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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Growth of Cd 0.9 Zn 0.1 Te 1-y Se y Single Crystals for Room Temperature Gamma-Ray Detection

Quaternary Cd 0.9 Zn 0.1 Te 1-y Sey (CZTS) single crystals, a novel room-temperature nuclear radiation detector semiconductor material, have been grown using a modified vertical Bridgman method (VBM) and a travelling heater method (THM). The percentage concentration of selenium in the VBM-grown crystal was 3% and that in the THM-grown crystal was 2%. While the THM Frisch collar detector (4.4 × 4.4 × 10.7 mm 3 ) produced a highly resolved pulse height spectra (PHS) with a resolution of ~1.06% for 662-keV gamma rays without any correction, the VBM-grown detector (10 × 10 × 2 mm 3 ) offered a high energy resolution of ~2% after application of a digital biparametric correction. The high-resolution performance of these detectors has been attributed to the addition of Se in the Cd 0.9 Zn 0.1 Te (CZT) matrix. Ab-initio calculations based on density functional theory (DFT) also confirmed that the addition of Se in the CZT matrix helps to reduce the formation of TeCd and the TeZn anti-sites. The VBM-grown crystals were characterized using powder x-ray diffraction (XRD) and energy dispersive x-ray spectroscopy (EDS). While XRD results revealed sharp diffraction peaks confirming the crystalline nature of the grown crystal, the EDS results confirmed the targeted stoichiometry of the elemental composition. The bulk resistivity of the grown crystal was calculated to be ~3 × 10 10 Ω-cm from current-voltage characteristics recorded at room temperature in a planar configuration, ensuring that the grown CZTS crystals have low dark current as required for detector-grade crystals.

42 ENGINEERING↗
DOE OSTI2022

Connecting radio emission to AGN wind properties with broad absorption line quasars

ABSTRACT Broad absorption line quasars (BALQSOs) show strong signatures of powerful outflows, with the potential to alter the cosmic history of their host galaxies. These signatures are only seen in ∼10 per cent of optically selected quasars, although the fraction significantly increases in IR and radio selected samples. A proven physical explanation for this observed fraction has yet to be found, along with a determination of why this fraction increases at radio wavelengths. We present the largest sample of radio matched BALQSOs using the LOFAR Two-metre Sky Survey Data Release 2 and employ it to investigate radio properties of BALQSOs. Within the DR2 footprint, there are 3537 BALQSOs from Sloan Digital Sky Survey DR12 with continuum signal-to-noise ≥5. We find radio-detections for 1108 BALQSOs, with an important subpopulation of 120 LoBALs, an unprecedented sample size for radio matched BALQSOs given the sky coverage to date. BALQSOs are a radio-quiet population that show an increase of ×1.50 radio-detection fraction compared to non-BALQSOs. LoBALs show an increase of ×2.22 that of non-BALQSO quasars. We show that this detection fraction correlates with wind-strength, reddening, and C iv emission properties of BALQSOs and that these features may be connected, although no single property can fully explain the enhanced radio detection fraction. We create composite spectra for subclasses of BALQSOs based on wind strength and colour, finding differences in the absorption profiles of radio-detected and radio-undetected sources, particularly for LoBALs. Overall, we favour a wind-ISM interaction explanation for the increased radio-detection fraction of BALQSOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The APOGEE value-added catalogue of Galactic globular cluster stars

ABSTRACT We introduce the Sloan Digital Sky Survey (SDSS)/ Apache Point Observatory Galactic Evolution Experiment (APOGEE) value-added catalogue of Galactic globular cluster (GC) stars. The catalogue is the result of a critical search of the APOGEE Data Release 17 (DR17) catalogue for candidate members of all known Galactic GCs. Candidate members are assigned to various GCs on the basis of position in the sky, proper motion, and radial velocity. The catalogue contains a total of 7737 entries for 6422 unique stars associated with 72 Galactic GCs. Full APOGEE DR17 information is provided, including radial velocities and abundances for up to 20 elements. Membership probabilities estimated on the basis of precision radial velocities are made available. Comparisons with chemical compositions derived from the GALactic Archaeology with HERMES (GALAH) survey, as well as optical values from the literature, show good agreement. This catalogue represents a significant increase in the public data base of GC star chemical compositions and kinematics, providing a massive homogeneous data set that will enable a variety of studies. The catalogue in fits format is available for public download from the SDSS-IV DR17 value-added catalogue website.

Schiavon, Ricardo P. (ORCID:0000000222440897)↗
DOE OSTI2021

HFIR SiC-SiC Composite Clad Tube Bowing Test: Pre-Irradiation Characterization

This report describes the pre-irradiation characterization of the SiC-SiC composite clad tube bowing experiment that will be irradiated in the High Flux Isotope Reactor (HFIR). There are concerns that SiCSiC fuel cladding in light water reactors could undergo bowing because of the non-uniform fast neutron flux profiles. This experiment is combined with the SiC-SiC channel box experiment, which has similar concerns regarding control blade movements in boiling water reactors. The HFIR experiment aims to validate the predicted deformation and stresses in the composite clad specimen after the exposure of fast neutron flux gradients. Significant radial fast neutron flux gradients that exist in the permanent reflector of HFIR were thoroughly characterized by using detailed 3D neutronic calculations. The 3D displacement damage dose rate profile and the resulting volumetric swelling in SiC were used as inputs to structural analyses to determine the predicted deformation that will affect the specimens. The tube specimens were characterized by using traditional dimensional inspection and surface profilometry to provide detailed information regarding the pre-irradiation condition as the first step to evaluate the radiation-induced deformation. Furthermore, fine engraving markers were inscribed along all outer surfaces of the specimen and mapped by using a digital microscope and a multidimensional stage. This allows marker spacings to be accurately measured and compared with similar measurements that will be made post-irradiation to provide local radiation-induced strain mapping. The experiment successfully completed two HFIR cycle irradiations, cycles 492 and 493, which finished on July 25, 2021.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

In situ high-temperature 3D imaging of the damage evolution in a SiC nuclear fuel cladding material

Silicon carbide (SiC)-based nuclear fission fuel rod cladding has been considered as one of the possible designs for accident tolerant fuels. It is in the form of a SiC fibre reinforced SiC matrix composite tube (SiC f -SiC m ) with monolithic SiC outer and/or inner coating layers. This study focuses on the deformation and fracture processes in this material using in situ X-ray micro-computed tomography (XCT) at room temperature (RT) and 1200 °C in an inert gas environment in a C-ring compression loading configuration. Prior to testing, local properties and residual stresses were characterised using nanoindentation and Raman spectroscopy since they can impact the mechanical behaviour of the material. The 3D strain distribution, crack formation and propagation processes including the toughening mechanisms (e.g., crack deflection, micro-cracking, crack bridging and bifurcation) are investigated in the coating and underlying composites at RT and 1200 °C. There is no particular sequence which toughening mechanism occurs first – this is very different from the conventional toughening theory in ceramic-matrix composites under uniaxial tension loading. Indeed, no evidence of fibre pull-out or fibre fracture was observed in this SiC f -SiC m nuclear cladding material in the current C-ring compression configuration. The correlation between local measurements and bulk mechanical behaviour are discussed.

3D imaging↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2025

A Privacy First Path Analysis using Clickstream Data

In the modern digital economy, data-driven decision making is crucial for effectively meeting the ever-evolving demands of consumer engagement and satisfaction. Clickstream data has become invaluable for understanding customer behavior, yet concerns over privacy and security persist, especially with some internet service providers profiting from its sale. This article introduces an innovative methodology that blends experiential learning with advanced cryptographic techniques, including differential privacy and graph analytics. The core objective of this methodology is to estimate Customer Lifetime Value (CLV) by analyzing clickstream data, achieving an average prediction accuracy of 92.4% in user engagement levels while ensuring user anonymity through Recency, Frequency, and Monetary (RFM) analysis. Our study introduces the concept of a “data depositor” and a privacy manager, employing the composition theorem to merge non-adaptive queries effectively. Privacy budgets (? = 1.0, d = 10-5), sensitivity-specific techniques, and data partitioning were applied. Randomization and noise addition protect data integrity, with special handling for categorical values. This approach, differing from prior studies, offers a 12.6% improvement in privacy-preserving targeting accuracy while maintaining strict confidentiality, presenting a novel path forward in data-driven decision-making.

Frequency and Monetary (RFM) analysis↗
DOE OSTI2025

Machine learning-driven design and self-sensing capabilities of automotive bumper lattices for adaptive impact response

We present a novel approach to design an automotive bumper energy absorber using carbon fiber reinforced polymer composites, optimized to meet conflicting performance requirements for two distinct impact scenarios. The design must satisfy both a low-speed (2.5 mph) pendulum intrusion test, simulating vehicle-to-vehicle collisions, and a high-speed (25 mph) leg flexion test, replicating pedestrian impacts. These tests demand opposing deformation characteristics: high flexibility (deformation < 85 mm) for the former and high stiffness (deformation < 22 mm) for the latter. To address these contradictory requirements, we developed a machine learning (ML) framework for inverse optimization of lattice designs and material selection. Unlike traditional iterative design processes, our ML model directly outputs optimal design parameters and material choices based on target performance inputs. The energy absorber was fabricated using advanced additive manufacturing techniques, including extrusion deposition and digital light processing. The integration of carbon fibers provides multifunctionality to the bumper structure, enabling self-sensing capabilities through changes in electrical resistivity under compression. This electrical response demonstrates high repeatability under multiple cycles at 2% compression and exhibits distinct signatures during crack formation under high deformation. This research offers adaptive performance through innovative design methodologies and smart material integration. The approach has potential applications in various fields requiring adaptive energy absorption and real-time structural health monitoring.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2024

Strength-ductility synergy through microstructural and compositional heterogeneity in directed energy deposition additive manufacturing of face-centered cubic materials

Directed energy deposition (DED) is an additive manufacturing (AM) process based on welding technology and offers the advantages of large build volume, high deposition rate, and ability to fabricate multi-material parts. Epitaxial continuous columnar grain growth is a characteristic microstructural feature of DED processed alloys. In this study, a bamboo-like microstructure (periodic alternation of equiaxed and columnar structure) was produced by adopting an intermittent deposition strategy in 316L stainless steel and Inconel 625. The formation of a bamboo-like alternating microstructure was confirmed through electron backscattered diffraction (EBSD) analysis. Hardness mapping showed that the columnar to equiaxed transition (CET) occurred at the region right below the fusion line. A finite element (FE) model was used to investigate the relationship between the temperature gradient (G) and the solidification rate (R). The FE model showed a low G/R ratio at the region right below the interface promoting the CET. The grain size and material-dependent deformation behaviors are analyzed using digital image correlation (DIC). The lower deformation on the fine-grain regions observed in DIC analysis is attributed to a higher strain hardening rate, which is confirmed through dislocation density analysis on a tensile-interrupted specimen. The periodically alternating grain size coupled with the microstructural changes caused by intermittent deposition strategy result in a better strength-ductility synergy in both single-material and bimetallic specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mapping TpPa-1 covalent organic framework (COF) molecular interactions in mixed solvents via atomistic modeling and experimental study

Complex solvent environments continue to limit the widespread adoption of organic solvent nanofiltration (OSN) in many chemical industry applications. In this paper we employ a commercially available covalent organic framework (COF), TpPa-1, and force field models to molecularly map separation performance of TpPa-1 membrane in mixed solvents. To minimize time and length scale mismatch between atomistic modeling and experiments, solvent permeance was normalized with water in modeling and experimental results to enable direct comparison. Model outputs, such as organic solvent permeance and solute rejection rate, matched well with filtration results. Since the atomistic models assume that all mass transfer is via through-pore transport, the discrepancies between modeling and experimental results provide insights on the effect of linear polymer defects, adsorption and interstitial mass transfer on polycrystalline COF membrane performance. In sum, force field models can serve as digital twins of COF membranes to simulate separation processes while capturing the effects of COF structure, chemistry, and crystallinity on membrane performance in complex organic solvent environments. Finally, this approach will provide insight into future COF design and synthesis for persisting separation challenges.

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

Subsurface spectroscopy of heterogeneous materials using optical wavefront shaping

Plastic-bonded explosives and propellants consist of energetic molecular crystals embedded in a polymer matrix that also includes additives, such as taggants, plasticizers, grit, antioxidants, etc. Furthermore, this heterogeneous composition renders these materials optically opaque, limiting optical characterization techniques (e.g., Raman spectroscopy) to probing chemical reactions at the sur-face. However, many reactions of interest are believed to occur inside the material where current optical techniques can not probe. To address this challenge, we have developed a new optical technique that utilizes feedback assisted wavefront shaping (using spatial light modulators) to focus probe light inside a heterogeneous material, such that we can perform subsurface spectroscopy. Recently, we demonstrated this technique by monitoring subsurface photodegradation and thermal degradation of Eu-doped molecular crystals using both fluorescence and Raman spectroscopy. Based off these successes we are now looking into improvements to this technique to allow for greater time resolution, including replacing our spatial light modulators with fast digital mirror devices, using ns optical phase conjugation, and self-healing optical beams.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Experiments and numerical modeling of surrogate droplet combustion for a 93 octane gasoline calibration fuel

Gasoline is the world’s most prolific liquid transportation fuel for ground transportation vehicles. Yet, a fundamental understanding of its burning characteristics is still lacking. Complicating this matter is the highly multicomponent nature of gasoline (comprised of hundreds of chemical species) and the seasonable variability of its composition due to variations in the crude oil feedstocks used in refining. The need to bring order to this complex problem has led to development of calibration fuels (Tier II in presented study) which on average will provide repeatable metrics that replicate performance of refined gasoline for various engine designs. This study presents results of experiments using the configuration of an isolated droplet to study the combustion dynamics of calibration fuel and three different surrogates (S4, S5, S6) for the calibration fuel. Droplet burning is under microgravity conditions, where gas transport is determined entirely by fuel evaporation in order to promote spherical symmetry in the burning process. The initial droplet diameters ranged between 0.55 mm and 0.60 mm. The droplets were spark-ignited and their burning histories were recorded by digital video cameras to obtain quantitative data. For each fuel, the evolutions of droplet, flame, and soot-shell diameters were measured to calculate the burn rates, flame-droplet diameter ratios (FSR) and soot-droplet diameter ratios. Additionally, a detailed numerical modeling is presented. This simulation includes multicomponent phase equilibrium, unsteady gas and liquid transport, radiative heat transfer, internal liquid diffusion and detailed combustion chemistry of different fuels during their burning process. Experiment results show that the calibration fuel and surrogates have a close burn rates and FSR, and S5 presents a relatively similar results with calibration fuel. Predictions from the numerical simulation using the surrogate are compared with experiment measurements, which showed a good agreement with the experiment results, including the droplet burning rate and FSR.

Guo, Songtao↗
DOE OSTI2022

Self-Sensing, Stretchable, Active Circuit Arrays: Liquid Metal Paste as a Combination Interconnect and Strain Sensor

Stretchable electrical interconnects and substrate materials are increasingly used to create flexible and con-formable electronic systems for soft robotics, smart textiles, and wearable devices. Wires fabricated using composite or liquid conductive inks encapsulated in silicone enable high strain and are mechanically robust. The resistance of these inter-connects increases with strain, which can present challenges for power delivery and signaling among electronic components. However, this effect can be used directly to estimate strain within a stretchable circuit without the need for additional sensor components. In this work, we present an approach for fabricating fully-stretchable, multi-layer active circuit arrays in silicone using liquid metal paste interconnects, in which power and data wires are re-purposed as strain sensors to estimate substrate deformation. As a proof-of-principle, we demonstrate a 3×3 active circuit array that self-senses relative voltage supply at each node to estimate deformation and consumes less than 10 mA per node. Each node is digitally addressable and performs both analog measurement and digital conversion. All interconnects are fabricated using liquid metal paste, and power, data, and sensing are performed over a shared, four-wire interface in this multi-layer stretchable printed circuit board.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Laser-induced digital oxidation for copper-based flexible photodetectors

Copper oxide compounds (Cu x O) with bandgaps of 1.3–2.1 eV (CuO) and 2.1–2.6 eV (Cu 2 O) have been investigated as promising p-type semiconducting materials. Cu x O is generally obtained by deposition or thermal oxidation, but those methods are not optimal for flexible substrates. Furthermore, additional patterning steps are required to fabricate devices. In this work, we present an easy, controllable method to fabricate a metal-semiconductor-metal (MSM) photodetector using laser-induced oxidation of a thin Cu film. After laser irradiation, the Cu film is heated under ambient conditions, and this leads to a thermal oxidation reaction, in which Cu oxide is monolithically formed in the Cu film and a Cu-Cu x O-Cu MSM structure is produced. Since the laser offers localized heating, an arbitrary CuxO pattern can be written in the Cu film by spatially controlled heating. In addition, by optimizing the heating time, the laser-induced oxidation can be successfully performed even on a flexible substrate. To study the laser-induced oxidation, we examined the correlation between laser parameters and the oxidation pattern and analyzed the composition using scanning electron microscopy, Raman spectroscopy, and X-ray photoemission spectroscopy. Furthermore, we measured the transient photoresponse and employed scanning photocurrent microscopy to investigate the mechanism of carrier transport behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Implementation and experimental validation of nonlocal damage in a large-strain elasto-viscoplastic FFT-based framework for predicting ductile fracture in 3D polycrystalline materials

Ductile materials, such as metal alloys, can undergo substantial deformation before failure. Additionally, these materials are usually of polycrystalline composition and exhibit strongly anisotropic behavior at small length scales. Previously developed fast Fourier transform (FFT)-based models can model ductile fracture of isotropic materials or the elastic–plastic behavior of anisotropic polycrystalline materials; however, there remains a need to couple both capabilities. This work extends a large-strain FFT-based crystal plasticity model to simulate ductile fracture of polycrystalline materials. In this work, a triaxiality-based continuum damage mechanics (CDM) formulation is incorporated into a large-strain elasto-viscoplastic FFT (LS-EVPFFT) framework. The CDM formulation is augmented with an integral-based nonlocal regularization approach that correctly handles gas-phase material necessary to model unconstrained surfaces. To validate the damage-enabled LS-EVPFFT framework, mesoscale copper tensile coupons were machined using microwire electrical discharge machining and experimentally characterized using electron backscatter diffraction. In-situ optical digital image correlation was performed during uniaxial testing to provide a side-by-side comparison of the experimental and computational strain fields and stress–strain responses. The damage-enabled LS-EVPFFT framework can simulate the complete macroscopic stress–strain response of ductile polycrystals to failure. The model reproduces necking behavior that qualitatively agrees with experimental observations. By leveraging the relatively low computational cost of the damage-enabled LS-EVPFFT framework, the framework presented here allows the ductile fracture response of 3D polycrystalline materials to be tractably predicted.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2025

Model Development and Analysis of a High-Fidelity Neutron Transport Sensor: The Quadrupole Detector Concept for Measurement of the Neutron Flux Gradient

Accurate reconstruction of the neutron flux distribution within a reactor core is essential for safe and efficient reactor operation. Traditional power shape synthesis in Light Water Reactors relies on hundreds of in-core detectors. However, this approach becomes impractical for Advanced Reactors and Microreactors due to limited space and harsh environments. To address this challenge, we propose a data-driven methodology that combines high-fidelity modeling with real-time ex-core sensor measurements, enabling the reconstruction of core power distribution while minimizing the reliance on intrusive in-core instrumentation. This project began in FY24 and achieved two initial milestones: (1) the definition of a three-year development plan for a Digital Twin framework and (2) the development of high-fidelity neutronics models of the Purdue University Reactor One (PUR-1) using both MCNP6 and OpenMC. The PUR-1 reactor, a zero-power facility, was selected due to its suitability for neutronics-focused modeling and the availability of experimental data for validation. Both models were benchmarked using neutron flux measurements obtained from irradiated gold foils, which were strategically placed within the core during a dedicated campaign in July 2024. This report marks the continuation and completion of those foundational tasks. The OpenMC model has been refined (improved geometric accuracy, expanded cross-section libraries, and refined sampling) and validated using additional experimental data. An updated sensor design—based on quadrupole configuration—was designed to measure both ex-core flux and its spatial gradient. These measurements will serve as inputs to a neural network-based reconstruction algorithm. Finally, the methodology was demonstrated on a two-dimensional test case representative of the heterogeneous material composition of the PUR-1 reactor core. A neural network implementation of the Kirchhoff-Helmholtz integral equation was employed to solve the boundary value problem using peripheral sensor measurements. The preliminary results confirm the strong potential of the proposed approach for accurate and minimally invasive neutron flux reconstruction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Large Volume Airborne Contamination Monitoring To Support Nuclear Processes' Deactivation and Decommissioning

Current D and D operations at Hanford have demonstrated a flaw in the current state of the art capability of defining airborne contamination boundaries - Airborne particulate emissions of Pu-239 from CM2H operations on the Hanford Pu Finishing Plant were detected well beyond areas controlled for airborne Pu, putting numerous workers at risk for radiological assimilations - Current air samplers and CAM systems were surveying insufficient volumes of air to accurately predict where respiratory protection was required - Hanford is located in a high radon environment, making air monitoring for alpha emitting actinides challenging in high alpha radon induced backgrounds. - Inexpensive HEPA based home and industrial air purifiers filter significantly higher volumes of air than commercially available continuous-air monitoring (CAM) systems. - Inexpensive models capable of filtered air volumes exceeding 1000 times that of a CAM. - Detector system can be built to detect x-rays generated from actinide decay. - Branching ratios of x-rays are 4 orders of magnitude more intense from Plutonium decay than its gamma emissions. - Detector system can be a low resolution systems, as x-ray region of interest is not terribly congested. - Detection systems evaluated will be designed based on slabs of NaI or pixelated NaI or CsI panels. - Simple graphical user interface software will be developed to operate the detection system. - Concept is to deploy some of these air-purifying systems in nonradiological areas around SRNL to confirm radon can be rejected with confidence. - Generate some filters contaminated with plutonium via electroplated Pu or lab generated simulated particles. - then deploy units in known airborne radiological environments to establish systems ability to accurately measure actinide based hot particles Lab analyses will follow up the system analyses to ensure hot particles were correctly identified. Analysis by Scintillation: - Application of scintillation media to the surface of the filters is being explored. - Evaluating slabs of ZnS(Ag), application of powdered ZnS(Cu) and spray on Perkin Elmer Enhance. - Scintillation events would then be digitized with a digital camera and quantified. - Comparing against sensitivity of a PMT or SiPD readout. - Currently evaluating a Thorlabs 8 Megapixel Monochrome Scientific CCD Camera, hermetically sealed cooled package with a wide angle lens. - Wide angle lens allows complete view of HEPA filter from 7 inches away. - Images taken of glow in the dark paint, as well as plutonium-induced fluorescence. - Plutonium was flamed mounted on a 1 inch diameter stainless steel planchet, covered with a layer of mylar and a section of scintillating ZnS obtained from Eljon. - Currently working on reducing signal-to-noise levels to boost sensitivity. - Dark box to hold contaminated or electroplated source covered filters under fabrication. Analysis by x-ray Spectroscopy: - Branching ratios for x-ray emissions from Plutonium isotopes are orders of magnitude more intense than gamma-ray emissions. - The nuclear databases are incomplete on the branching ratios of some of the isotopes. - Evaluating some custom built SrI2 x-ray spectrometers vs a conventional windowed NaI detector. - SrI2 detectors are carbon-composite-windowed 51 mm x 51 mm. - MCNP calculations to establish self absorbance of filter media on 17 keV x-ray. Radon Rejection: - Activated charcoal pre-filter acts as a Radon trap. - Adding a time lapse feature to camera to aid in radon rejection. SRS Plutonium Fuel Form D and D Operations: Currently deploying air sampler to D and D operation of Pu-238 facility at SRS to generate some field samples to analyze.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗