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Design, Instrumentation, and Data Analysis for the SLS Core Stage Green Run Test Series

The Space Launch System (SLS) Core Stage (CS) Thrust Vector Control (TVC) system is comprised of eight mechanical feedback Shuttle heritage Type III TVC actuators and four RS-25 engines, each attached to a Shuttle heritage gimbal block/bearing. The actuators are powered by a Shuttle-derived hydraulic Core Auxiliary Power Unit (CAPU), and integrated with an all-new Core Stage thrust structure. The actuators are interfaced to the SLS Vehicle Management (VM) software via an all-new TVC Actuator Control (TAC) avionics subsystem. Despite the significant test and flight experience of the Shuttle hardware, the SLS Green Run ambient and hot fire test activities revealed a number of new findings associated with the dynamic response of the TVC integrated system. Test responses suggested that the TVC system did not meet its performance specifications and its step and frequency responses exhibited unexpected departures from prior lab tests and modeled behavior. This paper is the fifth installment in a seven-paper series surveying the design, engineering, test validation, and flight performance of the Core Stage Thrust Vector Control system. In this paper, the design of the TVC analyses conducted during the Core Stage Green Run test series are discussed in detail. Throughout the course of the test activities, the SLS flight control team worked diligently with the Core Stage contractor to revise test command profiles and ensure sufficient instrumentation was available to collect data. Post-test analysis combined the Green Run modal, ambient, and hot fire test data, MSFC 2- axis Core Stage TVC Inertial Load Simulator (ILS) data, Hardware-In-the-Loop (HWIL) Systems Integration Lab (SIL) results, and actuator Acceptance Testing Procedure (ATP) responses. These data were used to characterize the response, validate critical math models of the TVC subsystem, and isolate the probable cause of the unexpected responses. Through comprehensive analysis of the available test data sources, the integrated team identified the dominant contributors to the observed response and developed test-correlated rationale for vehicle flight control system performance, ultimately leading to a confident posture for the Artemis I mission.

John H. Wall↗
NASA NTRS2022

Juno Gravity Science: Five Years of Radio Science Operations with Ka-band Uplink

Since entering orbit on July 4, 2016, the Juno spacecraft has executed 34 closest approach passes of Jupiter, completing the prime mission. During each closest approach, called perijove, the spacecraft comes within 4,000 km of the cloud tops and the motion of the spacecraft becomes perturbed by the gravitational field of Jupiter. These small changes in the motion of the spacecraft are detected using the Juno Gravity Science Instrument by measuring the Doppler shift of the radio link between the Juno spacecraft and NASA’s Deep Space Network (DSN). During a majority of these closest approach passes, the 34-meter DSS-25 antenna transmits simultaneous X-band and Ka-band uplink to the spacecraft. Juno’s onboard X-band transponder and Ka-band translator phase-coherently return the signals back to Earth for reception at the same DSS-25 antenna. The precise frequency of these signals is measured by processing open-loop recordings of the signal. These measurements, characterized by ~5-10 micron/sec accuracies (after calibration of charged particle noise and Earth troposphere), have probed the gravity field of Jupiter to unprecedented precision, allowing for discoveries of Jupiter’s core size and depth of the zonal winds. Successful operations of the instrument during perijoves requires careful planning and coordination between DSN engineers, the Juno project, and the Juno science team. This work discusses the operations of the Juno Gravity Science Instrument after five years of prime mission operations. Lessons learned are documented to be applied to future missions and the Juno extended mission. Although the Juno extended mission formally started on August 1, 2021, on June 7, 2021, the trajectory was modified with a flyby of Ganymede, the third Galilean moon of Jupiter. Gravity and radio science investigations of Jupiter and its moons will continue to play a key role in Juno’s objectives during the extended mission.

Oudrhiri, Kamal↗
NASA NTRS1995

Dense gas in high-latitude molecular clouds

The nearby molecular clouds MBM 7, 12, 30, 32, 40, 41, and 55 were surveyed for tracers of dense gas, including the (1-0), (2-1), and (3-2) rotational lines of CS and the (1-0) lines of HCO(+) and HCN. MBM 7 and MBM 12 contain dense cores, while the other clouds contain little or no traces of dense gas. Comparison of the emission from dense gas tracers to that of (13)CO reveals that the former are more compact in angular size as well as line width. An extensive CS(2-1) survey of part of MBM 12 reveals that the emission is characterized by clumps on approximately 3 min scales as well as extended emission. Observations of the CS(1-0) and (3-2) lines using telescopes with matched beam sizes reveal that the volume density must be at least approximately 10(exp 4.5)/cc within the (3-2) emitting regions, which are approximately 0.03 pc in radius. Electron excitation of the CS rotational levels is ruled out (in the cores) by comparing the (3-2)/(1-0) line ratios with models including H2 and electron collisions. The volume density in the cores is substantially larger than in the portions of the cloud traced by CO emission. The density increases into the cores as r(exp -2), suggesting dynamical collapse. The masses of the cores are close to the virial mass, suggesting they are dynamically bound. The cores in MBM 7 and MBM 12 are thus likely to form stars; they are the nearest sites of star formation.

Reach, William T.↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2021

In-operando surface-sensitive probing of electrochemical reactions on nanoparticle electrocatalysts: Spectroscopic characterization of reaction intermediates and elementary steps of oxygen reduction reaction on Pt

In this paper we present an in-operando spectroscopic study of the reaction intermediates involved in ORR on electrified Pt nanoparticle electrocatalysts. To accomplish this, we have synthesized Ag-Pt core–shell nanoparticles, that contain a thin shell of Pt (~1 nm) uniformly deposited on a larger Ag core (~60 nm). We show that these nanostructures are ideal platforms for in-operando Plasmon Enhanced Raman Spectroscopy (PERS) of ORR on Pt. We combined these PERS studies with DFT calculations to assign the measured vibrational spectra to relevant surface intermediates as a function of electrocatalyst potential. Overall, these vibrational assignments were further verified and validated by performing PERS measurements using de-oxygenated and heavy oxygen (O 2 -18) saturated electrolyte. These studies allowed us to identity the presence of critical reaction intermediates (OH, O 2 , OOH, and H 2 O) as the function of operating voltage on the Pt surface and derive voltage-dependent elementary step mechanisms of the reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Community Input on the Need, Scope, and Development Roadmap of a Proposed National Virtual Biosecurity for Bioenergy Crops Center (NVBBCC)

Brookhaven National Laboratory (BNL) was awarded a pilot project in FY22 under the U.S. Department of Energy (DOE) Office of Science Biopreparedness Research Virtual Environment (BRaVE) initiative, to define research priorities, needs, and requirements for a national virtual center devoted to the biosecurity of bioenergy crops. The mission of the proposed center, referred to as the National Virtual Biosecurity for Bioenergy Crop Center (NVBBCC), would be to provide the scientific basis and tools to detect, characterize, model, and mitigate biothreats to bioenergy crops. This function will be essential to ensure the projected increased US reliance over the next few decades on key plant-based energy products, such as biojet fuel. The NVBBCC is envisioned as a distributed, virtual center with multiple national laboratories at its core to maximize the use of existing unique facilities and expertise across the DOE complex. A major goal of the pilot project was to develop a roadmap for establishing NVBBCC through a series of meetings to gather community input. A total of about 150 individuals, drawn from DOE laboratories, the USDA, academia, NIH, DHS and the private sector participated in six planning meetings held in FY23. Four of the meetings were focused on specific research topics (disease detection, dispersion and disease propagation, biomolecular characterization of plant-pathogen interaction, and mitigation strategies). These four meetings were followed by a meeting that focused on computational needs to support collaborative, data-intensive research within a distributed center as well as workforce development. A final meeting focused on establishing and maintaining preparedness within NVBBCC to respond to an emerging disease within bioenergy crops and how it would collaborate and coordinate with USDA and DHS.

09 BIOMASS FUELS↗
DOE OSTI2026

Adsorption-based direct air capture using hierarchical porous composites prepared via confined-space crystallization

Capturing CO₂ at trace concentration remains a critical challenge in sustainable carbon management via adsorption, as conventional adsorbents suffer from low CO₂ selectivity, poor moisture tolerance, and energy-intensive regeneration requirements. Here, we report a hierarchical Ba²⁺-exchanged silicoaluminophosphate (Ba²⁺-CSAPO-34) composite synthesized via confined-space crystallization within an activated carbon matrix. Comprehensive characterization revealed a confined nucleation mechanism and the successful incorporation of Ba²⁺ active sites within the SAPO-34 framework, achieved via a two-step liquid ion-exchange protocol. The core-shell architecture combines the selective CO₂ binding of Ba²⁺-functionalized SAPO-34 with the hydrophobic protection of the carbon shell. Fixed-bed adsorption tests demonstrated strong CO₂ binding (at 500-2500 ppm), no roll-up, and effective suppression of water affinity, while maintaining high selectivity even at 90% relative humidity. A phenomenological adsorption model, validated against dynamic breakthrough data, accurately predicted dynamic adsorption behavior under real-world operating conditions, enabling rational process design for direct air capture (DAC) and closed-loop life support systems. Furthermore, these results establish Ba²⁺-CSAPO-34 as a scalable, moisture-resistant adsorbent that addresses key limitations in trace CO₂ capture, advancing practical implementation of carbon removal technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗
DOE OSTI2025

Emergent topological polarization textures in relaxor ferroelectrics

Relaxor ferroelectrics underpin high-performance actuators and sensors, yet the nature of polar heterogeneities driving their broadband dielectric response remains debated. Using a unified, multimodal structural refinement framework— simultaneously fitting complementary X-ray and neutron total scattering, X-ray absorption spectra, and diffuse scattering—we reconstruct 3D mesoscale polarization maps in the classic relaxor system PbMg 1/3 Nb 2/3 O 3 –PbTiO 3 . We uncover self-organized swirling polarization textures with half-skyrmion (meron) vortices, challenging models of independent polar nanoregions. These textures, characterized by smooth changes in the polarization direction, originate from overlapping volumes in which the projections of locally correlated polarization vectors onto each volume’s long axis share the same sign. Vortex cores correlate strongly with local charge and strain gradients imposed by compositional heterogeneities. In this work, our results suggest that chemical disorder, acting via depolarizing and strain fields, stabilizes topological vortex textures of the polarization field, offering a route for engineering new dielectric and ferroelectric functionalities.

Eremenko, Maksim [National Institute of Standards ↗
DOE OSTI2024

Characterization of the ELM-free negative triangularity edge on DIII-D

Tokamak plasmas with strong negative triangularity (NT) shaping typically exhibit fundamentally different edge behavior than conventional L-mode or H-mode plasmas. On DIII-D, every plasma with sufficiently negative triangularity (δ < δ crit ≃ -0.12) is found to be inherently free of edge localized modes (ELMs), even at injected powers well above the predicted L-H power threshold. It is also possible to access an ELM free state at weaker average triangularities provided that at least one of the two x-points is still sufficiently negative. Access to the ELM-free NT scenario is found to coincide with the closure of the second stability region for infinite-n ballooning modes, suggesting that ballooning stability may play a role in limiting the accessible pressure gradient in NT plasmas. Despite this, NT plasmas are able to support small pedestals and are typically characterized by an enhancement of edge pressure gradients beyond those found in traditional L mode plasmas. Further, the pressure gradient inside of this small pedestal is unusually steep, allowing access to high core performance that is competitive with other ELM-free regimes previously achieved on DIII-D. Since ELM-free operation in NT is linked directly to the magnetic geometry, NT fusion pilot plants are predicted to maintain advantageous edge conditions even in burning plasma regimes, potentially eliminating reactor core-integration issues caused by ELMs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Measurements of plasma parameters in the divertor island of Wendelstein 7-X through line-ratio spectroscopy on helium

We report electron temperature T e and density n e have been measured in the divertor island of Wendelstein 7-X by means of a new thermal He-beam system utilizing line-ratio spectroscopy. This system is equipped with a poloidal arrangement of five gas valves that allows to infer 2D plasma profiles T e ( R,Z ) and n e ( R,Z ) across the magnetic island forming the island divertor. A local maximum in T e ( R,Z ) was measured along the flux surfaces located between the island center and the outer separatrix. In the island center, characterized by closed field lines, a clear local minimum in T e was measured, indicating that no direct heat deposition occurs in this region and that the perpendicular heat flux from the core plasma into this domain is small. Increasing the island size leads to a reduction of the connection length in the scrape-off layer (SOL) and to a shift of the island center towards the divertor target. In this scenario, the T e ( R,Z ) are reduced in the entire island domain. Density profiles are found flat across the divertor island for both island size scenarios. These findings are supported by similar observations made with a reciprocating probe plunging through the same SOL island at the mid-plane of W7-X representing the upstream position in this 3D divertor geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Diagnosis of PV Cell Passivation Degradation Resulting from Hot-Humid, High Voltage Potential Aging

Corrosion of the antireflective coating on the cell ("AR c corrosion") was previously observed in studies using hot-humid test conditions with external high voltage (HV) bias. Because AR c corrosion is not well understood, mini-modules (MiMos) were examined in a comparative experiment using PERC and PERT as well as legacy Al-BSF cells. For separate MiMos with the cell circuit electrical at +1500 V, -1500 V, or unbiased "V oc", test conditions in the comparative study included 60degrees C/60% RH for 96 h, as in IEC TS 62804-1; 70degrees C/70%RH for 200 h; and 85degrees C/85% RH for 200 h. Characterizations at each read point included: camera and electroluminescence (EL) imaging, colorimetry, and I-V curve tracing. Characterizations at the final read point included: SunsVoc; spatially mapping external quantum efficiency (EQE); high resolution: photoluminescence (PL), EL, and dark lock-in thermographic (DLIT) imaging. Forensics were performed on extracted cores, including scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS) and scanning Auger microscopy (SAM). Forensics were also conducted on MiMos (stepped HV aging) and full-sized modules (outdoor aging) from previous studies. AR c corrosion was specifically observed for the glass/encapsulant/cell side of +1500 V (HV+) stressed MiMos, where appearance, color, and reflectance were the characteristics most distinguished relative to simultaneously occurring degradation modes. SEM/EDS and SAM identified conversion of silicon nitride to silicon oxide or hydrous silica, preferentially occurring at the edges and tips of the pyramidal textured cell surface.

aging↗
DOE OSTI2025

Impact of the nuclear equation of state on the formation of twin stars

Twin stars-two stable neutron stars (NSs) with the same mass but different radii have long been proposed to appear as a consequence of a possible first-order phase transition in NS matter. Within a meta-model for the EOS of hybrid stars, we revisit the viability of twin stars and its dependence on numerous parameters characterizing the EOS of nuclear matter, quark matter, and the phase transition between them. While essentially no experimental constraint exists for the last two, parameters characterizing the EOS of neutron-rich nucleonic matter have been constrained within various ranges by terrestrial experiments and astrophysical observations. Within these ranges, the impact of nuclear EOS and crust-core transition density on the formation of twin stars is studied. Here, it is found that the symmetry energy of neutron-rich nucleonic matter notably influences the formation of twin stars, particularly through its slope L and curvature K sym . Conversely, varying the EOS of symmetric nuclear matter within their currently known uncertainty ranges shows minimal influence on the formation of twin stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Dissemination of blaNDM-5 and mcr-8.1 in carbapenem-resistant Klebsiella pneumoniae and Klebsiella quasipneumoniae in an animal breeding area in Eastern China

Animal farms have become one of the most important reservoirs of carbapenem-resistant Klebsiella spp. (CRK) owing to the wide usage of veterinary antibiotics. “One Health”-studies observing animals, the environment, and humans are necessary to understand the dissemination of CRK in animal breeding areas. Based on the concept of “One-Health,” 263 samples of animal feces, wastewater, well water, and human feces from 60 livestock and poultry farms in Shandong province, China were screened for CRK. Five carbapenem-resistant Klebsiella pneumoniae (CRKP) and three carbapenem-resistant Klebsiella quasipneumoniae (CRKQ) strains were isolated from animal feces, human feces, and well water. The eight strains were characterized by antimicrobial susceptibility testing, plasmid conjugation assays, whole-genome sequencing, and bioinformatics analysis. All strains carried the carbapenemase-encoding gene bla NDM-5 , which was flanked by the same core genetic structure (IS 5 - bla NDM-5 - ble MBL - trpF - dsbD -IS 26 -IS Kox3 ) and was located on highly related conjugative IncX3 plasmids. The colistin resistance gene mcr-8.1 was carried by three CRKP and located on self-transmissible IncFII(K)/IncFIA(HI1) and IncFII(pKP91)/IncFIA(HI1) plasmids. The genetic context of mcr-8.1 consisted of IS 903 - orf - mcr-8.1-copR-baeS-dgkA - orf -IS 903 in three strains. Single nucleotide polymorphism (SNP) analysis confirmed the clonal spread of CRKP carrying- bla NDM-5 and mcr-8.1 between two human workers in the same chicken farm. Additionally, the SNP analysis showed clonal expansion of CRKP and CRKQ strains from well water in different farms, and the clonal CRKP was clonally related to isolates from animal farms and a wastewater treatment plant collected in other studies in the same province. These findings suggest that CRKP and CRKQ are capable of disseminating via horizontal gene transfer and clonal expansion and may pose a significant threat to public health unless preventative measures are taken.

Yang, Chengxia↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
NASA NTRS1984

CPD -48 deg 1577 - The brightest known cataclysmic variable

The discovery is reported of a V = 9.4 mag star which has the properties of a cataclysmic variable. Its UBV colors are similar to those of UX UMa, and its brightness fickers with an amplitude of the order of 0.1 mag. Larger brightness variations (0.5 mag) are found on a time scale of years. Its spectrum is characterized by extremely weak absorption lines of He I and He II, as well as extremely broad, shallow lines of H. A broad K line is very weakly present in absorption. Emission cores with a very slow decrement are seen in the H lines. From a comparison with the known properties of other cataclysmic variables, the distance, reddening, and mass exchange rate are estimated. The space density of cataclysmic variables is also estimated, since the original spectroscopic survey is complete to magnitude B = 10.0.

Krzeminski, W.↗
NASA NTRS2016

Venus Interior Probe Using In-Situ Power and Propulsion (VIP-INSPR)

Venus, despite being our closest neighboring planet, is under-explored due to its hostile and extreme environment, with a 92 bar pressure and 467 C temperature at the surface. The temperature decreases at higher altitudes, almost at the rate of 7.9 C/km, reaching the Earth surface conditions at 65 km. Due to the less extreme conditions, balloon missions could survive as long as 46 h at an altitude of 54 km. However, because of the opacity of the Venus atmosphere filled with clouds of sulfuric acid and CO2, orbiter or balloon missions are not as revealing and informative in characterizing the surface, as similar missions on Moon and Mars. To understand the evolutionary paths of Venus in relation to Earth, it is imperative to gather basic information on the crust, mantle, core, atmosphere/exosphere and bulk composition of Venus, through in-situ investigations using landers, probes and variable altitude areal platforms.

atmosphere↗