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DOE OSTI2024

Overview of T and D–T results in JET with ITER-like wall

In 2021 JET exploited its unique capabilities to operate with T and D–T fuel with an ITER-like Be/W wall (JET-ILW). This second major JET D–T campaign (DTE2), after DTE1 in 1997, represented the culmination of a series of JET enhancements—new fusion diagnostics, new T injection capabilities, refurbishment of the T plant, increased auxiliary heating, in-vessel calibration of 14 MeV neutron yield monitors—as well as significant advances in plasma theory and modelling in the fusion community. DTE2 was complemented by a sequence of isotope physics campaigns encompassing operation in pure tritium at high T-NBI power. Carefully conducted for safe operation with tritium, the new T and D–T experiments used 1 kg of T (vs 100 g in DTE1), yielding the most fusion reactor relevant D–T plasmas to date and expanding our understanding of isotopes and D–T mixture physics. Furthermore, since the JET T and DTE2 campaigns occurred almost 25 years after the last major D–T tokamak experiment, it was also a strategic goal of the European fusion programme to refresh operational experience of a nuclear tokamak to prepare staff for ITER operation. The key physics results of the JET T and DTE2 experiments, carried out within the EUROfusion JET1 work package, are reported in this paper. Progress in the technological exploitation of JET D–T operations, development and validation of nuclear codes, neutronic tools and techniques for ITER operations carried out by EUROfusion (started within the Horizon 2020 Framework Programme and continuing under the Horizon Europe FP) are reported in (Litaudon et al Nucl. Fusion accepted), while JET experience on T and D–T operations is presented in (King et al Nucl. Fusion submitted).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Infrastructure-Based Cooperative Perception at a Traffic Intersection: Overview and Challenges: Preprint

Recent advancement in autonomous driving vehicles and V2X communication has attracted increasing attention towards Intelligent Transportation Systems to build a safe and reliable traffic intersection. However, most of the systems are still at the initial stages and require significant progress to become a reality. This paper presents an overview of NREL Infrastructure Perception and Control (IPC) framework which is an open-source track-data fusion engine which takes input from infrastructure-based perception sensors and cooperatively shared messages from Connected Autonomous Vehicles (CAVs) and Connected Vehicle (CVs) and the challenges associated with deploying such cooperative perception framework at a four-way traffic intersection in the city of Colorado Springs, CO, USA. The sensor data is collected by deploying two radars and two LiDAR sensors on the IPC mobile lab and two radars on diagonally opposite traffic poles at the proposed intersection. The sensor output results imply the need for rapid sensor calibration to bring the collective perception to a common coordinate frame, the importance of time synchronization between the sensors in order to capture accurate spatial and temporal alignment of the objects, and the need for a health monitoring system with fail safe closed-loop detection model for real-time deployment.

camera↗

DOE OSTI2022

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This presentation provides an overview of a recently initiated project "Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities" which will (1) develop and demonstrate data-driven process controls at full-scale facilities for five promising WRRF Applications (i.e., process technologies) that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) create a toolbox of new process control approaches and an implementation guide including five examples for application at utilities. The presentation also provides a detailed overview of the research approach and progress being made on one of the five Applications, namely Application 2: Biological Nutrient Removal (BNR): ammonium-based aeration control (ABAC) / ammonia vs. NOx (AvN) + partial denitration with anammox (PdNA), which is being implemented at Hampton Roads Sanitation District. This project is a collaboration of work being conducted by DC Water, Hampton Roads Sanitation District, Metro Water Recovery, University of Michigan, Northwestern University, US Military Academy - West Point, Black & Veatch, and Oak Ridge National Laboratory. Research partner: U.S. Department of Energy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Interactions Between Diabetes Mellitus and Osteoarthritis: From Animal Studies to Clinical Data

Diabetes mellitus (DM) and osteoarthritis (OA) are commonly known metabolic diseases that affect a large segment of the world population. These two conditions share several risk factors such as obesity and aging; however, there is still no consensus regarding the direct role of DM on OA development and progression. Interestingly, both animal and human studies have yielded conflicting results, with some showing a significant role for DM in promoting OA, while others found no significant interactions between these conditions. In this review, we will discuss preclinical and clinical data that assessed the interaction between DM and OA. We will also discuss possible mechanisms associated with the effect of high glucose on the articular cartilage and chondrocytes. An emerging theme dominates the breath of published work in this area: most of the studies discussed in this review do not take into consideration the role of other factors such as the type of diabetes, age, biological sex, type of animal model, body mass index, and the use of pain medications when analyzing and interpreting data. Therefore, future studies should be more rigorous when designing experiments looking at DM and its effects on OA and should carefully account for these confounding factors, so that better approaches can be developed for monitoring and treating patients at risk of OA and DM.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 3

This project is a collaborative research effort between PKMJ Technical Services LLC, Idaho National Laboratory, and Public Service Enterprise Group (PSEG) Nuclear, LLC. The collaboration, led by PKMJ Technical Services LLC, is part of the industry Funding Opportunity Announcement (FOA) award under Advanced Nuclear Technology Development FOA #DE-FOA-0001817. The pilot demonstration focuses on the Circulating Water System (CWS), an important non-safety-related system that impacts the power generation capability of the plant site. Achieving riskinformed condition-based Predictive Maintenance (PdM) on the CWS will result in significant economic benefits, and the developed methodologies can also be applied to other plant systems. This approach supports an industry goal of ensuring that nuclear power generation remains a viable, economically competitive option in the energy market. Operation and Maintenance (O&M) costs include labor-intensive Preventive Maintenance (PM) programs that involve manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies as well as time-based replacement of assets, irrespective of condition. This project offers an alternative by focusing on riskinformed condition-based maintenance to reduce O&M costs while still maintaining plant health and safety. This report summarizes the progress made toward achieving a risk-informed condition-based maintenance approach. The research and development (R&D) activities presented in this report are associated with development of a nuclear digital platform application, integration of fault signature models, and automated work management processes. The fault signatures and Machine Learning (ML) models are key components in predictive analytics and are heavily leveraged to improve the insights received by existing plant process data sources. Availability of the analysis results within a centralized digital platform enhances efficiency by enabling automation of activities otherwise performed manually. Personnel are presented with enhanced information that can be used to evaluate plant status and risks. Utilizing the enhancements to data analytics supports automated responses, (i.e. issuance of work orders) to address developing equipment faults and thus preventing forced, unplanned shutdowns of components or systems. The R&D activities described within this report lay the foundation for developing and demonstrating a digital automated platform to centralize the implementation of condition monitoring and response to equipment faults. The digital automated platform is cloud-based and designed to enable improved efficiency of plant processes. The digital platform includes content related to maintenance optimization, fault signature analysis, and plant records, which can all be used to support efficiencies when located within a centralized digital platform. These efficiencies could be further enhanced when deployed through industry-wide deployment of the technology to improve insights and processes based upon economies of scale.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Progress Toward Simulating Departure from Nucleate Boiling at High-Pressure Applications with Selected Wall Boiling Closures

Recently, a Eulerian-based two-fluid computational fluid dynamics (CFD) framework with a wall heat flux partitioning approach has been intensively investigated for departure from nucleate boiling (DNB) simulation under the U.S. Department of Energy–funded Consortium for Advanced Simulation of Light Water Reactors (CASL) program. Understanding of the DNB characteristics over a range of pressurized water reactor–like operating conditions and accurate prediction of boiling crisis in the nuclear power system have been grand challenges because of the large impact of DNB on reactor safety and operational economics. The ultimate goal of this task in the CASL program is to introduce a robust multiphase CFD–based DNB modeling framework that is capable of characterizing an entire boiling history in which the wall boiling mode experiences the following through multiple stages of heat transfer mode: (1) single-phase convective heat transfer, (2) nucleate boiling heat transfer, and (3) identification of the departure of nucleate boiling. To validate the CASL boiling model, we have benchmarked simulated DNB over three different flow channel configurations (pipe flow, 5 × 5 fuel bundle with mixing vane tests, and 5 × 5 fuel bundle without mixing vane tests) against experimental measurements, and the validation result with open literature is reported. The DNB detection criteria in the simulation are checked by monitoring the peak wall temperature, wall dryout factor, and net energy balance. In addition to the DNB performance test, some preliminary sensitivity results on closure model selection are reported to address the prediction capability of local void profile against measurements. The boiling simulation tested in this study exhibits a maximum deviation of 24% from the measured DNB value in a high-pressure (i.e., 138 bars) subcooled pipe flow test. The ranges of operating conditions are as follows: 1650 to 2650 kg/m 2 ·s for mass flux and 8.5 to 96 K for subcooled inlet temperature. The deviation is even reduced to 7% when the subcooled temperature is less than 40 K. Besides accuracy, base practice guidelines for DNB detection criteria are tested by monitoring three simulation variables: (1) maximum wall temperature, (2) wall dryout factor (i.e., K-value), and (3) energy balance. Numerical robustness of DNB simulation is largely achieved in most of the validation test except for a few high subcooled test cases.

42 ENGINEERING↗

DOE OSTI2024

Evaluating Optical Techniques to Characterize Solid State Samples for the Pu-238 Supply Program

This report documents steps that have been taken to establish advanced characterization capabilities for solid-state samples to support the 238 Pu Supply Program. The program has already invested significantly in spectroscopy and online monitoring development for liquid processes. Many of the data, analytical methods, and equipment could also be leveraged for the analysis of Np/Pu oxides and Np pellets to help classify material properties and link them to operations. The techniques described in this work include diffuse reflectance spectroscopy (DRS), Raman spectroscopy, and laser-induced breakdown spectroscopy (LIBS). Each technique is sensitive to various chemical and/or elemental species and can provide complimentary information to more standard methods such as powder x-ray diffraction and scanning electron microscopy. These options are remotely deployable via fiber-optic cables. DRS and Raman spectroscopy are already approved for glove box and hot cell work, but LIBS requires US Department of Energy approval for incorporation. Raman spectroscopy does require laser shielding, which means that DRS is the easiest method to implement. However, each technique provides complimentary data, and the most robust sample characterization could be achieved when all three, among other methods, are harnessed together. This report documents some of the progress made testing these capabilities, some of the challenges, and next steps to optimize the technology and integration strategies.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗

DOE OSTI2020

Functional metal–organic frameworks as effective sensors of gases and volatile compounds

Developing efficient sensor materials with superior performance for selective, fast and sensitive detection of gases and volatile organic compounds (VOCs) is essential for human health and environmental protection, through monitoring indoor and outdoor air pollutions, managing industrial processes, controlling food quality and assisting early diagnosis of diseases. Metal–organic frameworks (MOFs) are a unique type of crystalline and porous solid material constructed from metal nodes (metal ions or clusters) and functional organic ligands. They have been investigated extensively for possible use as high performance sensors for the detection of many different gases and VOCs in recent years, due to their large surface area, tunable pore size, functionalizable sites and intriguing properties, such as electrical conductivity, magnetism, ferroelectricity, luminescence and chromism. The high porosity of MOFs allows them to interact strongly with various analytes, including gases and VOCs, thus resulting in easily measurable responses to different physicochemical parameters. Although much of the recent work on MOF-based luminescent sensors have been summarized in several excellent reviews (up to 2018), a comprehensive overview of these materials for sensing gases and VOCs based on chemiresistive, magnetic, ferroelectric, and colorimertic mechanisms is missing. In this review, we highlight the most recent progress in developing MOF sensing and switching materials with an emphasis on sensing mechanisms based on electricity, magnetism, ferroelectricity and chromism. We provide a comprehensive analysis on the MOF–analyte interactions in these processes, which play a key role in the sensing performance of the MOF-based sensors and switches. We discuss in detail possible applications of MOF-based sensing and switching materials in detecting oxygen, water vapor, toxic industrial gases (such as hydrogen sulfide, ammonia, sulfur dioxide, nitrous oxide, carbon oxides and carbon disulfide) and VOCs (such as aromatic and aliphatic hydrocarbons, ketones, alcohols, aldehydes, chlorinated hydrocarbons and N,N'-dimethylformamide). Altogether, this review serves as a timely source of information and provides insight for the future development of advanced MOF materials as next-generation gas and VOC sensors.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Impact of Molecular Design on Degradation Lifetimes of Degradable Imine-Based Semiconducting Polymers

Transient electronics are a rapidly emerging field due to their potential applications in the environment and human health. Recently, a few studies have incorporated acid-labile imine bonds into polymer semiconductors to impart transience; however, understanding of the structure–degradation property relationships of these polymers is limited. In this work, we systematically design and characterize a series of fully degradable diketopyrrolopyrrole-based polymers with engineered sidechains to investigate the impact of several molecular design parameters on the degradation lifetimes of these polymers. By monitoring degradation kinetics via ultraviolet–visible spectroscopy, we reveal that polymer degradation in solution is aggregation-dependent based on the branching point and M n , with accelerated degradation rates facilitated by decreasing aggregation. Additionally, increasing the hydrophilicity of the polymers promotes water diffusion and therefore acid hydrolysis of the imine bonds along the polymer backbone. The aggregation properties and degradation lifetimes of these polymers rely heavily on solvent, with polymers in chlorobenzene taking six times as long to degrade as in chloroform. We develop a new method for quantifying the degradation of polymers in the thin film and observe that similar factors and considerations (e.g., interchain order, crystallite size, and hydrophilicity) used for designing high-performance semiconductors impact the degradation of imine-based polymer semiconductors. We found that terpolymerization serves as an attractive approach for achieving degradable semiconductors with both good charge transport and tuned degradation properties. This study provides crucial principles for the molecular design of degradable semiconducting polymers, and we anticipate that these findings will expedite progress toward transient electronics with controlled lifetimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements

Estimates of the power level of a nuclear reactor based on measurements from an independent monitoring sensor system can help in the compliance verification of its declared operations. We present a three-level fusion method to estimate the power level of a nuclear reactor using features derived from infrared, electromagnetic, and acoustic sensor measurements collected in proximity to the reactor. Based on a simplified analytical model of the secondary coolant system of the reactor, we identify partial regression functions of the power level in terms of the temperature difference between inlet and outlet of coolant pipes, and the activity levels of four fans and four pumps, which are estimated as features from the sensor measurements. The power level estimator employs a combination of aggregate and complementary fusion steps at three levels to incorporate the multi-modal features in a structure that reflects the secondary cooling system and its partial regression functions. Using the measurements from a test campaign at an operational reactor, we show that this estimator achieves 3.47% or lower root mean square error under 5-fold cross validation. More generally, these results illustrate a progressive reduction in estimation error as additional modalities are appropriately incorporated, and that the fuser outperforms single modality features and their sub-combinations.

Rao, Nageswara↗

DOE OSTI2021

Spatial distributions of X CO 2 seasonal cycle amplitude and phase over northern high-latitude regions

Satellite-based observations of atmospheric carbon dioxide (CO 2 ) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO 2 (X CO 2 ) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed X CO 2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of X CO 2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of X CO 2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of X CO 2 from the GEOS-Chem CO 2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72° N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of X CO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest X CO 2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30d. The correlation of X CO 2 SCA with these land surface contact tracers is stronger than the correlation of X CO 2 SCA with the SCA of CO 2 fluxes or the total annual CO 2 flux within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO 2 flux during atmospheric transport is a major driver of regional variations in X CO 2 SCA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Degradation science: Integrating modeling and experiments to predict localized corrosion processes (Annual Progress Report)

Additively manufactured (AM) eutectic high-entropy alloys (EHEAs), such as nano-lamellar AlCoCrFeNi 2.1 , have excellent strength, ductility, and wear resistance even at elevated temperatures, but their corrosion behavior in aggressive acids at different length scales remain poorly understood. This work investigates the corrosion behavior of laser powder bed–fused (L-PBF) AlCoCrFeNi 2.1 as a function of annealing temperatures, probing degradation mechanisms from nanoscopic to macroscopic length scales. The alloy is dual phase consisting of a ductile FCC L1 2 phase and a high-strength BCC B2 phase. Rapid solidification during L-PBF produces a far-from-equilibrium nano-lamellar structure with nearly homogeneous elemental distribution, which tends to evolve upon annealing toward Cr/Co/Fe-enriched FCC and Al/Ni-enriched B2. Three conditions were studied: as-printed, 600 °C/5 h, and 1000 °C/1 h, over which B2 lamellae coarsen, lamellar spacing increases, and elemental segregation becomes more prominent. Microstructure and chemistry were characterized by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), while in-situ electrochemical atomic force microscopy (EC-AFM) was used to link early (<5 h) local dissolution to microstructure after exposure in sulfuric acid. EC-AFM highlights preferential dissolution of the BCC/B2 phase where the surrounding matrix is Cr-depleted and directly quantifies the dissolution rates within individual phases, tracks the transition from early nano-scale attack to partial repassivation, to correlate height differences with current and impedance responses. To monitor longer-term behavior (up to 96 h), ex-situ AFM, SEM, and confocal imaging were combined with conventional bulk electrochemical tests, bridging nanoscale observations to micro/meso-scale damage morphologies. At the meso-scale, the deepest dissolution channels align with the build-direction lamellae and melt-pool boundaries, indicating that printing directionality guides the propagation of these localized corrosion sites. Annealing modifies corrosion by restructuring BCC/FCC phase fractions, lamellar spacing, and Cr/Al segregation, thereby changing the cathode/anode ratio and passive film stability. The results clarify how as-printed and annealed nano-lamellar architectures differ in their susceptibility to selective dissolution; how elemental segregation competes with residual stresses along the build direction. With these insights, future work will use CALPHAD-guided alloy modification to stabilize higher Cr contents in the B2 phase while retaining a dominant FCC+B2/BCC microstructure, with the goal of designing mechanically robust, corrosion-resistant EHEAs for safety-critical applications to leverage the LLNL’s broader national and global security mission.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Applying Two Active Acoustic Technologies to Document Presence of Large Marine Animal Targets at a Marine Renewable Energy Site

Coastal regions are highly used by humans. The growing marine renewable energy (MRE) industry will add to existing anthropogenic pressures in these regions. Regulatory bodies require animal risk assessment before new industrial activities can progress, and MRE is no exception. Preliminary data of marine mammal use of an MRE device deployment location could be informative to permitting. A combination of downlooking hydroacoustics using an echosounder and acoustic camera (imaging sonar) was used to provide a number of large targets (proxy for large fish and marine mammals) in an area of interest for MRE tidal turbine deployment in Western Passage, Maine, USA. Data were collected in May, June, August, and September of 2010 and 2011. Of the nine large targets confirmed to be animals, eight were porpoises and one was a shark. Few large targets were observed in May and June, with the majority (90%) being present in August and September of both years. The most large targets were observed when tidal current speed was less than 1 m·s−1. These data provide a preliminary assessment of large targets in a single location over sixteen 24-h surveys. The aforementioned methodology could be used for future pre- and post-installation assessments at MRE device deployment locations. Their use in concert with visual and passive acoustic monitoring can provide water depth usage by marine mammals, which is a metric that is difficult to assess with passive acoustic and visual techniques.

Staines, Garrett↗

DOE OSTI2022

Windowless Optical Cell Flow System

One of the goals for advanced integrated nuclear safeguards is on-line optical analysis of molten salt in molten salt reactors and electrochemical fuel reprocessing facilities. While progress has been made in the development of optical techniques that may be suitable for on-line analysis of molten salts, significant technology gaps exist with regard to integrating these analytical techniques into industrial-scale processes. One key technological hurdle is the short service life of optical window materials, which are subject to clouding and scaling during extended exposure to molten salts. To address this issue, Argonne is developing a gravity flow cell with open-apertures to support on-line optical analysis without the need for salt-wetted windows. The flow cell uses gravity flow and fluid dynamics principles to enable molten salt flow through a pipe that has holes in its wall through which salt does not flow. In FY21, an open-aperture gravity flow cell with windowless optical access to flowing salt was successfully demonstrated. Future work should include the integration of the flow cell into a sampling loop, stakeholder outreach and collaboration, integration of multiple optical analysis techniques, evaluation of the integrated methods, and analyses to determine how the various tools might fit into an integrated safeguards monitoring system of unattended near real time monitoring tools.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Sex comparisons in physiological and cognitive performance during hypoxic challenge

Within the tactical aviation community, human performance research lags in considering potential psychophysiological differences between male and female aviators due to little inclusion of females during the design and development of aircraft systems. A poor understanding of how male and female aviators differ with respect to human performance results in unknown potential sex differences on aeromedically relevant environmental stressors, perchance leading to suboptimal performance, safety, and health guidelines. For example, previous hypoxia studies have excluded female participants or lacked a sizeable sample to examine sex comparisons. As such, progress toward sensor development and improving hypoxia familiarization training are stunted due to limited knowledge of how individual differences, including sex, may or may not underlie hypoxia symptoms and performance impairment. Investigating sex differences bridges the gap between aerospace medicine and operational health, and addressing hypoxia is one of many facets yet to be studied. In the current study, we retrospectively examined N = 6 hypoxia studies with male-female participant samples (total, N = 189; male, n = 118; female, n = 71). We explored sex as a predictor of physiological response, sensory deficits, the severity of cognitive performance declines, and symptom manifestation via linear and binary logistic regression models. We found that the female sex predicted lower peripheral oxygen saturation and the likelihood of headache reporting in response to hypoxic challenge, yet explained little variance when combined with age and body mass index. The sensory and cognitive performance models did not converge, suggesting high intra-individual variability. Together, sex, age, and body mass index were not the most robust predictors in responses to hypoxic challenge; we cannot infer this for sensory deficits and cognitive performance within an experimentally induced hypoxic environment. The findings have implications for improving hypoxia familiarization training, monitoring sensor development, and emergency response and recovery protocols in case of a hypoxia occurrence suitable for all aircrew. We recommend continuing to elucidate the impact of sex and intrapersonal differences in hypoxia and other aeromedically relevant stressors in tactical aviation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Resolving SPARC–HSA binding kinetics with an ultrasensitive photonic sensor based on bound states in the continuum

Secreted protein acidic and rich in cysteine (SPARC) is critical in cell-matrix interactions and tissue remodeling. It influences tumor progression through its affinity for human serum albumin (HSA) - the most abundant plasma protein, which also plays a crucial role in drug delivery. Strong molecular binding leads to a dissociation constant KD in the nanomolar range. Thus, determining KD requires detecting sub-nanomolar concentrations with ultrasensitive methods. This may be crucial for elucidating the nature of SPARC-HSA binding, as their interaction remains a subject of debate. Capturing these interactions accurately requires a platform capable of resolving rapid binding kinetics at extremely low analyte concentrations. In this work, we report on a microfluidics-integrated photonic nanostructure that supports bound states in the continuum (BICs) and is optimized for studying the fast kinetics of high-affinity protein-protein interactions. The unprecedented capability of detecting sub-nanomolar concentrations allows quantifying KD between SPARC and HSA beyond the state of the art. We leverage an all-dielectric photonic crystal slab (PhCS) sustaining two BIC branches arising from gapped Dirac cone dispersion. HSA is covalently immobilized on the PhCS bonded to a PDMS microfluidic chamber. SPARC dissociation is carried out using PBS buffer (pH 7.4), ensuring complete protein release through precise control of the flow rate and continuous spectral monitoring of the BICs. The measured KD=8.2±0.8 nM confirms the strong affinity of SPARC for HSA. This study highlights the potential of BIC-based sensing as a versatile tool for investigating protein interactions. These results also have implications for the optimization of drug delivery systems and cancer treatment strategies.

Albumin↗