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DOE OSTI2023

Preparation and Characterization Methods of Thin Layer Samples for Standoff Detection

Detection of analytes deposited on surfaces is crucial for many applications: Development of methods to prepare thin layers (e.g. ~5 to 100 µm) is important for both system design and field studies. In this work, solid and liquid analytes were deposited on painted and bare substrates including aluminum, glass, plastic, and concrete using an ExactaCoat ultrasonic spray coater. Laboratory hemispherical reflectance (HRF) spectra were collected for samples with different layer thicknesses so as to characterize both the composition and layer thickness. Preliminary results demonstrate that to prepare homogenous layers on surfaces, parameters such as substrate type, analyte solubility, vapor pressure, paint color, surface porosity, and surface roughness are all important. Liquid chemicals posed several issues during deposition: Diisopropyl methyl phosphonate evaporated from surfaces more quickly than the other chemicals and was thus not detected in the HRF experiments. Less volatile liquids, such as tributylphosphate, remained on the surface for the duration of the test, but a uniform layer thickness could not be obtained as the liquid pooled to one side when mounted at an angle. The deposition of solids (e.g., acetaminophen, caffeine and methylphosphonic acid) from volatile solvents such as chloroform also proved problematic due to streaking caused by rapid solvent evaporation. Solids deposited from ethanol, however, worked well on bare substrates. For most samples plotting the integrated infrared band strength vs. surface thicknesses showed a linear relationship, confirming that the surface loading can be controlled by programming the concentration and the number of passes on the ultrasonic sprayer.

Thin layer, deposition, infrared standoff, Hemisph↗

DOE OSTI2021

Alpha Spectrometry Results for Groundwater Samples Collected in Northern Iraq and a Summary of the Environmental Setting of the Adaya Burial Site

The Radiation Protection Center (RPC) of the Iraqi Ministry of Environment continues to evaluate the potential health impacts associated with the Adaya Burial Site, which is located 33 kilometers (20.5 miles) southwest of Mosul. This report documents the radiological analyses of 16 groundwater samples collected from wells located in the vicinity of the Adaya Burial Site and at other sites in northern Iraq. The Adaya Burial Site is a high-risk dump site because a large volume of radioactive material and contaminated soil is located on an unsecure hillside above the village of Tall ar Ragrag. The uranium activities for the 16 water samples in northern Iraq are considered to be naturally occurring and do not indicate artificial (man-made) contamination. With one exception, the alpha spectrometry results for the 16 wells that were sampled in 2019 indicate that the water quality concerning the three uranium isotopes (Uranium-233/234, Uranium-235/236, and Uranium-238) was acceptable for potable purposes (drinking and cooking). However, Well 7 in Mosul had a Uranium-233/234 activity concentration that slightly exceeded the World Health Organization guidance level. Eight of the 16 wells are located in the villages of Tall ar Ragrag and Adaya and had naturally occurring uranium concentrations. Wells in the villages of Tall ar Ragrag and Adaya are located near the Adaya Burial Site and should be sampled on an annual schedule. The list of groundwater analytes should include metals, total uranium, isotopic uranium, gross alpha/beta, gamma spectroscopy, organic compounds, and standard water quality parameters. Our current understanding of the hydrogeologic setting in the vicinity of the Adaya Burial Site is solely based on villager's domestic wells, topographic maps, and satellite imagery. To better understand the hydrogeologic setting, a Groundwater Monitoring Program needs to be developed and should include the installation of twelve groundwater monitoring wells in the vicinity of Tall ar Ragrag and the Adaya Burial Site. Characterization of the limestone aquifer and overlying alluvium is needed. RPC should continue to support health assessments for the villagers in Tall ar Ragrag and Adaya. Collecting samples for surface water (storm water), airborne dust, vegetation, and washway sediment should be conducted on a routine basis. Human access to the Adaya Burial Site needs to be strictly limited. Livestock access on or near the burial site needs to be eliminated. The surface-water exposure pathway is likely a greater threat than the groundwater exposure pathway. Installation of a surface-water diversion or collection system is recommended in order to reduce the potential for humans and livestock to come in contact with contaminated water and sediment. To reduce exposure to villagers, groundwater treatment should be considered if elevated uranium or other contaminants are detected in drinking water. Installing water-treatment systems would likely be quicker to accomplish than remediation and excavation of the Adaya Burial Site. The known potential for human exposure to uranium and metals (such as arsenic, chromium, selenium, and strontium) at the Adaya Burial Site is serious. Additional characterization , mitigation, and remediation efforts should be given a high priority.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Neural network based emulation of galaxy power spectrum covariances: A reanalysis of BOSS DR12 data

We train neural networks to quickly generate redshift-space galaxy power spectrum covariances from a given parameter set (cosmology and galaxy bias). This covariance emulator utilizes a combination of traditional fully connected network layers and transformer architecture to accurately predict covariance matrices for the high redshift, north galactic cap sample of the BOSS DR12 galaxy catalog. We run simulated likelihood analyses with emulated and brute-force computed covariances, and we quantify the network’s performance via two different metrics: (1) difference in Χ 2 and (2) likelihood contours for simulated BOSS DR 12 analyses. We find that the emulator returns excellent results over a large parameter range. We then use our emulator to perform a reanalysis of the BOSS HighZ NGC galaxy power spectrum, and find that varying covariance with cosmology along with the model vector produces Ω m = $0.27⁢6$$^{+0.013}_{–0.015}$, H 0 = 70.2 ± 1.9 km/s/Mpc, and σ 8 = $0.67⁢4$$^{+0.058}_{–0.077}$. These constraints represent an average 0.46⁢σ shift in best-fit values and a 5% increase in constraining power compared to fixing the covariance matrix (Ω m = 0.293 ± 0.017, H 0 = 70.3 ± 2.0 km/s/Mpc, σ 8 = $0.70⁢2$$^{+0.063}_{–0.075}$). As a result, this work demonstrates that emulators for more complex cosmological quantities than second-order statistics can be trained over a wide parameter range at sufficiently high accuracy to be implemented in realistic likelihood analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Alternating chempolishing and electropolishing for interior and exterior surface finishing of additively manufactured (AM) metal components

Additively manufactured (AM) components’ surface finishing is crucial in adopting them for intended applications in challenging environments involving fatigue, corrosion, high temperature, and nuclear radiation. In our prior research, chempolishing (C) was utilized as an electroless etching process that uniformly smoothens complex AM components’ accessible interior and exterior surfaces (Tyagi et al. Additive Manufacturing, 25:32–38, 22). A wide range of electropolishing (E) has been demonstrated for AM surface finishing. However, electropolishing can impact a surface that can be juxtaposed to counter electrode and can yield a very smooth surface to sub-micrometer level roughness. However, a knowledge gap exists about the impact of applying both approaches on the same surface one after another and what new advantages may arise because of combining two methods. This paper uses dual-stage liquid-based surface finishing strategies produced by alternating the chempolishing (C) and electropolishing (E) steps. Two dual-stage surface finishing approaches, i.e., chempolishing followed by electropolishing (CE) and electropolishing followed by chempolishing (EC), were performed on the 316 stainless AM steel component. Impacts of EC and CE approaches were compared with single-stage C and E surface finishing approaches. An optical microscope and mechanical profilometer were utilized to investigate the wide range of surface roughness parameters. CE and EC produced Ra ~ 1.4 µm and ~ 1.6 µm, respectively. Surface roughness on CE- and EC-treated AM samples was lower than those individually treated by C and E approaches. Scanning electron microscopy provided further insights into the microstructural difference between CE- and EC-treated AM samples. This paper reports a liquid contact angle study on CE- and EC-treated AM samples to provide insights into the relative difference in surface energy that is crucial for making coatings on AM parts. A spectroscopic reflectance study was also employed to register the difference in physical properties of AM components treated with CE and EC approaches. Further, this study reveals industrially practicable interior and exterior surface finishing approaches for complex AM metal components that require minimum tooling and real-time process monitoring.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Derivative-free optimization of a rapid-cycling synchrotron

Here, we develop and solve a constrained optimization model to identify an integrable optics rapid-cycling synchrotron lattice design that performs well in several capacities. Our model encodes the design criteria into 78 linear and nonlinear constraints, as well as a single nonsmooth objective, where the objective and some constraints are defined from the output of Synergia, an accelerator simulator. We detail the difficulties of the 23-dimensional simulation-constrained decision space and establish that the space is nonempty. We use a derivative-free manifold sampling algorithm to account for structured nondifferentiability in the objective function. Our numerical results quantify the dependence of solutions on constraint parameters and the effect of the form of objective function.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Ink-based laser powder bed fusion of barium titanate

Barium titanate (BTO) is a lead-free functional ceramic that is widely used in sensors, transducers, and actuators. The increasing demand for complex geometries and quick design iterations motivates the use of additive manufacturing techniques. Laser powder bed fusion (L-PBF) additive manufacturing technique enables the net-shape or near-net-shape fabrication of metals and alloys. Ceramic L-PBF remains challenging due to their high melting temperature. Here, to address this challenge, a nanoparticle ink feedstock was incorporated into the L-PBF process to allow low power melting of BTO. Key print parameters (i.e. laser power, scanning speed, and hatching spacing) were optimized to fabricate millimeter-scale BTO parts. The printed BTO samples possessed an inhomogeneous equiaxed-columnar-cellular microstructure. Semiconducting current-voltage behavior was observed in as-printed samples, which was attributed to the creation of oxygen vacancies. The piezoelectric d 33 coefficient was measured to be 4.6 pC/N.

Dielectrics↗

DOE OSTI2020

On the quantification and efficient propagation of imprecise probabilities with copula dependence

This paper addresses the problem of quantification and propagation of uncertainties associated with dependence modeling when data for characterizing probability models are limited. Practically, the system inputs are often assumed to be mutually independent or correlated by a multivariate Gaussian distribution. However, this subjective assumption may introduce bias in the response estimate if the real dependence structure deviates from this assumption. In this work, we overcome this limitation by introducing a flexible copula dependence model to capture complex dependencies. Here, a hierarchical Bayesian multimodel approach is proposed to quantify uncertainty in dependence model-form and model parameters that result from small data sets. This approach begins by identifying, through Bayesian multimodel inference, a set of candidate marginal models and their corresponding model probabilities, and then estimating the uncertainty in the copula-based dependence structure, which is conditional on the marginals and their parameters. The overall uncertainties integrating marginals and copulas are probabilistically represented by an ensemble of multivariate candidate densities. A novel importance sampling reweighting approach is proposed to efficiently propagate the overall uncertainties through a computational model. Through an example studying the influence of constituent properties on the out-of-plane properties of transversely isotropic E-glass fiber composites, we show that the composite property with copula-based dependence model converges to the true estimate as data set size increases, while an independence or arbitrary Gaussian correlation assumption leads to a biased estimate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Application of a Geochemically Informed Leak Detection (GILD) Model to CO 2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO 2 enhanced oil recovery (EOR) and CO 2 storage. A geochemically informed leak detection (GILD) model has been applied to CO 2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO 2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO 2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca 2+ or HCO 3 - is used as the monitoring parameter, but only for aquifers with calcite.

58 GEOSCIENCES↗

DOE OSTI2020

In Situ Catalytic Fast Pyrolysis using Red Mud Catalyst: Impact of catalytic fast pyrolysis temperature and biomass feedstocks

Catalytic fast pyrolysis (CFP) has been considered as a very promising approach for converting lignocellulosic biomass into higher-quality bio-oils followed by hydrotreating to produce fuelrange products. A reactive, robust, and low-cost catalyst is required to drive the CFP process. Red mud, a side-product produced during the refining of bauxite to alumina, appears to be an effective catalyst for in situ CFP of biomass. In this paper, we report the impact of CFP reaction temperature on the conversion of a pinyon juniper feedstock to bio-oils using red mud as the catalyst and then to fuel-range hydrocarbons by hydrotreating of the produced bio-oil. The yield and quality of the CFP bio-oil produced and the yield and quality of hydrotreated final products were determined. When the CFP process temperature was lowered from 450 to 400ºC, the bio-oil yield increased with minimal differences in the oxygen content, hydrogen-to-carbon ratio, water content, etc. In addition, CFP bio-oils at both temperatures were processed in a single-stage continuous hydrotreater without reactor plugging during the testing period (i.e., ~100 hours on stream). The yield of CFP bio-oil produced at 400ºC was lower than the yield at 450ºC. However, the overall yield, from biomass to hydrocarbon fuel, was still higher for CFP processing at 400ºC than for processing at 450ºC. This indicates the potential for enhancing overall carbon efficiency, including CFP processing and hydrotreating, by employing red mud catalysts for biomass pyrolysis and tuning the CFP parameters. Detailed analysis of bio-oil and hydrotreated products showed a lower content of lignin-derived species in both samples. Furthermore, CFP processing of three different biomass feedstocks corroborated red mud catalyst development for producing improved quality bio-oil and, when combined with hydrotreating, for the production of fuel range hydrocarbons.

Santosa, Daniel M.↗

DOE OSTI2021

Time–connectivity superposition and the gel/glass duality of weak colloidal gels

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Fast radio burst dispersion measure distribution as a probe of helium reionization

Fast radio burst (FRB) discoveries are occurring rapidly, with thousands expected from upcoming surveys. The dispersion measures (DM) observed for FRB include important information on cosmological distances and the ionization state of the universe from the redshift of emission until today. Rather than considering the DM–redshift relation, we investigate the statistical ensemble of the distribution of dispersion measures. Here, we explore the use of this abundance information, with and without redshift information, to probe helium reionization through simulated data to redshift z = 6. Carrying out Monte Carlo simulations of FRB survey samples, we examine the effect of different source redshift distributions, host galaxy models, sudden vs gradual reionization, and covariance with cosmological parameters on determination of helium reionization properties. We find that a fluence limited survey with 10 4 FRBs can discriminate different helium reionization histories at ~6σ using the DM-distribution of bursts, without redshift information (and ~10σ with redshifts).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Shedding light on the MiniBooNE excess with searches at the LHC

The origin of the excess of low-energy events observed by the MiniBooNE experiment remains a mystery, despite exhaustive investigations of backgrounds and a series of null measurements from complementary experiments. One intriguing explanation is the production of beyond-the-Standard-Model particles that could mimic the experimental signature of additional ν e appearance seen in MiniBooNE. In one proposed mechanism, muon neutrinos up-scatter to produce a new “dark neutrino” state that decays by emitting highly collimated electron-positron pairs. We propose high-energy neutrinos produced from W boson decays at the Large Hadron Collider as an ideal laboratory to study such models. Simple searches for a low-mass, boosted dilepton resonance produced in association with a high- p T muon from the W decay with run 2 data would already provide unique sensitivity to a range of dark neutrino scenarios, with prompt and displaced searches providing complementarity. Looking farther ahead, we show how the unprecedented sample of W boson decays anticipated at the HL-LHC, together with improved lepton acceptance would explore much of the parameter space most compatible with the MiniBooNE excess. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

CMB lensing and Ly⁢ α forest cross bispectrum from DESI’s first-year quasar sample

The squeezed cross-bispectrum B κ,Ly α between the gravitational lensing in the cosmic microwave background and the 1D Ly α forest power spectrum can constrain bias parameters and break degeneracies between σ8 and other cosmological parameters. We detect B κ,Ly ⁢α with 4.8⁢σ significance at an effective redshift z eff =2.4 using Planck PR3 lensing map and over 280,000 quasar spectra from the Dark Energy Spectroscopic Instrument’s first-year data. We test our measurement against metal contamination and foregrounds such as Galactic extinction and clusters of galaxies by deprojecting the thermal Sunyaev-Zeldovich effect. Finally, we compare our results to a tree-level perturbation theory calculation and find reasonable agreement between the model and measurement.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Spectroscopic search for optical emission lines from dark matter decay

We search for narrow-line optical emission from dark matter decay by stacking dark-sky spectra from the Dark Energy Spectroscopic Instrument (DESI) at the redshift of nearby galaxies from DESI’s Bright Galaxy and Luminous Red Galaxy samples. Our search uses regions separated by 5 to 20 arcsec from the centers of the galaxies, corresponding to an impact parameter of approximately 50 kpc. No unidentified spectral line shows up in the search, and we place a line flux limit of 10 −19 ergs/s/cm 2 /arcsec 2 on emissions in the wavelength range of 2000–9000⁢ Å. This places the tightest constraints yet on the two-photon decay of dark matter in the mass range of 5 to 12 eV, with a particle lifetime exceeding 3 × 10 25 s. Furthermore, this detection limit also implies that the line surface brightness contributed from all dark matter along the line of sight is at least 2 orders of magnitude lower than the measured extragalactic background light (EBL), ruling out the possibility that narrow optical-line emission from dark matter decay is a major source of the EBL.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Experimental and Numerical Investigation of Flow Boiling in Additive Manufactured Foam Structures With Vapor Pathways

Abstract The unique properties of metal foams make them potential candidates for a range of applications, including microsystem thermal management. Using additive manufacturing to create foam-type structures can improve upon prior thermal solutions by eliminating thermal interface materials and allowing for customization/local control of parameters. In the present investigation, flow boiling in additive-manufactured metal foams is investigated both experimentally and numerically. Two test samples, one with uniform structure and the other with pathways for vapor removal, are compared both experimentally and numerically. A conjugate computational fluid dynamics and heat transfer (CFD-HT) model utilizing a three-dimensional volume of fluid (VOF) model with accompanying evaporation/condensation model provided in-depth visualization of the boiling flow phenomena. The experiments generated the thermohydraulic performance over a range of heat fluxes, demonstrating that the sample incorporating dedicated vapor pathways performed better in both pressure and heat transfer performance metrics compared to the uniform foam. Additionally, negative system-level effects (i.e., hydraulic oscillations) were shown to be abated using the vapor removal structures. The numerical model yielded further insight into the factors contributing to the improved performance. Results indicated the pathways functioned as vapor removal channels, allowing the generated vapor to vent from the foam structure into the lanes. Further computational investigations demonstrated changes in flow regimes, where the addition of vapor channels caused the flow to change from churn to annular. Bubble behavior unique to the vapor pathway structure was studied, showing stagnant regions that eject vapor into the channel.

Engineering↗

DOE OSTI2023

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.

Computer Science↗

DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗

DOE OSTI2022

EQ_phase_detection

The EQ_phase_detection software is designed to scan continuous daily waveforms to detect earthquake phase arrivals from local to regional (150 km) events. The detections are made with a deep learning encoder-decoder model. When the model detects an earthquake in the waveforms, a second model is implemented to classify the first arriving motions. Both deep learning models are trained with the Tensorflow package using publicly available benchmark data sets. The software input is a path to a directory that contains waveforms in mseed format and the associated response files in xml format. The output is a data table of time stamped detections, signal amplitude, signal-to-noise ratio, and softmax probability of the detection in a generic format applicable to post-processing association algorithms for event locations. Additionally, the p-wave and s-wave waveforms are saved in a data table for rapid access when producing improved locations using correlation-based techniques. The software is designed for multiprocessing with multiple GPU’s for rapid processing of large data sets. The configuration file provides flexibility in the trained models implemented and allows access to multiple models trained for different sampling rates or input dimensions. This is particularly useful for regions with multiple networks that do not have the same data parameters.

Johnson, Christopher↗