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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Aliovalent gallium dopants remove Ti 3+ defects and improve photocatalytic and photoelectrochemical water oxidation properties of LaTiO 2 N

LaTiO 2 N is a promising semiconductor for the water splitting reaction due to its 2.1 eV band gap and stability against corrosion. However, its solar energy conversion is limited by Ti 3+ recombination defects introduced during ammonolysis. Here we show for the first time that Ti 3+ defects in LaTiO 2 N can be suppressed with incorporation of 2, 5, and 10% aliovalent gallium (Ga 3+ ) dopants during synthesis via the layered La 2 Ti 2 O 7 intermediate. Electron paramagnetic resonance (EPR) spectroscopy on the solid powders confirms a reduction in the Ti 3+ donor density from 2.97 × 10 17 cm −3 for the non-doped material to ∼6.24 × 10 16 cm −3 for 5% Ga-doped LaTiO 2 N. The remaining Ti 3+ defects are concentrated near the LaTiO 2 N surface, according to X-ray photoelectron spectroscopy. The defect reduction shifts the optical absorption edge from 2.02 to 2.09 eV and eliminates a broad absorption band at 1050 nm from the optical absorption spectra. It also removes a 1.0–1.7 eV sub-band gap photovoltage signal from surface photovoltage spectra. This suggests that empty Ti 3+ d-orbitals are located 1.0–1.7 eV above the LaTiO 2 N valence band edge. Removing these recombination states with increasing Ga 3+ content enhances the photoconversion efficiency of LaTiO 2 N during water oxidation. The optimized 2 wt% CoO x -loaded 5% Ga-doped LaTi O2 N material has a 16% AQE (400 nm) for O 2 production from aqueous silver nitrate solution and a ∼2.1 mA cm −2 water oxidation photocurrent at 1.23 V under 100 mW cm −2 Xe arc lamp illumination. The water oxidation photocurrent is stable during a 50 min test, and the Faraday efficiency for O 2 generation is 97%, confirming short-term corrosion stability of LaTiO 2 N. Altogether, these results provide a better understanding of the distribution, concentration, and impact of Ti 3+ defects on the optical, photovoltage, and photoelectrochemical properties of LaTiO 2 N. In combination with other defect control strategies, aliovalent Ga 3+ doping can help bring the solar energy conversion efficiency of LaTiO 2 N closer to the theoretical limit.

Wang, Li [University of California, Davis, CA (Uni↗
NASA NTRS2005

Chapter 12 - Flight Envelope

The term "flight envelope" is used to refer to the boundaries of aircraft loading and flight conditions within which operation of the aircraft is satisfactory, and beyond which some aspect becomes unacceptable. This flight envelope represents, in fact, the limiting conditions arising from a matrix of inter-related flight envelopes covering the appropriate variables. Thus, for each loading (i.e., external stores configuration and its associated range of weight and center of gravity (c.g.) position) and aircraft configuration (i.e., position of undercarriage (u/c), flaps, slats, etc.), the envelopes of airspeed versus altitude, airspeed versus load factor, angle of attack versus angle of sideslip, etc., must be investigated to establish the limits within which all aspects such as handling qualities, engine behavior, structural loads, etc., remain acceptable. Flight testing of new or derivative aircraft models is carried out with the initial purpose of defining a flight envelope which is, first and foremost, safe and secondarily, which enables the effective use of the vehicle for its intended purpose. Flight testing occurs only after numerous reviews of the design and review of results from ground tests and predictions of flight characteristics in such areas as structures, aerodynamics, stability and control, flight controls (particularly fly-by-wire control systems, propulsion, etc.). Accordingly, opening and expanding the envelope is a task that must be approached cautiously, systematically, and with coordination and cooperation of the many disciplines involved in the design and test of an airplane. (Sections 8 and 10 cover test planning and safety of flight considerations, respectively). The fundamental tenet in establishing a flight envelope via flight test is risk reduction. This is reflected in the typical sequence of events leading to initial flight test - design reviews (both hardware and software), then ground test involving singular disciplines (windtunnel tests for aerodynamics, structurally loading the wing/fuselage/nacelle on a ground test article with loads anticipated to occur in flight, flight control system control law checkout, propulsion test cell runs and/or flying test bed tests, etc.), and then ground tests involving multi-disciplines (See Section 9). Only after these have been accomplished will an initial, limited, low-risk, flight envelope be established. The limited envelope will typically be in the middle of the projected final flight envelope. Subsequent flight tests will then be devoted to expanding the initial envelope by operating the airplane at increasing ranges - representing increasing risk - of engine operation, airspeeds both fast and slow, altitude, load factor both above and below 1g, centers of gravity (fore and aft), and with system/subsystem failures. Whether flight tests are to define a flight envelope on a new model airplane with the attendant new airframe, new engine(s), and new subsystems (hydraulics, pressurization, etc.), or on an airplane involving only a few of these areas such as new engines in an old airframe, the fundamental approach to establishing an envelope is the same.

H Walgemoed↗
DOE OSTI2026

Impact of anion-exchange membrane degradation on long-term CO 2 electrolysis to ethylene

Electrochemical CO 2 reduction (eCO2R) to ethylene offers a unique opportunity to diversify domestic supply chains for chemical manufacturing. Large-scale deployment of eCO2R to ethylene reactors is currently limited by low energy efficiencies and poor durability. Herein, we demonstrate >100 h of continuous electrolysis at an industrially relevant current density of 200 mA cm −2 in a 25 cm 2 geometric area zero-gap reactor with a full-cell voltage <3.1 V and ethylene Faradaic efficiency >30%. Incorporating expanded polytetrafluoroethylene (PTFE)-supported electrodes into zero-gap reactors allows for durable electrode catalysts that are resistant to flooding, and this enables wider ranges of operating parameters not typically accessible in CO 2 electrolysis. Finally, we identify membrane degradation, evidenced by a loss of membrane ion-exchange capacity, as the leading cause of performance loss over 144 h of electrolysis.

Energy - Conversion↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
NASA NTRS2005

Chapter 19B - Antenna Radiation Patterns

Modern civil and military aircraft are equipped with a variety of communication devices, radio navigation equipment, and air traffic control systems. For all of these devices appropriate antennas must be available to transmit and receive the signals. As a result, as many as 30 antennas, and sometimes even more, are mounted around today's aircraft. For this reason, it is necessary to know the capabilities of the receiving/transmitting equipment on board. These capabilities are driven by the antenna characteristics. Therefore, coverage, shading, and beam pointing pattern data are necessary for optimum electronic coverage. The aircraft antenna has to convert the available power density of the electromagnetic field to an electric voltage at its connector. This voltage level must be sufficiently high to operate the connected equipment. The reciprocity principle states that it makes no difference if the antenna is receiving or transmitting. For the certification of the aircraft antennas it must be proven that the antennas are at least generating the minimum receiver input voltage which is required for each radio service. The prime condition is, of course, that the specified field power densities of the different radio navigation and communication services are available. An important property of a radio frequency link is the electro-magnetic field intensity at every point in space for a given output power of the antenna. As the propagation of radio frequency waves in free space is well known, the spatial distribution of the field intensity needs only be measured at one spatial sphere around the antenna. The information is usually given as distributions along the circumference of flat sections through this sphere: each of these is called an ARP. Several ARPs are usually required to describe the complete spatial antenna pattern of an antenna. In addition to mathematical modeling, measurements on sub-scale models, and static measurements on full size models or aircraft on the ground, dynamic measurements on aircraft in flight play the most important role in the aircraft antenna testing and certification. [19B-1] The shape of an ARP, for one given frequency, is determined by the shape of the antenna and the shape and material of the surface it is mounted on. As the directly transmitted waves interfere with waves reflected by the aircraft skin with its complex geometry, and the surface material parameters are only roughly known, it is not possible to predict the ARP with the required accuracy. In ground measurements the earth's surface also acts as a reflector thereby causing the ground ARP to be different from the in-flight ARP. As the in-flight ARP is the ARP we are actually interested in it becomes clear that it is necessary to conduct in-flight ARP measurements. The Flight Test Engineer must be aware of the needs of the specialists who are establishing the test program for measuring antenna patterns and radar cross sections. These tests will require special test equipment and dedicated flights to obtain the data that they require. This Section provides an introduction to the principles of determining antenna patterns and the flight techniques for determining both antenna patterns and radar cross section. Reference 19B-1 provides detailed information.

Helmut Bothe↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
NASA NTRS2005

Introduction to Flight Test Engineering [Introduction Aux Techniques Des Essais En Vol]

Flight test is at the core of what organizations must do in order to validate the operation and systems on an aircraft. While the AGARDograph series 300 and 160 series deal with aspects of this testing, this volume pulls it all together as an introduction to the process required to do effective flight test engineering. This volume was originally published in 1995. Its utility has been proven in that many flight test organizations and universities have requested copies for their engineers and students. It was felt that re-issuing it in a new format designed for electronic publication would be valuable to the community. This second printing changes none of the text, but rather reformats it. All the original references to AGARD (instead of RTO) are left in place so that none of the flavor of the original publication is lost. This is the Introductory Volume to the Flight Test Techniques Series. It is a general introduction to the various activities and aspects of Flight Test Engineering that must be considered when planning, conducting, and reporting a flight test program. Its main intent is to provide a broad overview to the novice engineer or to other people who have a need to interface with specialists within the flight test community. The first two Sections provide some insight into the question of why flight test and give a short history of flight test engineering. Sections 3 through 10 deal with the preparation for flight testing. They provide guidance on the preliminary factors that must be considered; the composition of the test team; the logistic support requirements; the instrumentation and data processing requirements; the flight test plan; the associated preliminary ground tests; and last, but by no means least, discuss safety aspects. Sections 11 through 27 describe the various types of flight tests that are usually conducted during the development and certification of a new or modified aircraft type. Each Section offers a brief introduction to the topic under consideration, and the nature and the objectives of the tests to be conducted. It lists the test instrumentation (and, where appropriate, other test equipment and facilities) required, describes the test maneuvers to be executed, and indicates the way in which the test data is selected, analyzed, and presented. The various activities that should take place between test flights are presented next. Items that are covered are: who to debrief; what type of reports to send where: types of data analysis required for next flight; review of test data to make a comparison to predicted data and some courses of action if there is not good agreement; and comments on selecting the next test flight. The activities that must take place upon completion of the test program are presented. The types of reports and briefings that should take place and a discussion of some of the uses of the flight test data are covered. A brief forecast is presented of where present trends may be leading.

Test facilities↗
NASA NTRSDate not supplied

Investigating Laser Beam Welding as an In-Space Joining Technique via Thermal Vacuum and Microgravity and Vacuum Experiments

In-space joining technologies are crucial for stimulating an in-space economy and for enabling sustained space exploration by in-space manufacturing and repair of metallic structures. Compared to brazing or soldering, in-space welding (ISW) can provide highly hermetic, strong, and complex joints, potentially without introducing additional material. However, the influence of extreme temperatures, reduced pressure, and reduced gravity on ISW is not yet fully elucidated. Several efforts at NASA are investigating laser beam welding (LBW) as a joining and repair method in both thermal vacuum (TVAC) and combined vacuum & reduced gravity environments. NASA Marshall Space Flight Center (MSFC) shepherded several ISW projects in its past, including the 1973 electron beam welding on Skylab, the 1989 low-power LBW on parabolic flights, and the unflown 1990s-era In-Space Welding Experiment. Recent parabolic flights and 3 degree-of-freedom ground testing build upon this heritage. A collaboration with the Ohio State University using NASA Langley Research Center (LaRC) hardware retrofitted for LBW achieved the first high-powered laser welds under vacuum and low gravity and developed a workforce capable developing such experimental hardware. A ground testing campaign at the MSFC Flat Floor simulated fit-up and welding representative of ISW in 3 degrees of freedom to emulate microgravity effects on inertial systems. One ongoing effort is a NASA Early Career Initiative project – Lunar Assembly and Servicing by Autonomous Robotics (LASAR). Ruggedized LBW components were developed by an external partner for use in TVAC. A TVAC-rated robotic arm was procured by MSFC and used in the first known robotic laser weld where all components save the laser generator were under vacuum. NASA Johnson Space Center (JSC) is advancing supervised autonomy of ISW. NASA LaRC continues to adapt their unique snowflake joint geometry, suitable for connecting segments in trusses and other structures, to LBW. Upcoming TVAC campaigns will focus on testing extreme temperatures, proving out autonomous operations, and demonstrating weld repair. Weld inspection will occur via a non-contact nondestructive evaluation (NDE) technique – electromagnetic acoustic transduction (EMAT). Another ongoing effort based at MSFC is the DISCMAN -- DIsk-Shaped Configurable and Modular vAcuum uNit – which seeks to development a compact, modular payload that can provide a vacuum environment while in a reduced gravity condition. This payload could support multiple in-space manufacturing developmental efforts, with the first demonstration technology being LBW. Currently, the design is targeting operations in the pressurized volume of a space station, but the payload could readily be adapted to other flight platforms such as parabolic or even suborbital vehicles. LASAR elucidates the effects of temperature and vacuum on LBW while DISCMAN probes those of vacuum and gravity. Through these complementary efforts, NASA is addressing the primary challenges of ISW across the space environment while simultaneously developing and maturing technologies including robotic systems and inspection methodologies for future practical implementation on the Moon and beyond. This approach is timely, as upcoming missions requiring sustained human presence in space will depend on reliable ISW capabilities to create robust metallic joints currently unproven in the space environment and to perform repairs in situ .

hypogravity↗
NASA NTRSDate not supplied

Phosphate Textural Diversity in CI Chondrites and C-Type Asteroids

Introduction: Apatite, Ca 5 (PO 4 ) 3 (CL/F/OH-), is a ubiquitous phosphate found throughout the solar system, including the most primitive solids, CI-chondrites [1,2] and related samples of carbonaceous asteroids Ryugu [3] and Bennu [4], returned by JAXA’s Hayabusa2 and NASA’s OSIRIS-REx missions, respectively. Apatite in these primitive solids is found as individual grains or mineral clusters and has been inferred to form from the hydrothermal sequence during cooling of their respective parent body or bodies. To better understand the formation history and reworking of these early phosphates we have undertaken a highly coordinated study of phosphate microstructures, geochemistry and U/Pb geochronology from a suite of CI meteorites and carbonaceous asteroid samples. These data have identified novel phosphate microstructures and complex relationships across a suite of apatite grains from the early solar system, indicating multiple episodes of growth and modification on the CI parent body(ies). Methods: Samples of CI chondrites, Alais, Ivuna, Orgueil, Oued Chebeika 002, Yamato (Y) 82162, Y980115, Y980134, and two chips of Hayabusa2 Ryugu particles (A0262 and C0263) have been acquired for analysis. The bulk texture of the samples were first scanned by X-ray Computed Tomography (XCT) using the Nikon XT H 320 within the XFACT facility at NASA JSC or the Xradia 620 Versa at the UTCT facility. Based on the identification of petrofabrics or features of interest from the XCT data, samples were chipped, oriented, potted in epoxy and thick sections were prepared. After anhydrously polishing the samples with silicon carbide and dry diamond powder down to 1 µm, the samples were ion polished using a Hitachi ArBlade. Energy dispersive Xray spectrometry (EDS) maps were collected to ID phosphates of interest using a JEOL 7900F SEM. The internal microstructures of identified phosphates were then mapped by electron backscatter diffraction (EBSD) using the JEOL 7900F. Based on the EDS and EBSD data, domains of interest were targeted for quantitative chemical analyses using a JEOL 8530 EPMA. Subsequently, in situ U-Pb and 207 Pb/ 206 Pb ages will be collected using a Cameca ims1290 secondary ion mass spectrometer at UCLA across a range of microstructures. Results: Apatite grains are ubiquitous throughout the CI chondrites and carbonaceous asteroid materials, found as individual grains, disseminated clusters or grain aggregates. Apatite grains are associated with serpentine, magnet-ite, and/or carbonate. Of particular interest, we have identified individual and aggregate polycrystalline grains ex-hibiting an internal ‘honeycomb’ texture (Fig. 1). Some of these grains appear overprinted by subsequent alteration while others remain unaltered. Apatite halogen sites are dominated by the missing component, assumed to be OH, and F, comparable to published values from Bennu, Ryugu and CM-chondrites [3-5]. However, the Yamato CI-like meteorites show a broader range of Cl values, and one grain from Alais is dominated by CL. Summary: Apatite growth features indicate a protracted and complex growth history, consistent with precipitation from an evolving fluid system. The ‘honeycomb’ texture identified in some CI meteorites is, to the best of our knowledge, the first report of such a microstructure in meteoritic phosphate. The microtextures and zoning will guide subsequent age analyses, to better constrain the formation and reworking of phosphate in CI(-like) materials. Acknowledgments: We thank the National Institute of Polar Research, Japan for samples of Y82162, 980115 and 980134 meteorites, ASU’s Buseck Center for Meteorite Studies for samples of Ivuna and Alais meteorites, and JAXA curation for chips of Ryugu material. This work was funded by NASA ROSES LARS grant 24-LARS24-0014. References: [1] Morlok et al., 2016, GCA 70:5371-5394. [2] Alfin g et al., 2019, Geochemistry 79:125532. [3] Nakamura et al. (2022) Science 379:1-15. [4] Seifert et al. (2026) MAPS 61:504-521. [5] Piralla et al. (2021) MAPS 56:809-828.

CI chondrites↗
DOE OSTI2026

Evaluation of 3D pixel silicon sensors for the CMS Phase-2 Inner Tracker

The high-luminosity upgrade of the CERN LHC requires the replacement of the CMS tracking detector to cope with the increased radiation fluence while maintaining its excellent performance. An extensive R&D program, aiming at using 3D pixel silicon sensors in the innermost barrel layer of the detector, has been carried out by CMS in collaboration with the FBK (Trento, Italy) and CNM (Barcelona, Spain) foundries. The sensors will feature a pixel cell size of 25 × 100 µm 2 , with a centrally located electrode connected to the readout chip. The sensors are read out by the RD53A and CROCv1 chips, developed in 65 nm CMOS technology by the RD53 Collaboration, a joint effort between the ATLAS and CMS groups. This paper reports the results achieved in beam test experiments before and after irradiation, up to a fluence of approximately 2 . 6 × 1 0 16 n eq /cm 2 . Measurements of assemblies irradiated to a fluence of 1 × 10 16 n˙eq/cm 2 show a hit detection efficiency higher than 96% at normal incidence, with fewer than 2% of channels masked, across a bias voltage range greater than 50 V . Even after irradiation to a higher fluence of 1.6 × 10 16 n˙eq/cm 2 , similar performance is maintained over a bias voltage range of 30 V , remaining well within CMS requirements.

3D pixel↗
DOE OSTI2026

Identification of potent inhibitors of JUN N-terminal kinases for treatment of endometriosis and associated pain

Endometriosis, defined as the ectopic growth of endometrial tissue outside of the uterine cavity, is an inflammatory and hormone-dependent disease that causes excruciating pelvic pain, infertility, and significantly decreases quality of life in affected patients. The JUN N-terminal kinases (JNKs) are a leading class of nonhormonal therapeutic targets that have been validated in preclinical models of endometriosis and in a Phase 1/2 clinical trial. Despite their therapeutic potential, JNK inhibitors with increased potency and specificity are needed to address the inflammatory pathology of endometriosis and to prevent disease progression. Leveraging a DNA-encoded chemical library collection of ~4 billion compounds, we identified lead inhibitor CDD-2428 and optimized derivatives, CDD-2728 and CDD-3013, with excellent binding affinity to JNK1-3 (K d = 0.12 to 3.7 nM), enhanced selectivity, metabolic stability, and cellular permeability. Crystallographic and biochemical studies confirmed that CDD-3013 exhibited superior kinase selectivity with improved efficacy compared to existing JNK inhibitors. In primary endometriosis cell models, CDD-2728 and CDD-3013 suppressed JNK-dependent inflammatory signaling, dampening pathways linked to pain, invasion, angiogenesis, and macrophage recruitment. In an endometriosis mouse model, both CDD-2728 and CDD-3013 reduced endometriotic lesion size, macrophage infiltration, and cellular proliferation, showing in vivo efficacy. When tested in a lipopolysaccharide-induced hyperalgesia model, CDD-2728 and CDD-3013 decreased markers of induced pain, as measured by changes in a dynamic weight bearing test and Grimace scores. These findings nominate CDD-2728 and CDD-3013 as potent, nonhormonal therapeutic candidates for endometriosis with broad anti-inflammatory and analgesic activity, addressing a critical unmet clinical need.

Madasu, Chandrashekhar [Department of Pathology an↗
DOE OSTI2025

Low-energy calibration of SuperCDMS HVeV cryogenic silicon calorimeters using Compton steps

Cryogenic calorimeters for low-mass dark matter searches have achieved sub-eV energy resolutions, driving advances in both low-energy calibration techniques and our understanding of detector physics. The energy deposition spectrum of gamma rays scattering off target materials exhibits step-like features, known as Compton steps, near the binding energies of atomic electrons. Here, we demonstrate a successful use of Compton steps for sub-keV calibration of cryogenic silicon calorimeters, utilizing four SuperCDMS High-Voltage eV-resolution detectors operated with 0 V bias across the crystal. This new calibration at 0 V is compared with the established high-voltage calibration using optical photons. The comparison indicates that the detector response at 0 V is about 30% weaker than expected, highlighting challenges in detector response modeling for low-mass dark matter searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗