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DOE OSTI2020

Vibrational metrics for the evaluation of internal conditions in a scaled nuclear dry storage cask

In many countries, permanent repositories for high burnup spent nuclear fuel assemblies have yet to be established. As a result, much of the spent fuel from nuclear reactors is contained within dry storage casks, with many being beyond their designed service life. The assessment of the internal structural integrity of these casks and the fuel assemblies contained therein is of critical importance for both extended storage and transport to permanent repositories. The large size, structural complexity, and inaccessibility of the interior of the casks make this task challenging. To address these difficulties, a 1:6 scaled model based closely on the design of a Transnuclear (TN-32) dry storage cask was fabricated to facilitate controlled studies of these structures in the laboratory. Vibrational spectroscopy was used to evaluate the state of the cargo and internal structures within the cask utilizing only measurements on the outer surface. Using modes identified through Finite Element modeling corresponding to those previously measured on a full-scale TN-32 cask, we report on the development of amplitude- and phase-based metrics that are sensitive to internal conditions in the lab cask. Steel rod bundles and steel shot were used as surrogates for intact and damaged fuel assemblies, and various internal configurations of these materials were investigated. Finally, the metrics were based on acquired spectra involving the (1, 2) global bending mode and the (2, 1) radial-with-shearing mode. The results show that the metrics are sensitive to the condition of a single assembly and have some ability to determine the locations of damaged and empty slots.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Loads assessment of a fixed-bottom offshore wind farm with wake steering

Wake steering via deliberate yaw offset is an emerging wind farm control technique that has the potential to mitigate wake losses and further increase wind farm energy yield. The loads impact of this technique has been studied, but there is limited insight into wind-farm-wide impacts of wake steering. Understanding such impacts is crucial to determining the feasibility of using wake steering in commercial wind farms. To that end, this work investigates the impacts of wake steering on the loads of all turbine components across all turbines in a wind farm operating under a broad set of inflow conditions, including inflow velocity, shear exponent, turbulence class, and inflow angle. This was done by performing FAST.Farm simulations of a 12-turbine wind farm array, excerpted from a larger hypothetical wind farm. The International Energy Agency Wind 15-MW reference wind turbine was modeled atop a monopile substructure, an open-source model that closely approximates the properties of similar commercial options. Wake steering was included via yaw offsets that were computed using an offline optimization with the National Renewable Energy Laboratory tool FLORIS. For each inflow case, the 12-turbine array was simulated with and without wake steering. Results were compared in terms of time-averaged means, standard deviations, ultimate loads, and damage-equivalent loads. The findings show that because wake steering is generally applied at rated wind speeds and below, it is unlikely to drive ultimate loads. For fatigue loads, wake steering does increase the overall fatigue accumulation for some load channels, such as blade-root and shaft bending. This is to be expected when overall power yield increases but may cause the damage accumulation to be more uniform throughout the array. The significance of the added fatigue loading is dependent on how frequent wake steering is utilized in the overall set of inflow conditions across the wind rose.

17 WIND ENERGY↗

DOE OSTI2025

Structural basis of heme scavenging by the ChtA and HtaA hemophores in Corynebacterium diphtheriae

Corynebacterium diphtheriae causes diphtheria, a potentially fatal infectious disease that damages tissues in the upper respiratory tract. In order to proliferate, this pathogen acquires the essential nutrient iron from heme (iron-protoporphyrin IX) primarily found in human hemoglobin (Hb). C. diphtheriae secretes ChtA and HtaA hemophore proteins that bind ferric heme (hemin) via conserved region (CR) domains. Here, we demonstrate that their CR domains scavenge hemin after it is spontaneously released from Hb, and define the structural basis of hemin binding to ChtA and the N-terminal CR domain from HtaA by determining X-ray crystal structures of their protein-hemin complexes. Resonance Raman and electron paramagnetic resonance experiments demonstrate that the CR domains from ChtA and HtaA engage in pentacoordinate hemin binding through a conserved iron-tyrosyl linkage, though variations in their hemin pockets alter the way they stabilize the axial tyrosine and mask hemin’s metal. The importance of these interactions is probed using isothermal titration calorimetry experiments, which represent the first quantitative assessment of CR-hemin affinity and reveal that ChtA binds hemin via an enthalpically driven process. Hemin partitioning experiments using native mass spectrometry demonstrate that the cohort of CR domains within C. diphtheriae ’s hemin-uptake system have dissociation constants for hemin between 0.8 and 22 nM, raising the possibility that affinity differences contribute to the directional flow of hemin into the cell. Collectively, the results of this work provide insight into how C. diphtheriae and other pathogenic and commensal corynebacterium species utilize CR domains to scavenge iron rich hemin from their environment.

Corynebacterium diphtheriae↗

DOE OSTI2024

Impact of the Earthquake Rupture on Ground-Motion Variability of the 24 August 2016 M w 6.2 Amatrice, Italy, Earthquake

Here, the devastating 24 August 2016 M w 6.2 earthquake that struck Amatrice, Italy, marked the beginning of a prolonged seismic sequence dominated by three subsequent M w ≥6.0 events in the central Apennines region. The earthquake destroyed Amatrice’s historic center, claiming the lives of 299 individuals and causing widespread damage in the neighboring villages. The severity of the ground shaking, with a recorded maximum acceleration of 850 cm/s 2 on the east–west component at the Amatrice station, was far greater than the predicted acceleration based on the Italian ground-motion model (GMM). As pointed out by several investigations, the observed ground-motion amplitude and its spatial variability during the earthquake can be linked to specific rupture characteristics, including slip distribution and rupture directivity effects revealed by the observed data (Tinti et al., 2016; Pischiutta et al., 2021). In this study, we conducted physics-based 3D numerical simulations of ground motion for the Amatrice earthquake for frequencies up to 3 Hz. We employed a series of kinematic rupture models and a well-constrained local 3D velocity model incorporating surface topography. The kinematic rupture realizations were generated using multiscale hybrid and fully stochastic models, following the technique proposed by Graves and Pitarka (2016). We focused on assessing the sensitivity of near-fault ground-motion amplitudes to earthquake rupture characteristics, in particular, the spatial slip pattern. To evaluate the quality of our simulations, we employed goodness-of-fit measurements performed in comparisons of simulated and recorded ground motions. The simulated ground motions compare well with the recorded data and predictions from GMMs for Italy, ITA18 (Lanzano et al., 2019). However, we found that the simulated interevent ground-motion variability (randomness in the source process) of peak ground velocity, σ (PGV) is higher than the constant σ (PGV) predicted by conventional GMMs. Our simulations using several rupture scenarios demonstrate that the near-fault ground-motion amplification pattern is directly related to the slip distribution pattern.

3D Ground Motions Simulations↗

DOE OSTI2020

Genomic analyses implicate noncoding de novo variants in congenital heart disease

A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; P = 8.7 × 10 -4 ). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, P = 1 × 10 -5 ). We observed significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1-5.0, P = 5.4 × 10 -3 ). CHD DNVs altered transcription levels in 5 of 31 enhancers assayed. Finally, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1-1.2, P = 8.8 × 10 -5 ). Overall, our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Exploring acute weather resilience: Meeting resilience and renewable goals

We report the United States is affected by an average of almost seven severe weather events a year, often resulting in billions of dollars in physical and economic damages, a subset of which are related to grid outages. There is a need for power and energy system stakeholders to better understand and implement the strategies that help reduce net-economic and societal consequences associated with grid outages by improving the resilience of their systems. In addition, there are incentives to reduce emissions and meet climate goals, several pathways of which include resilient technologies. Including resilience constraints and metrics in energy system planning models may help inform the design of more resilient systems that are also more renewable and sustainable. This paper reviews qualitative definitions of resilience, quantitative approaches to resilience, recent examples of the inclusion of resilience in energy system models with respect to acute climatological threats, and the gaps in fully articulating resilience in current modeling tools. We then outline steps to effectively improve resilience considerations against such threats into energy sector modeling tools. Based on the findings, the authors propose a novel framework for energy system resilience assessment and future areas of research to bridge the current modeling gaps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Fiber-optic sensor packaging assessment and benchmarking for embedded sensing applications

Fiber optic-based sensing for non-destructive evaluation (NDE) and structural health monitoring of various infrastructure and energy assets is an increasingly important sensing scheme. Because of their immunity to electromagnetic interferences, versatility in sensing mechanisms and designs as well as capability for distributed sensing, the fiber optic sensing is of great interest for embedding within infrastructures using advanced manufacturing methods. However, the protection of fiber optic sensors during advanced manufacturing-based embedding is an important aspect for infrastructure monitoring in practice. While successful installation of fiber optic sensors has been accomplished through application of epoxy or adhesives on infrastructure surfaces, there has been only limited investigation of fiber protection packaging schemes which are compatible with in-situ repair techniques such as the metallic cold-spray. With increasing interest and importance of cold spray-based repair processes, the integration of fiber optic sensors simultaneously during the repair is equally important. In this work, we focus our efforts on investigating various types of packaging schemes for their compatibility and integrability on the surfaces of metallic structures such as oil and gas pipelines during an in-situ repair coating. First, we consider sandblasting using silica sand particles of various size for optical fibers on the surface of a metallic coupon as a useful proxy test of sensor packaging stability. Structural damage of packaging is characterized by means of optical microscopy and the optical integrity of embedded fiber optic cables is examined using optical backscatter reflectometry (OBR). In addition, initial investigation of metallic cold-spray embedded fiber optic cables under various packaging on metallic substrates is also considered with early conclusions and future directions.

Fiber-optic sensors, Sensors packaging, Metallic C↗

DOE OSTI2022

The Effect of APS-HVOF Bond Coating Thickness Ratio on TBC Furnace Cycle Lifetime

In this work, combinations of NiCoCrAlY APS and high-velocity oxygen fuel (HVOF) bond coatings were deposited on alloy 247 disk substrates with APS yttria-stabilized zirconia (YSZ) top coatings to assess the benefit of air plasma sprayed (APS) ‘flash’ bond coatings. Using 1-h cycles at 1100 °C in air with 10% H 2 O and HVOF-only and APS-only bond coatings as a baseline, it was found that APS flash coatings extended the average coating lifetime by 16% to 35% with the thicker flash coating performing best. Principal component analysis and energy dispersive spectroscopy (EDS) compositional mapping on coatings characterized after 0, 100, 300 and 500 cycles and after failure showed that the Al in the bond coatings was depleted due to both oxidation and back diffusion into the substrate. The APS-only bond coating had significant oxidation throughout the bond coating and was so depleted in Al, that Al diffused from the substrate to the coating. Residual stress maps of the thermally-grown alumina scale were obtained every 100 cycles using photo-stimulated luminescence piezospectroscopy (PLPS) revealing that the Thick Flash coating had the slowest rate of damage accumulation in the oxide scale.

alumina scale↗

DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗

DOE OSTI2021

A Markov framework for generalized post-event systems recovery modeling: From single to multihazards

State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.

42 ENGINEERING↗

DOE OSTI2024

Specific iron binding to natural sphingomyelin membrane induced by non-specific co-solutes

Sphingomyelin (SPM), a crucial phospholipid in the myelin sheath, plays a vital role in insulating nerve fibers. We hypothesize that iron ions selectively bind to the phosphatidylcholine (PC) template within the SPM membrane under near-physiological conditions, resulting in disruptions to membrane organization. These interactions could potentially contribute to the degradation of the myelin sheath, thereby playing a role in the development of neurodegenerative diseases. We utilized synchrotron-based X-ray spectroscopy and diffraction techniques to study the interaction of iron ions with a bovine spinal-cord SPM monolayer (ML) at the liquid-vapor interface under physiological conditions. The SPM ML serves as a model system, representing localized patches of lipids within a more complex membrane structure. The experiments assessed iron binding to the SPM membrane both in the presence of salts and with additional evaluation of the effects of various ion species on membrane behavior. Grazing incidence X-ray diffraction was employed to analyze the impact of iron binding on the structural integrity of the SPM membrane. Furthermore, our results demonstrate that iron ions in dilute solution selectively bind to the PC template of the SPM membrane exclusively at near-physiological salt concentrations (e.g., NaCl, KCl, KI, or CaCl 2 ) and are pH-dependent. In-significant binding was detected in the absence of these salts or at near-neutral pH with salts. The surface adsorption of iron ions is correlated with salt concentration, reaching saturation at physiological levels. In contrast, multivalent ions such as La 3+ and Ca 2+ do not bind to SPM under similar conditions. Notably, iron binding to the SPM membrane disrupts its in-plane organization, suggesting that these interactions may compromise membrane integrity and contribute to myelin sheath damage associated with neurological disorders.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Tensile and fatigue characterization of multifunctional composites

This research is part of a larger effort to develop advanced self-sensing multifunctional polymer composites that are both lightweight and high-strength, while also enabling structural damage detection, fatigue cycle monitoring, and service life prediction. These multifunctional composites are particularly sought after in the automotive industry for their potential to significantly reduce vehicle weight and simultaneously provide additional functionality like condition monitoring to enhance safety. This study examines the tensile and fatigue properties of a composite material composed of acrylonitrile butadiene styrene (ABS) polymer embedded with piezoelectric barium titanate (BaTiO3) nanoparticles. The integration of BaTiO3 nanoparticles not only supplies the material with self-sensing capabilities but also influences its mechanical properties. While a high content of BaTiO3 nanoparticles is desired to enhance sensing capacity, the brittle nature of such materials causes concerns of decreased strength characteristics. To explore this, various composite samples were fabricated with nanoparticle contents ranging from 0 wt% to 20 wt%. These samples underwent tensile testing to measure their ultimate tensile strengths and Young’s moduli. Following this, fatigue tests were conducted to generate S-N curves, which are essential for understanding the material's durability under cyclic loading. The findings from these tests assess the impact of nanoparticle content on the composite’s tensile strength and fatigue life, providing essential insights that can guide the optimization and design of future self-sensing multifunctional composites. The results suggest that 5 wt% BaTiO3 provides an optimal balance between mechanical properties and nanoparticle concentration, making it a promising composition for semi-structural applications.

Bowland, Christopher [ORNL] (ORCID:000000021229431↗

DOE OSTI2023

Finding Of Adverse Effect and Proposed Mitigation for the Removal of Joshua Trees at Building 23-210, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) recently removed two fallen Joshua trees that were part of an Accessory Resource (AR) to the former Department of Defense (DOD) Motor Pool Maintenance Compound in Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. C307) on the Nevada National Security Site (NNSS) in Nye County, Nevada (Figure 1). The Joshua trees were part of AR3, a landscaped feature at the entrance to the compound. There are two remaining Joshua trees at risk of falling which may require removal in the future. The NNSA/NFO is proactively consulting with the SHPO regarding the potential removal of the remaining two trees. The purpose of the undertaking is to ensure that dead or dying Joshua trees do not cause damage to the remaining landscape elements, nor injury to NNSS employees. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-210 was the DOD’s motor vehicle maintenance compound constructed in 1951. The compound included its own repair shop and fuel station. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (National Register, NRHP) as the Mercury Historic District (MHD, SHPO Resource No. D230) under Criteria A and C for their importance in supporting nuclear testing and scientific research from 1951 through 1992. The former DOD Motor Pool Compound was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al. 2018) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). The foundation for Building 23-210 was also identified in Appendix C of the Mercury PA as a Category III contributing element with three contributing ARs. Category III properties are those that may include elements for which there are several representatives in the MHD, such as foundations, and those elements which possess characteristics that are not unique to the MHD and are commonly found in other non-NNSS facilities. The former DOD compound, which is identified by the foundation for the Building 23-210 and its ARs, is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Unified Model of Current-Limiting Grid-Forming Inverters for Large-Signal Analysis

Grid-forming (GFM) inverters can hardly withstand any overloading. As such, GFM inverters need a current limiter in their control system to avoid hardware damage during overloading or grid disturbances. When the current limiter is engaged for inverter protection, however, it dynamically changes the inverter output impedance, which can significantly affect the system behavior against large disturbances. For example, when the current limiter curtails the output power, the primary controller of the GFM inverter-which generates the voltage and angle reference-is prone to losing synchronization with the grid. This can cause large-signal instability in the network. This paper presents a unified GFM current-limiter model to gain a deeper understanding of the impact of the GFM inverter current limiting on large-signal instability and other system behaviors. The unified model captures several well-known current limiters for GFM inverters, and it reveals their large-signal equivalence characterized by the limiter angle. We use the unified model to present in-depth insights into the driving forces that cause large-signal instability in GFM inverter-driven grids. Further, this paper illustrates that the unified model can be used as a tool for assessing the system-level impacts of various GFM current-limiter designs during grid disturbances. Using numerical, analytical, electromagnetic transient models, and a hardware setup, the validity of the unified model is evaluated.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗

DOE OSTI2026

Comparison of time-resolved photoluminescence and deep-level transient spectroscopy defect evaluations in an InAs nBn detector subjected to in situ and ex situ 63 MeV proton irradiation

Deep-level transient spectroscopy and temperature-dependent time-resolved photoluminescence experiments are performed on identical InAs nBn photodetector structures as a function of in situ and ex situ 63 MeV proton irradiation to assess their generation and recombination dynamics. Pre-irradiation, the n-type InAs absorbing region, exhibits a steadily increasing minority carrier lifetime with increasing temperature, providing evidence that excited minority carriers may be recombining via shallow defect levels. From deep-level transient spectroscopy, two features are found between 10 and 275 K: a low temperature broad “shoulder,” which suggests emission from multiple shallow electron defect levels with energies <29 meV and a high temperature minimum occurring at ∼230 K with an activation energy of 539 meV, which suggests a defect in the barrier layer in the device. Two similar nBn detectors are then subjected to 63 MeV proton irradiation in step doses and measured between steps. One experiment is performed in situ with an nBn held at ∼10 K during dosing, and the other experiment is performed ex situ with a similar nBn held at room temperature for dosing. The ex situ dosing results in an evaluation of the defect introduction rate that is three to four times lower than in situ due to partial annealing of the proton-induced displacement damage at room temperature. The results of these two experiments are then compared with the dose-dependent recombination rate analysis, resulting in an estimated recombination defect cross section of 1.6 × 10 −13 cm 2 for the shallow shoulder defect.

Carrasco, Rigo A. [Air Force Research Laboratory (↗

DOE OSTI2024

Temporal serum neurofilament light chain concentrations in sheep inoculated with the agent of classical scrapie

Objective: Neurofilament light chain (Nf-L) has been used to detect neuroaxonal damage in the brain caused by physical injury or disease. The purpose of this study was to determine if serum Nf-L could be used as a biomarker for pre-symptomatic detection of scrapie in sheep. Methods: Four sheep with prion protein genotype AVQQ were intranasally inoculated with the classical scrapie strain x124. Blood was collected every 4 weeks until 44 weeks post-inoculation, at which point weekly collection commenced. Serum was analyzed using single molecule array (Quanterix SR-X) to evaluate Nf-L concentrations. Results: Scrapie was confirmed in each sheep by testing homogenized brainstem at the level of the obex with a commercially available enzyme immunoassay. Increased serum Nf-L concentrations were identified above the determined cutoff during the last tenth of the respective incubation period for each sheep. Throughout the time course study, PrP Sc accumulation was not detected antemortem by immunohistochemistry in rectal tissue at any timepoint for any sheep. RT-QuIC results were inconsistently positive throughout the timepoints tested for each sheep; however, each sheep had at least one timepoint detected positive. When assessing serum Nf-L utility using receiver operator characteristic curves against different clinical parameters, such as asymptomatic and symptomatic (pruritus or neurologic signs), results showed that Nf-L was most useful at being an indicator of disease only late in disease progression when neurologic signs were present. Conclusion: Serum Nf-L concentrations in the cohort of sheep increased as disease progressed; however, serum Nf-L did not increase during the presymptomatic window. The levels increased substantially throughout the final 10% of the animals’ scrapie incubation period when other clinical signs were present. Serum Nf-L is not a reliable biomarker for pre-clinical detection of scrapie.

59 BASIC BIOLOGICAL SCIENCES↗