon cycling. We expanded on existing studies in the East River watershed, Colorado, in collaboration with the Berkeley Lab Watershed Function Scientific Focus Area (SFA), SLAC Groundwater Quality SFA, and Rocky Mountain Biological Laboratory (RMBL) to develop an elevation and vegetation gradient that is now the subject of long-term monitoring by the USGS. To achieve the overall objective, we combined field studies of soil respiration with laboratory analyses, ranging from spectroscopy to incubation studies. This combination of techniques enabled us to develop a new understanding of the drivers of high-elevation soil respiration. In the process, we developed three new modeling approaches that improve our ability to conceptualize and ultimately to represent soil respiration in numerical models. The first approach is a plot-scale transient inverse model that can be used to determine in situ CO 2 production rates from measured concentration profiles and surface fluxes. Application of this method revealed the importance of plant phenology and deep CO 2 production in moderating CO 2 fluxes to the atmosphere. The second modeling approach is a molecular-scale tool that enables spectroscopic and elemental data for carbon speciation to be transformed into functional group abundances, or the ‘SOC-fga model’. This method uniquely enables carbon speciation to be tracked within a reactive transport framework to partition carbon among different pathways and storage zones within the soil. The third modeling tool builds strongly on the previous approaches and captures the microbial processes driving heterotrophic respiration. This ‘dormancy model’ allows the native soil microbial population to respond transiently to the presence or absence of water in order to catalyze carbon respiration. This approach was also compared to the simpler and more widely used first-order model using two experimental datasets with different temporal and spatial resolutions. Our results illustrate that the simpler first order model provides a robust and efficient representation of deep (>1 meter) soil respiration, but that shallow soils, where most respiration occurs, require explicit representation of moisture-dependent activation and dormancy rates. By assessing soil organic carbon turnover at multiple scales, we see a complex array of controls emerge. Atte scale of a hillslope, spatial heterogeneity in soil respiration rates dominates and is uncorrelated with instantaneous soil moisture and plant community. At the profile scale, the balance between plant inputs and water availability is the dominant control. At the microbial to molecular scale, physiological processes associated with carbon use and carbon speciation are important controls. These scale-dependent controls emphasize the need for new modeling approaches that examine their interactions and hierarchies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimizing pollinator conservation and crop yield among perennial bioenergy crops

In order to both combat the decline of biodiversity and produce food, fuel, and fiber for a growing human population, current agricultural landscapes must transition into diversified, multifunctional systems. Perennial cellulosic biofuel crops have potential to meet both of these challenges, acting as multifunctional systems that can enhance biodiversity. What is not well understood, and what we test here, are the tradeoffs among different perennial crops in their performance as biofuels and in biodiversity conservation. Working in an established bioenergy experiment with four native, perennial, cellulosic biofuel crop varieties - ranging from monoculture to diverse restoration planting - we tested the effect of biofuel crop management on flower communities, pollinator communities, and crop yield. The greatest abundance and diversity of pollinators and flowers were in treatments that were successional (unmanaged), followed by restored prairie (seeded mix of native grasses and forbs), switchgrass, and a mix of native grasses. However, biofuel crop yield was approximately the inverse, with native grasses having the highest yield, followed by switchgrass and prairie, then successional treatments. Restored prairie was the optimal biofuel crop when both pollinator conservation and crop yield are valued similarly. We add to mounting evidence that policy is needed to create sustainable markets that value the multifunctionality of perennial biofuel systems in order to achieve greater ecosystem services from agricultural landscapes.

09 BIOMASS FUELS↗
DOE OSTI2023

Photodissociation of H 2 on Ag and Au Nanoparticles: Effect of Size and Plasmon versus Interband Transitions on Threshold Intensities for Dissociation

This Article provides new insights concerning the simulation of plasmon-driven chemical reactions using real-time TDDFT based on the tight-binding electronic structure code DFTB+, with applications to the dissociation of H 2 on octahedral silver and gold nanoparticles with 19–489 atoms. A new component of these calculations involves sampling a 300 K canonical ensemble to determine the distribution of possible outcomes of the calculations, and with this approach we are able to determine the threshold for dissociation as a function of laser intensity, wavelength, and nanocluster size. We show that the threshold intensity varies as an inverse power of nanocluster size, which makes it possible to extrapolate the results to sizes that are more typical of experimental studies. The intensities obtained from this extrapolation are around a factor of 100 above powers used in the pulsed experiments. This is a closer comparison of theory and experiment than has been obtained in previous real-time simulations, and the remaining discrepancy can be understood in terms of electromagnetic hot spots that are associated with cluster formation. Here, we also compare the influence of plasmon excitation versus interband excitation on reaction thresholds, revealing that for silver clusters plasmon excitation leads to lower thresholds, but for gold clusters interband excitation is more effective. Our study also includes an analysis of charge transfer to and from the H 2 molecule, and a determination of orbital populations during and after the pulse, showing the correlation between metal excitations and the location of the antibonding level of H 2 .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Spectra and characteristics of somatic mutations induced by ionizing radiation in hematopoietic stem cells

Spectra and frequencies of spontaneous and X-ray-induced somatic mutations were revealed with mouse long-term hematopoietic stem cells (LT-HSCs) by whole-genome sequencing of clonal cell populations propagated in vitro from single isolated LT-HSCs. SNVs and small indels were the most common types of somatic mutations, and increased up to twofold to threefold by whole-body X-irradiation. Base substitution patterns in the SNVs suggested a role of reactive oxygen species in radiation mutagenesis, and signature analysis of single base substitutions (SBS) revealed a dose-dependent increase of SBS40. Most of spontaneous small deletions were shrinkage of tandem repeats, and X-irradiation specifically induced small deletions out of tandem repeats (non-repeat deletions). Presence of microhomology sequences in non-repeat deletions suggested involvement of microhomology mediated end-joining repair mechanisms as well as nonhomologous end-joining in radiation-induced DNA damages. We also identified multisite mutations and structural variants (SV), i.e., large indels, inversions, reciprocal translocations, and complex variants. The radiation-specificity of each mutation type was evaluated from the spontaneous mutation rate and the per-Gy mutation rate estimated by linear regression, and was highest with non-repeat deletions without microhomology, followed by those with microhomology, SV except retroelement insertions, and multisite mutations; these types were thus revealed as mutational signatures of ionizing radiation. Further analysis of somatic mutations in multiple LT-HSCs indicated that large fractions of postirradiation LT-HSCs originated from single LT-HSCs that survived the irradiation and then expanded in vivo to confer marked clonality to the entire hematopoietic system, with varying clonal expansion and dynamics depending on radiation dose and fractionation.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

General relativistic MHD simulations of non-thermal flaring in Sagittarius A*

Sgr A* exhibits regular variability in its multiwavelength emission, including daily X-ray flares and roughly continuous near-infrared (NIR) flickering. The origin of this variability is still ambiguous since both inverse Compton and synchrotron emission are possible radiative mechanisms. The underlying particle distributions are also not well constrained, particularly the non-thermal contribution. In this work, we employ the GPU-accelerated general relativistic magnetohydrodynamics code H-AMR to perform a study of flare flux distributions, including the effect of particle acceleration for the first time in high-resolution 3D simulations of Sgr A*. For the particle acceleration, we use the general relativistic ray-tracing code bhoss to perform the radiative transfer, assuming a hybrid thermal+non-thermal electron energy distribution. We extract ~60 h light curves in the sub-millimetre, NIR and X-ray wavebands, and compare the power spectra and the cumulative flux distributions of the light curves to statistical descriptions for Sgr A* flares. Our results indicate that non-thermal populations of electrons arising from turbulence-driven reconnection in weakly magnetized accretion flows lead to moderate NIR and X-ray flares and reasonably describe the X-ray flux distribution while fulfilling multiwavelength flux constraints. These models exhibit high rms per cent amplitudes, $\gtrsim 150{{\ \rm per\ cent}}$ both in the NIR and the X-rays, with changes in the accretion rate driving the 230 GHz flux variability, in agreement with Sgr A* observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Heavy dark matter in rapidly evolving massive stars

We study the impact of heavy dark matter (DM) captured in massive stars via scattering(s) with the star constituents. We focus on the first stars and use stellar evolution simulations to track down how DM capture evolves over time from the zero-age main sequence to the late metal-rich stages of stellar evolution. During the early hydrogen-helium-dominated phase, the capture process is well described by scattering with two targets. As a star evolves, metal production leads to the formation of a dense core surrounded by a lighter envelope. The core significantly enhances the capture of ultra-heavy DM; in this case, three distinct nuclear species are required to accurately describe multiple-scattering capture. We use the Eddington inversion method to obtain a realistic DM velocity distribution, better suited when the star is near the center of a halo, than the widely used Maxwell-Boltzmann distribution. We find that heavy DM would be able to thermalize and achieve capture-annihilation equilibrium within a massive star's lifetime for regions of the parameter space not excluded by direct detection. For non-annihilating DM, because of the high amount of targets available for capture and despite massive stars being short-lived, it would even be possible for DM to achieve self-gravitation and collapse to a black hole, which eventually could swallow the star from within before the expected end of the star's life, for non-excluded regions of the parameter space. Our results highlight the dependence of DM capture on the stellar evolutionary stage, composition, and halo location, demonstrating that accurate modeling of massive stars is essential for constraining heavy DM with primordial stellar populations.

dark matter theory↗
DOE OSTI2026

Seismic Event Characterization Using Full Moment Tensors on the Hypersphere

Moment tensor solutions provide insights into the deformation that has occurred in the source region of a seismic event and are therefore of great value in identifying different types of seismic sources, such as when monitoring for underground nuclear tests. Despite this utility, inversion of waveforms recorded by seismometers for their full seismic moment tensor is not yet routine, and development of robust methods to classify events based on this information is in its infancy. Here, we assemble an inventory of 1405 full moment tensor solutions that include explosive, earthquake, and collapse events, and investigate the use of anisotropic probability distribution functions on the 5D hypersphere to discriminate between these sources. Using a Bayesian classifier, we obtain optimal success rates of 98.4% across all events and demonstrate that modification of the prior probabilities provides a natural way to alter the balance between not missing desirable events (such as explosions) versus misclassifying large numbers of undesired events (such as earthquakes). The approach is specifically designed to progress from traditional, bipolar event screening metrics to more generalized event identification across multiple types of seismic sources. Despite current databases containing insufficient numbers of events to definitively demonstrate at present, we also find intriguing evidence of subgroupings within individual source populations on the hypersphere, for example, between chemical and nuclear explosions, raising the potential possibility of discriminating between these event types in the future.

Geosciences↗
DOE OSTI2021

Evolutionary inverse design of defects at graphene 2D lateral interfaces

Grain boundaries (GBs) in two-dimensional (2D) materials often have a profound impact on various material properties from mechanical to optical to electronic, yet predicting all possible GB formations is a challenge. Here, we introduce a workflow based on an evolutionary algorithm for exploring possible GBs formed at a lateral 2D interface. In a departure from conventional genetic algorithm based structure optimization methods, we perform genetic operations in the near interface region that allow us to be computationally efficient. We benchmark our method using graphene, which is a well-studied 2D material with a wide range of point defects. An empirical potential was used as the surrogate of the evolutionary search. More than 11.5 × 106 structures in total were evaluated for 128 GB orientations, and for each orientation, the ten best structures are recorded. A subset of low energy GBs predicted by empirical potential based search was relaxed by first-principles calculations and used to validate the energetic rank order. With the validated formation energy, we rank-ordered the best 128 GB structures and performed a detailed statistical analysis of primitive rings to find the correlation between the ring distribution and the formation energy. We found that for low energy GBs (below 0.5eV/Å), Stone–Wales defects will dominate, while structures with a higher energy (0.5–1.1eV/Å) show an increasing population of heptagons and nine-membered rings to form seven-nine defect pairs. For structures with energy higher than 1.1eV/Å, the percentage of octagons and nine-membered rings increases, which indicates that these two types of rings are not energetically favorable. Our proposed methodology is broadly applicable to explore defective low dimensional materials and represents a powerful tool that enables a systematic search of GBs of lateral interfaces for 2D materials.

Zhang, Jianan↗
DOE OSTI2021

Production and Persistence of Extreme Two-temperature Plasmas in Radiative Relativistic Turbulence

Turbulence is a predominant process for energizing electrons and ions in collisionless astrophysical plasmas, and thus is responsible for shaping their radiative signatures (luminosity, spectra, and variability). To better understand the kinetic properties of a collisionless radiative plasma subject to externally driven turbulence, we investigate particle-in-cell simulations of relativistic plasma turbulence with external inverse Compton cooling acting on the electrons. We find that ions continuously heat up while electrons gradually cool down (due to the net effect of radiation), and hence the ion-to-electron temperature ratio T i /T e grows in time. Furthermore, we show that Ti/Te is limited only by the size and duration of the simulations (reaching ${T}_{i}/{T}_{e}\sim {10}^{3}$), indicating that there are no efficient collisionless mechanisms of electron–ion thermal coupling. This result has implications for models of radiatively inefficient accretion flows, such as observed in the Galactic center and in M87, for which so-called two-temperature plasmas with ${T}_{i}/{T}_{e}\gg 1$ have been invoked to explain their low luminosity. Additionally, we find that electrons acquire a quasi-thermal distribution (dictated by the competition of turbulent particle energization and radiative cooling), while ions undergo efficient nonthermal acceleration (acquiring a harder distribution than in equivalent nonradiative simulations). There is a modest nonthermal population of high-energy electrons that are beamed intermittently in space, time, and direction; these beamed electrons may explain rapid flares in certain high-energy astrophysical systems (e.g., in the Galactic center). These numerical results demonstrate that extreme two-temperature plasmas can be produced and maintained by relativistic radiative turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Host-region parameters for an adjustable model for crustal earthquakes to facilitate the implementation of the backbone approach to building ground-motion logic trees in probabilistic seismic hazard analysis

The backbone approach to constructing a ground-motion logic tree for probabilistic seismic hazard analysis (PSHA) can address shortcomings in the traditional approach of populating the branches with multiple existing, or potentially modified, ground-motion models (GMMs) by rendering more transparent the relationship between branch weights and the resulting distribution of predicted accelerations. To capture epistemic uncertainty in a tractable manner, there are benefits in building the logic tree through the application of successive adjustments for differences in source, path, and site characteristics between the host region of the selected backbone GMM and the target region for which the PSHA is being conducted. The implementation of this approach is facilitated by selecting a backbone GMM that is amenable to such host-to-target adjustments for individual source, path, and site characteristics. The NGA-West2 GMM of Chiou and Youngs (CY14) has been identified as a highly adaptable model for crustal seismicity that is well suited to such adjustments. Rather than using generic source, path, and site characteristics assumed appropriate for the host region, the final suite of adjusted GMMs for the target region will be better constrained if the host-region parameters are defined specifically on the basis of their compatibility with the CY14 backbone GMM. To this end, making use of a recently developed crustal shear-wave velocity profile consistent with CY14, we present an inversion of the model to estimate the key source and path parameters, namely the stress parameter and the anelastic attenuation. With these outputs, the effort in constructing a ground-motion logic tree for any PSHA dealing with crustal seismicity can be focused primarily on the estimation of the target-region characteristics and their associated uncertainties. The inversion procedure can also be adapted for any application in which different constraints might be relevant.

Stafford, Peter J.↗
DOE OSTI2024

β -delayed neutron emission of Mn 64 , Cr 62 , and Fe 65

The β-decay properties of nuclei near the second nuclear “island of inversion” around neutron rich nuclei with neutron number 40 are important tests of nuclear structure models and interactions. In particular, the β-delayed neutron emission branch (Ρ n ), is useful for investigating β-strength and neutron-γ competition above the neutron separation energies of the daughter nuclei. We report new constraints for Ρ n values for three nuclei in the region: 62 Cr (Ρ n <1%), 64 Mn (Ρ n = 1.5⁢(6)%), and 65 Fe (Ρ n < 1%), measured with the Neutron Emission Ratio Observer (NERO) neutron long counter system and the Beta Counting Station (BCS) at the National Superconducting Cyclotron Laboratory (NSCL). Our results resolve the large discrepancy between previous direct and indirect measurements for 64 Mn and confirm the predictions of global theoretical models when a statistical treatment of the γ and neutron decays of the daughter states is included. Here we also obtain improved half-lives for 62 Cr [206(5) ms] and the short-lived isomer in the 62 Fe daughter [112(7) ms] from β-delayed γ emission data obtained in the same experiment with the Summing NaI (SuN) total absorption spectrometer. Finally, we use γ emission data to obtain a new upper limit for the 62 Cr β-decay population of the long-lived isomeric state in 62 Mn.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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↗
DOE OSTI2020

Effect of lithium incorporation on tweaking the electrocatalytic behavior of tantalum-based oxides

To study the effect of lithium incorporation on the electrochemical properties and catalytic behavior of tantalum-based oxides, a series of mesoporous lithium-contained tantalum oxides was developed adopting an inverse micelle-based sol gel synthesis method. Four various phases of amorphous Ta 2 O 5 , polycrystalline LiTaO 3 , Li 5.04 Ta 6.16 O 17.92 , and Li 3 TaO 4 were determined for samples synthesized at Li/Ta ratios of 0.5 (LTO-0.5), 1 (LTO-1), 2 (LTO-2), and 3 (LTO-3) respectively. The surface analysis revealed that LTO-2 sample is featured with lower amount of lithium atoms at the surface rather than the bulk of the sample. The electrochemical analysis of LTO samples showed that LTO-2 possesses lower charge transfer resistance extracted from impedance spectroscopy that consequently leads to a better electrocatalytic performance. The LTO-2 electrode also showed a low open circuit potential (OCP) combined with a relatively higher electroactive surface area, thus capable of running an electrooxidation reaction at a potential lower than all other LTO samples. The more negative OCP value of LTO-2 electrode in AA solution states a more facile electron transfer between the analyte and electrode compared to other synthesized samples. Mott–Schottky plots of LTO electrodes are characterized by positive slopes which indicate n-type semiconductor behavior. The charge carrier density (5.5 × 10 19 cm -3 ) in LTO-2 sample is the lowest among all other samples. The charge carrier density is dependent on the amount of applied lithium in the synthesis confirming various lithium diffusion behaviors in LTO samples. The lower carrier concentration in LTO-2 justifies its higher activity in electrooxidation providing less populated lower unoccupied molecular orbital (LUMO) for the accommodation of electrons extracted from the analyte higher occupied molecular orbital (HOMO). Therefore, LTO-2 showed an improved electrocatalytic performance due to lower charge transfer resistance (higher conductivity) offered by lower amount of lithium atom at its surface and higher oxygen vacancies in the bulk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗