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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simultaneous Discovery of Positive and Negative Interactions Among Rhizosphere Bacteria Using Microwell Recovery Arrays

Understanding microbe-microbe interactions is critical to predict microbiome function and to construct communities for desired outcomes. Investigation of these interactions poses a significant challenge due to the lack of suitable experimental tools available. Here we present the microwell recovery array (MRA), a new technology platform that screens interactions across a microbiome to uncover higher-order strain combinations that inhibit or promote the function of a focal species. One experimental trial generates 10 4 microbial communities that contain the focal species and a distinct random sample of uncharacterized cells from plant rhizosphere. Cells are sequentially recovered from individual wells that display highest or lowest levels of focal species growth using a high-resolution photopolymer extraction system. Interacting species are then identified and putative interactions are validated. Using this approach, we screen the poplar rhizosphere for strains affecting the growth of Pantoea sp. YR343, a plant growth promoting bacteria isolated from Populus deltoides rhizosphere. In one screen, we montiored 3,600 microwells within the array to uncover multiple antagonistic Stenotrophomonas strains and a set of Enterobacter strains that promoted YR343 growth. The later demonstrates the unique ability of the platform to discover multi-membered consortia that generate emergent outcomes, thereby expanding the range of phenotypes that can be characterized from microbiomes. This knowledge will aid in the development of consortia for Populus production, while the platform offers a new approach for screening and discovery of microbial interactions, applicable to any microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

Data compression: The end-to-end information systems perspective for NASA space science missions

The unique characteristics of compressed data have important implications to the design of space science data systems, science applications, and data compression techniques. The sequential nature or data dependence between each of the sample values within a block of compressed data introduces an error multiplication or propagation factor which compounds the effects of communication errors. The data communication characteristics of the onboard data acquisition, storage, and telecommunication channels may influence the size of the compressed blocks and the frequency of included re-initialization points. The organization of the compressed data are continually changing depending on the entropy of the input data. This also results in a variable output rate from the instrument which may require buffering to interface with the spacecraft data system. On the ground, there exist key tradeoff issues associated with the distribution and management of the science data products when data compression techniques are applied in order to alleviate the constraints imposed by ground communication bandwidth and data storage capacity.

Tai, Wallace↗
DOE OSTI2017

Preparation, Performance and Mechanism of Tc and I Getters in Cementitious Waste Forms - 17124

Cementitious waste forms offer a low temperature, cost effective technology for the solidification of liquid wastes. At the Department of Energy Hanford Site, 99-Tc and 129-I are radionuclides contained within low activity waste (LAW) which contribute significantly to environmental impacts associated with clean-up of the site. Cast Stone, a cementitious waste form comprised of 47 wt% blast furnace slag, 45 wt% fly ash and 8 wt% ordinary Portland cement, has undergone investigation as a possible candidate technology for the solidification of LAW. In screening tests using EPA Method 1315 leach testing of various mixes of Cast Stone with simulated LAW, Tc observed effective diffusivities (Dobs) had an average of 5.3 x 10-11 cm{sup 2}/s and I Dobs had an average of 5.7 x 10-9 cm{sup 2}/s. From these measurements, an opportunity exists to improve retention of Tc and I by Cast Stone. One approach to accomplish this is through the incorporation of Tc and I getters to selectively sequester Tc and I in the waste stream and eventual waste form. Initial testing conducted at PNNL identified potassium metal sulfide (KMS-2) and Sn(II)-apatite (Sn-A) as the strongest performing Tc getters and silver exchanged zeolite (Ag-Z) as the top performing I getter. This research also demonstrated that the Tc and I getters, when added to solution simultaneously, can have deleterious interactions with one another and lower the Tc and I removal levels. However, these interactions can be overcome through sequential treatment of solution with the getters. Cast Stone samples were fabricated using a 6.5 M Na average LAW simulant spiked with Tc and I with the addition of various combinations of Tc and I getters. Through EPA Method 1315 and Method 1313 testing it was found that the KMS-2 Tc getter was most successful at lowering Tc Dobs. The I getters were not effective, as apparent interference from sulfide components of the waste form may have led to dissociation of the AgI formed by the Ag-Z getter in sequestering the I from solution. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Single and multi-layered core-shell structures based on ZnO nanorods obtained by aerosol assisted chemical vapor deposition

Core–shell nanorod structures were prepared by a sequential synthesis using an aerosol assisted chemical vapor deposition technique. Several samples consisting of ZnO nanorods were initially grown over TiO{sub 2} film-coated borosilicate glass substrates, following the synthesis conditions reported elsewhere. Later on, a uniform layer consisting of individual Al, Ni, Ti or Fe oxides was grown onto ZnO nanorod samples forming the so-called single MO{sub x}/ZnO nanorod core–shell structures, where MO{sub x} was the metal oxide shell. Additionally, a three-layer core–shell sample was developed by growing Fe, Ti and Fe oxides alternately, onto the ZnO nanorods. The microstructure of the core–shell materials was characterized by grazing incidence X-ray diffraction, scanning and transmission electron microscopy. Energy dispersive X-ray spectroscopy was employed to corroborate the formation of different metal oxides. X-ray diffraction outcomes for single core–shell structures showed solely the presence of ZnO as wurtzite and TiO{sub 2} as anatase. For the multi-layered shell sample, the existence of Fe{sub 2}O{sub 3} as hematite was also detected. Morphological observations suggested the existence of an outer material grown onto the nanorods and further microstructural analysis by HR-STEM confirmed the development of core–shell structures in all cases. These studies also showed that the individual Al, Fe, Ni and Ti oxide layers are amorphous; an observation that matched with X-ray diffraction analysis where no apparent extra oxides were detected. For the multi-layered sample, the development of a shell consisting of three different oxide layers onto the nanorods was found. Overall results showed that no alteration in the primary ZnO core was produced during the growth of the shells, indicating that the deposition technique used herein was and it is suitable for the synthesis of homogeneous and complex nanomaterials high in quality and purity. In addition, materials absorptance determined from the total transmittance and reflectance spectra revealed a broader absorption interval including visible light, indicating potential uses of these nanostructures on solar energy appliances. - Graphical abstract: Display Omitted - Highlights: • Uniform ZnO nanorods (core)–metal oxide (shell) were obtained sequentially by AACVD. • Shells were structured of homogeneous single or multi-layered non-mixed metal oxides. • ZnO nanorod core was preserved during the shell synthesis. • Optical absorptance revealed visible interval absorption for FeO{sub x} shell samples. • Materials can be suitable for photocatalytic or photovoltaic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2011

Cytometer on a Chip

A cytometer now under development exploits spatial sorting of sampled cells on a microarray chip followed by use of grating-coupled surface-plasmon-resonance imaging (GCSPRI) to detect the sorted cells. This cytometer on a chip is a prototype of contemplated future miniature cytometers that would be suitable for rapidly identifying pathogens and other cells of interest in both field and laboratory applications and that would be attractive as alternatives to conventional flow cytometers. The basic principle of operation of a conventional flow cytometer requires fluorescent labeling of sampled cells, stringent optical alignment of a laser beam with a narrow orifice, and flow of the cells through the orifice, which is subject to clogging. In contrast, the principle of operation of the present cytometer on a chip does not require fluorescent labeling of cells, stringent optical alignment, or flow through a narrow orifice. The basic principle of operation of the cytometer on a chip also reduces the complexity, mass, and power of the associated laser and detection systems, relative to those needed in conventional flow cytometry. Instead of making cells flow in single file through a narrow flow orifice for sequential interrogation as in conventional flow cytometry, a liquid containing suspended sampled cells is made to flow over the front surface of a microarray chip on which there are many capture spots. Each capture spot is coated with a thin (.50-nm) layer of gold that is, in turn, coated with antibodies that bind to cell-surface molecules characteristic of the cell species of interest. The multiplicity of capture spots makes it possible to perform rapid, massively parallel analysis of a large cell population. The binding of cells to each capture spot gives rise to a minute change in the index of refraction at the surface of the chip. This change in the index of refraction is what is sensed in GCSPRI, as described briefly below. The identities of the various species in a sample of cells is spatially encoded in the chip by the pattern of capture spots. The number of cells of a particular species is determined from the magnitude of the GCSPRI signal from that spot. GCSPRI as used here can be summarized as follows: The cytometer chip is fabricated with a diffraction grating on its front surface. The chip is illuminated with a light emitting diode (LED) from the front. By proper choice of grating parameters and of the wavelength and the angle of incidence of a laser beam, laser light can be made to be coupled into an electromagnetic mode that resonates with surface plasmons and thus couples light into surface plasmons. Coupling of light into a surface plasmon at a given location reduces the amount of incident light reflected from that location. A change in the index of refraction at the surface of a capture spot gives rise to a change in the resonance condition. Depending on the specific design, the change in the index of refraction could manifest itself as a brightening or darkening, a change in the wavelength needed to excite the plasmon at a given angle of incidence, or a change in the angle of incidence needed to excite the plasmon at a given wavelength. Whereas a multiwavelength laser system with multichannel detection would be needed to detect multiple species in conventional flow cytometry, it suffices to use an LED and a single detector channel in the GCSPRI approach: this contributes significantly to reductions in cost, complexity, size, mass, and power. GCSPRI cytometer chips could be made of plastic and could be mass-produced cheaply by use of molding and other methods adopted from the manufacture of digital video disks. These methods are amenable to a high degree of miniaturization: such additional features as fluidic channels, reaction chambers, and fluid-coupling ports could readily be incorporated into the chips, without incurring substantial additional costs.

Fernandez, Salvador M.↗
NASA NTRS2011

Cytometer on a Chip

A cytometer now under development exploits spatial sorting of sampled cells on a microarray chip followed by use of grating-coupled surface-plasmon-resonance imaging (GCSPRI) to detect the sorted cells. This cytometer on a chip is a prototype of contemplated future miniature cytometers that would be suitable for rapidly identifying pathogens and other cells of interest in both field and laboratory applications and that would be attractive as alternatives to conventional flow cytometers. The basic principle of operation of a conventional flow cytometer requires fluorescent labeling of sampled cells, stringent optical alignment of a laser beam with a narrow orifice, and flow of the cells through the orifice, which is subject to clogging. In contrast, the principle of operation of the present cytometer on a chip does not require fluorescent labeling of cells, stringent optical alignment, or flow through a narrow orifice. The basic principle of operation of the cytometer on a chip also reduces the complexity, mass, and power of the associated laser and detection systems, relative to those needed in conventional flow cytometry. Instead of making cells flow in single file through a narrow flow orifice for sequential interrogation as in conventional flow cytometry, a liquid containing suspended sampled cells is made to flow over the front surface of a microarray chip on which there are many capture spots. Each capture spot is coated with a thin (approximately 50-nm) layer of gold that is, in turn, coated with antibodies that bind to cell-surface molecules characteristic of one the cell species of interest. The multiplicity of capture spots makes it possible to perform rapid, massively parallel analysis of a large cell population. The binding of cells to each capture spot gives rise to a minute change in the index of refraction at the surface of the chip. This change in the index of refraction is what is sensed in GCSPRI, as described briefly below. The identities of the various species in a sample of cells is spatially encoded in the chip by the pattern of capture spots. The number of cells of a particular species is determined from the magnitude of the GCSPRI signal from that spot. GCSPRI as used here can be summarized as follows: The cytometer chip is fabricated with a diffraction grating on its front surface. The chip is illuminated with a light emitting diode (LED) from the front. By proper choice of grating parameters and of the wavelength and the angle of incidence of a laser beam, laser light can be made to be coupled into an electromagnetic mode that resonates with surface plasmons and thus couples light into surface plasmons. Coupling of light into a surface plasmon at a given location reduces the amount of incident light reflected from that location. A change in the index of refraction at the surface of a capture spot gives rise to a change in the resonance condition. Depending on the specific design, the change in the index of refraction could manifest itself as a brightening or darkening, a change in the wavelength needed to excite the plasmon at a given angle of incidence, or a change in the angle of incidence needed to excite the plasmon at a given wavelength. Whereas a multiwavelength laser system with multichannel detection would be needed to detect multiple species in conventional flow cytometry, it suffices to use an LED and a single detector channel in the GCSPRI approach: this contributes significantly to reductions in cost, complexity, size, mass, and power. GCSPRI cytometer chips could be made of plastic and could be mass-produced cheaply by use of molding and other methods adopted from the manufacture of digital video disks. These methods are amenable to a high degree of miniaturization: such additional features as fluidic channels, reaction chambers, and fluid-coupling ports could readily be incorporated into the chips, without incurring substantial additional costs.

Fernandez, Salvador M.↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries

The rapid growth of automated and autonomous instrumentation brings forth opportunities for the co-orchestration of multimodal tools that are equipped with multiple sequential detection methods or several characterization techniques to explore identical samples. This is exemplified by combinatorial libraries that can be explored in multiple locations via multiple tools simultaneously or downstream characterization in automated synthesis systems. In co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, an orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Herein, we propose and implement a co-orchestration approach for conducting measurements with complex observables, such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure and integration into an iterative workflow via multi-task Gaussian Processes (GPs). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GPs. We illustrate this method for different modes of piezoresponse force microscopy and micro-Raman spectroscopy on a combinatorial Sm-BiFeO3 library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of the measured signals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Characterization of Oil and Gas Drill Cuttings for Critical Mineral Recovery and Reuse Potential as Soil Supplements

Expansion of unconventional oil and natural gas production over the last decade in the United States has resulted in record production of natural gas and oil in 2024. Millions of tons of drill cuttings generated from shale gas development are currently disposed of in landfills. Converting these cuttings into value product(s) such as critical minerals (CMs) and soil supplements, has the potential of reducing environmental impact of fossil fuel exploration. In this study we characterize the CM distribution and extractability from collected drill cuttings and core samples from major U.S. shale formations. A four-step sequential extraction, consisting of a sonicated soap step, sonicated EDTA step , mildly reducing agent, and a oxidizing agent, was developed to simultaneously extract CMs and convert the drill cuttings into soil supplements. Viability of converted drill cuttings as soil supplement was determined with a seedling growth experiment. Results show high concentrations of vanadium (V) (up to 1575 ppm, barite (up to 5 wt. %), and rare earth elements (REE) concentrations (up to 253 ppm). High extractability of selected critical minerals such as REE (19-50%) and Ba (10-58%) show promising results. Preliminary results show seedling growth in a mixture of converted drill cuttings with soil. These results show the potential for recovery of CMs from drill cuttings as an alternative domestic and the reduction of the environmental impact of fossil fuel production.

Barczok, Maximilian↗
DOE OSTI2023

Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography ( MSOT ) imaging

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.

Juhong, Aniwat↗
DOE OSTI2017

Multilayered vacuum-arc nanocomposite TiN/ZrN coatings before and after annealing: Structure, properties, first-principles calculations

Highlights: • Multilayer TiN/ZrN films were deposited using sequential vacuum-arc deposition of Ti and Zr targets in nitrogen atmosphere; • Several samples were annealed in air at the temperature 700 °C; • First-principles calculations of TiN(111), ZrN(111) structures and TiN(111)/ZrN(111) multilayer were carried out; • All deposited samples were highly polycrystalline with quite large 20–25 nm crystals; • The nanohardness and elastic modulus of non-annealed coatings reached 42 GPa and 348 GPa, respectively; • Deposited coatings demonstrate good wear and oxidation resistance; - Abstract: Nanoscale multilayered TiN/ZrN films were deposited using sequential vacuum-arc deposition of Ti and Zr targets in a nitrogen atmosphere. Studies of film's properties were carried out using various modern methods of analysis, such as XRD, STEM, HRTEM, SIMS combined with results of nanoindentation and tribological tests. To interpret the mechanical properties of the deposited multilayer films first-principles calculations of TiN(111), ZrN(111) structures and TiN(111)/ZrN(111) multilayer were carried out. To study the influence of thermal annealing, several samples were annealed in air at the temperature 700 °C. All deposited samples were highly polycrystalline with quite large 20–25 nm crystals. The crystalline planes were very ordinated and demonstrated an excellent coordinated growth. The nanohardness and elastic modulus of non-annealed coatings reached 42 GPa and 348 GPa, respectively. Annealing in air at the temperature 700 °C led to partial oxidation of the multilayered coatings, however hardness of the non-oxidized part of the coatings remained as high, as for initial coatings. All deposited coatings demonstrate good wear resistance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRSDate not supplied

Comprehensive Analysis Aeroelastic Stability Predictions for the Mars Sample Recovery Helicopters and Ingenuity

A rotorcraft comprehensive analysis was used to generate aeroelastic stability predictions for both Ingenuity and the Mars Sample Recovery Helicopters. The analysis was performed multiple times for each rotor system with sequential increases to the level of modeling complexity. Initial cases used an independent blade analysis, trim inflow distribution, no airframe degrees of freedom, and a hover condition (a time-invariant problem). The final model used all blades, dynamic inflow, airframe degrees of freedom, and a Floquet theory approach to the flutter analysis. The approach of sequentially increasing modeling complexity was employed to ensure the causes of any observed instabilities could be identified. For each case, stability results were determined across a range of air densities, tip Mach numbers, and rotor collective pitch settings. For the Sample Recovery Helicopter, a forward flight condition analysis with the highest level of modeling complexity was also performed. The aeroelastic stability predictions showed both the Ingenuity and Sample Recovery Helicopter rotor systems to be stable for all analyzed conditions. Parameter excursions of structural properties were performed to explore conditions resulting in aeroelastic instabilities.

Aeroelastic↗
DOE OSTI2022

Zinc speciation and desorption kinetics in a mining waste impacted tropical soil amended with phosphate

Mining is an important component of the Brazilian economy. However, it may also contribute to environmental problems such as the pollution of soils with zinc and other potentially toxic metals. Our objective was to evaluate changes in the chemical speciation and mobility of Zn in a soil amended with phosphate. Soil samples were collected from a deactivated mining area in the state of Minas Gerais, Brazil, and amended with NH 4 H 2 PO 4 saturated with deionized water to 70 % of maximum water retention and incubated at 25 ± 2 °C in open containers for 60 days. The soil was chemically and mineralogically characterized, and sequential extraction, desorption kinetics, and speciation were carried out using synchrotron bulk-sample and micro–X-ray Absorption Near-Edge Structure (XANES/μ-XANES) spectroscopy at the Zn K-edge, and X-ray fluorescence microprobe analysis (μ-XRF). The combination of μ-XRF and μ-XANES techniques made it possible to identify Zn hotspots in the main species formed after phosphate remediation. The best fit combination for bulk XANES and μ-XANES was observed in Zn-montmorillonite, Zn-kerolite, Zn-ferrihydrite, and gahnite. In the course of phosphate treatment, gahnite, Zn layered double hydroxides (Zn-LDH), Zn3(PO4), and ZnO were identified by bulk XANES, while Zn-ferrihydrite, Zn-montmorillonite, and scholzite were identified by μ-XANES. Zinc in the phosphate-amended soil had the strongest partial correlations (r' > 0.05) with Ni, Co, Fe, Cr, Mn, Si, P, Cd, Pb, and Cd, while the unamended soil showed the strongest correlation with Cu, Pb, Fe, and Si. The application of NH 4 H 2 PO 4 altered Zn speciation and favored an increase in Zn desorption. Here, the most available Zn contents after phosphate amendment were correlated with the release of exchangeable Zn fractions, associated with carbonate and organic matter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗
DOE OSTI2021

Distribution and speciation of Sb and toxic metal(loid)s near an antimony refinery and their effects on indigenous microorganisms

Although several studies have investigated the effects of Sb contamination on surrounding environments and indigenous microorganisms, little is known about the effect of co-contamination of Sb and toxic metal(loid)s. In this study, the occurrence of Sb and other toxic metal(loid)s near an operating Sb refinery and near-field landfill site were investigated. Topsoil samples near the refinery had high Sb levels (~3250 mg kg -1 ) but relatively low concentrations of other toxic metal(loid)s. However, several soil samples taken at greater depth from the near-field landfill site contained high concentrations of As and Pb, as well as extremely high Sb contents (~21,400 mg kg -1 ). X-ray absorption fine structure analysis showed that Sb in the soils from both sites was present as Sb(V) in the form of tripuhyite (FeSbO 4 ), a stable mineral. Three-dimensional principal coordinate analysis showed that microbial community compositions in samples with high toxic metal(loid)s concentrations were significantly different from other samples and had lower microbial populations (~10 4 MPN g -1 ). Sequential extraction results revealed that Sb is present primarily in the stable residual fraction (~99 %), suggesting low Sb bioavailability. Finally, however, microbial redundancy analysis suggested that the more easily extractable Pb might be the major factor controlling microbial community compositions at the site.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Investigating growth to detonation in vapor-deposited hexanitrostilbene and pentaerythritol tetranitrate films using high-throughput methods

In this work, a high-throughput experimental setup was used to characterize initiation threshold and growth to detonation in the explosives hexanitrostilbene (HNS) and pentaerythritol tetranitrate (PETN). The experiment sequentially launched an array of laser-driven flyers to shock samples arranged in a 96-well microplate geometry, with photonic Doppler velocimetry diagnostics to characterize flyer velocity and particle velocity at the explosive–substrate interface. Vapor-deposited films of HNS and PETN were used to provide numerous samples with various thicknesses, enabling characterization of the evolution of growth to detonation. One-dimensional hydrocode simulations were performed with reactions disabled to illustrate where the experimental data deviate from the predicted inert response. Prompt initiation was observed in 144 μm thick HNS films at flyer velocities near 3000 m/s and in 125 μm thick PETN films at flyer velocities near 2400 m/s. This experimental setup enables rapid quantification of the growth of reactions in explosive materials that can reach detonation at sub-millimeter length scales. These data can subsequently be used for parameterizing reactive burn models in hydrocode simulations, as discussed in Paper II [D. E. Kittell, R. Knepper, and A. S. Tappan, J. Appl. Phys. 131, 154902 (2022)].

36 MATERIALS SCIENCE↗