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Draft ASME Code Case to qualify L-PBF 316H material for Section III, Division 5 applications
This report documents the AMMT program’s development and submission of a draft ASME Code Case to qualify Laser Powder Bed Fusion (L PBF) Type 316H stainless steel for Section III, Divi-sion 5 Class A and SM high temperature nuclear applications. It summarizes the technical basis, the comprehensive high temperature mechanical test database assembled between 2023–2026, and the proposed code language and qualification framework submitted to ASME. The work was co-ordinated across multiple national laboratories and leverages prior ASME efforts to integrate additive manufacturing into the Boiler & Pressure Vessel Code. The body of the report describes the experimental database and analysis supporting the Code Case: tensile, creep, fatigue, creep fatigue, and thermal aging tests collected from multiple additive manufacturing sites, machine types, and powder lots, with material processed by a solution anneal heat treatment. The dataset — including both full size and subsized specimens and tests oriented parallel and perpendicular to build direction — shows limited tensile anisotropy, tensile properties comparable to wrought 316H, creep strength within the scatter of wrought material, but markedly reduced creep ductility above about 650 °C associated with rapid σ phase formation in L PBF microstructures. The draft Code Case itself prescribes a staged qualification model (manufacturing process qualification, component qualification, and per build witness testing), treats L PBF components as equivalent to Type 316 weld metal for design and inspection, and requires mechanical, chemical, and metallographic controls tied to ASTM/ISO 52946. Key acceptance criteria include tensile tests within a 90% prediction interval of the AMMT dataset, a creep fatigue screening test adapted from ASME Section III, Division 5, Subsection HB, HBB 2800 but with the cycle acceptance reduced to 100 for L PBF material, and double volumetric inspection of production components. The report concludes that the present data support treating L PBF 316H as analogous to conventional fusion weld metal for Division 5 design and inspection, while highlighting important caveats: the σ phase driven loss of creep ductility above ~650 °C, preliminary indications of enhanced creep fatigue sensitivity in some lots, and remaining gaps in long term aging and additional cyclic testing. Recommended next actions include completing outstanding cyclic and long duration creep/aging tests on the solution annealed condition, supporting inclusion of the 316H chemistry and heat treatment in ASTM/ISO 52946, and continuing engagement with ASME and NRC during balloting and review to enable industry adoption.
Sustainable Triacetic Acid Lactone Production from Sugarcane by Fermentation and Crystallization
Triacetic acid lactone (TAL) has the potential to serve as a bioderived platform chemical for commercial products including sorbic acid and recyclable polydiketoenamine plastics. In this study, we leveraged BioSTEAM to design, simulate, and evaluate (via techno-economic analysis, TEA, and life cycle assessment, LCA) TAL production from sugarcane. We experimentally characterized TAL solubility, calibrated solubility models, and designed a process to separate TAL from fermentation broths by crystallization. The biorefinery could produce TAL at a minimum product selling price (MPSP) of $\$3.73$–5.86·kg –1 (5th–95th percentiles; baseline at $\$4.60$·kg –1 ) and a carbon intensity (CI) of 5.31 [2.60–8.71] kg CO 2 -eq·kg –1 , which could enable financially viable, low-CI production of sorbic acid and polydiketoenamines. To drive down costs and CI, we explored the theoretical fermentation space (titer, yield, productivity combinations), operation scheduling and capacity expansion strategies (e.g., integrated sorghum processing), and potential separation improvements (mitigating TAL loss through pH control). Advancements in key design and technological parameters could further reduce MPSP by 51% to $\$2.26$·kg –1 [$\$1.97$–2.80·kg –1 ] and CI by 43% to 3.05 [1.91–4.15] kg CO 2 -eq·kg –1 . This research highlights the ability of agile TEA-LCA to screen promising designs, navigate sustainability trade-offs, prioritize research needs, and chart quantitative roadmaps to advance bioproducts and biofuels.
Fuel injector design for high temperature aircraft engine
The objective of the Innovative High Temperature Aircraft Engine Fuel Nozzle Program was to design and evaluate a nozzle capable of operating at a combustor inlet air temperature of 1600 F (1144 K) and a fuel temperature of 350 F (450 K). The nozzle was designed to meet the same performance requirements and fit within the size envelope of a current production dual orifice fuel nozzle. The design approach was to use improved thermal protection and fuel passage geometry in combination with fuel passage surface treatment to minimize coking at these extreme fuel and air temperatures. Heat transfer models of several fuel injector concepts were used to optimize the thermal protection, while sample tube coking tests were run to evaluate the effect of surface finish, coatings and tube material on the coking rate. Based on heat transfer analysis, additional air gaps, reduced fuel passage flow area and use of ceramic tip components reduced local fuel wetted wall temperatures by more than 200 F (100 K) when compared to a current production fuel nozzle. Sample tube coking test results showed the importance of surface finish on the fuel coking rate. Therefore, a one micro-inch (0.025 micron) roughness was specified for all fuel passage surfaces. A novel flow divider valve in the tip was also employed to reduce weight, allow room for additional thermal protection, and provide back pressure to reduce the risk of fuel vaporization. The fuel nozzle was fabricated and evaluated in a series of high temperature coking tests. Initial results of these tests indicate that thermal protection and surface treatments were partially successful in preventing nozzle fouling, but additional refinement of the internal flowpath is needed to prevent buildup of coke particles that partially blocked the spin slots which meter the nozzle fuel flow.
Preliminary Evaluation of Joint Electricity-Hydrogen Concept of Operations
An initial thermal power dispatch (TPD) concept of operations was evaluated that couples a nuclear power plant to a nearby hydrogen production plant. GSE Systems’ generic pressurized water reactor full-scope simulator was modified with a TPD model comprised of a thermal power extraction and delivery system. A prototype human-system interface (HSI) was developed to interact with the TPD model and allow participants to execute the basic operating scenarios for normal operations. Four retired operators performed the evaluation, and due to COVID-19 travel restrictions, the original in-person experimental design was restructured to support a remote participator evaluation using a web meeting platform. Data from operator feedback, observations from the research team, and quantitative survey responses revealed that the initial TPD concept of operations is feasible. The operators were comfortable with the engineered system and HSI and could manage it without adverse impacts to reactor power, plant safety, or equipment. Findings are discussed in terms of both the TPD system design and HSI performance.
Improving Marine Energy Production Through Commercialization of a Low Cost, Drag-Reducing Slippery Coating (CRADA 679 Abstract)
Marine energy capture systems offer great promise for providing clean energy, but they operate in a challenging and dynamic environment and must be optimized to the highest extent possible. Computational studies predict that drag reduction on marine energy and blue economy systems will result in meaningful improvements in energy efficiency. Based on extensive coating experience, PNNL is proposing to bring to market a new drag-reducing coating called Superhydrophobic Lubricant-Infused Drag-Efficient Coating – SLIDE-Coat. PNNL has a deep knowledge base regarding this class of slippery coatings, and we have a group of enthusiastic industry partners who have committed to partnering, conducting field testing, and providing well over 50% cost share. In addition to validating the technology, the team will create a commercialization roadmap to ensure the commercial success of the technology after the government-sponsored two-year program is complete. The main technical goal is to design, manufacture, and experimentally validate a new coating that can reduce hydrodynamic skin friction drag by 20%. The main commercialization goal is to develop a roadmap that identifies activities needed to complete SLIDE-Coat’s development after the conclusion of this project. Year 1 will focus on adaptation and modification of the existing SLIC coating system to optimize drag reduction and assessment and quantification of drag reduction on relevant materials in a laboratory setting. Year 2 will focus on optimizing and demonstrating drag reduction with preferred coatings. The manufacturability, ease of application, adhesion to relevant surfaces, and consistency will be assessed. Quantification of drag reduction on prototype materials in a relevant marine field setting will be executed.
Autonomous Radiation Cartographer (ARC) System Training Manual
This training manual is designed to provide end users with a comprehensive knowledge base for the safe and effective use of the Autonomous Radiation Cartographer (ARC) System. The ARC is a fully autonomous radiation detection robot based on the Spot Robot platform manufactured by Boston Dynamics.
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
Pressure Safety Training (Rev. 8)
This is the workbook for the Pressure Safety Training Course. It is intended as a reference manual and guide for all work with pressurized liquids or gases. This workbook contains basic references to make work with pressure safer. It is intended to supplement classroom instruction, rather than serve as a definitive text on pressure. Earlier versions of the manual were intended specifically for training at Lawrence Livermore National Laboratory. This revision is a generic version intended for training at all DOE facilities. The information in the Standards chapter is from the LLNL Health and Safety Manual. It is included here as a convenient reference and guide for developing similar standards at your own facility.
Microgravity Game Changers: Recent Accomplishments of Microgravity Assisted Materials Experiments on the ISS
This paper discusses important new materials, devices and technology produced in low-Earth orbit (LEO) that have been developed with funding from NASA’s In Space Production Applications (InSPA) Portfolio. In coordination with the International Space Station (ISS) National Laboratory, InSPA supports innovative small businesses and other research & development organizations to develop microgravity-assisted applications that make significant impacts in a wide variety of industries on Earth. These technology development efforts are using the microgravity of orbital space to overcome gravity-induced defects on Earth to create high-value products for terrestrial markets in the areas of semiconductors, pharmaceuticals, and communications. Through 24/7/365 operation on ISS for 25 years, coupled with commercial space transportation and services to, in, and from orbit, the microgravity environment has enabled pioneering science and significant commercial applications, including novel medical advances. World-leading researchers, innovators, and space payload developers provide compelling evidence through demonstrations on the ISS that materials processing in microgravity improves outcomes for a wide range of materials and products at all levels of the organization of matter, from quantum entities such as Bose-Einstein condensates, to crystals, alloys, composites, thin films, and photonics, to stem cells, medical diagnoses, medical treatments, and medical devices for humans. As of 2026, numerous examples proved by demonstration on ISS show the potential of these technologies in various fields. In this paper, a summary of four important experiments is presented.
Solar Training and Education Partnership for Underserved Populations
Solar Landscape’s STEP-UP program provided high quality solar installation training in partnership with community-based organizations (CBOs) in various regions within the U.S. Solar Landscape leveraged internal subject matter experts (SME’s) industry guidance and regional training assessments to provide customized training designed to support the growing solar and broader energy sector. Upon completion of the program, the team assisted trainees and nonprofit partners with connections to Solar Landscape contractors as well as local and National residential solar installation companies to facilitate placement into careers and apprenticeships.
Space Power
Appropriate directions for the applied research and technology programs that will develop space power systems for U.S. future space missions beyond 1995 are explored. Spacecraft power supplies; space stations, space power reactors, solar arrays, thermoelectric generators, energy storage, and communication satellites are among the topics discussed.
Rapid Assessment of Sulfate Resistance in Mortar and Concrete
Extensive research has been conducted on the sulfate attack of concrete structures; however, the need to adopt the use of more sustainable materials is driving a need for a quicker test method to assess sulfate resistance. This work presents accelerated methods that can reduce the time required for assessing the sulfate resistance of mixtures by 70%. Class F fly ash has historically been used in concrete mixtures to improve sulfate resistance. However, environmental considerations and the evolving energy industry have decreased its availability, requiring the identification of economically viable and environmentally friendly alternatives to fly ash. Another challenge in addressing sulfate attack durability issues in concrete is that the standard sulfate attack test (ASTM C1012) is time-consuming and designed for only standard mortars (not concrete mixtures). To expedite the testing process, accelerated testing methods for both mortar and concrete mixtures were adopted from previous work to further the development of the accelerated tests and to assess the feasibility of testing the sulfate resistance of mortar and concrete mixtures rapidly. This study also established criteria for interpreting sulfate resistance for each of the test methods used in this work. A total of 14 mortar mixtures and four concrete mixtures using two types of Portland cement (Type I and Type I/II) and various supplementary cementitious materials (SCMs) were evaluated in this study. The accelerated testing methods significantly reduced the evaluation time from 12 months to 21 days for mortar mixtures and from 6 months to 56 days for concrete mixtures. The proposed interpretation method for mortar accelerated test results showed acceptable consistency with the ACI 318-19 interpretations for ASTM C1012 results. The interpretation methods proposed for the two concrete sulfate attack tests demonstrated excellent consistency with the ASTM C1012 results from mortar mixtures with the same cementitious materials combinations. Metakaolin was shown to improve sulfate resistance for both mortar and concrete mixtures, while silica fume and natural pozzolan had a limited impact. Using 15% metakaolin in mortar or concrete mixtures with Type I/II cement provided the best sulfate resistance.
Turbofan Engine Power Extraction Demonstration Final Report
As GE Aerospace advances toward a revolutionary step change in propulsion efficiency, the integration and demonstration of new engine architectures and technology systems are essential. The NASA Turbofan Engine Power Extraction Demonstration (PEx), conducted through the Hybrid Thermally Efficient Core (HyTEC) project, aims to develop and demonstrate megawatt-class hybrid electric capability on a modern commercial turbofan engine. The hybrid electric system is critical to meeting the needs of the U.S. aviation industry for next-generational propulsion systems with greater efficiency, durability, and range. This supports energy independence and helps ensure the security and resilience of one of America's largest export industries. The PEx project specifically targets three key objectives: mechanically integrating hybrid electric capability into a commercial turbofan engine, integrating electric machine control with turbofan control for advanced power management, and de-risking performance modeling of future hybrid electric architectures. To mature these technologies to Technology Readiness Level (TRL) 6, a series of electric power system component tests and a baseline engine performance test campaign were conducted. These efforts culminated in an integrated hybrid electric turbofan test campaign demonstrating power extraction, power insertion, and power transfer between spools. Tests of the electric power system were completed at GE Aerospace’s Electrical Power Integrated Systems Center in Dayton, Ohio and engine tests were completed at Peebles Test Operation in Peebles, Ohio. Hybrid electric trade studies extended the demonstrated capability to altitude using the validated cycle model from the PEx test campaigns, allowing for comments on expanded mission benefits not demonstrated in the ground campaign. The knowledge gained from PEx also supports GE Aerospace’s Compact Core Demonstrator as part of HyTEC Phase 2 and ultimately informs the implementation of hybrid electric systems in the next generation of GE Aerospace commercial engine products. This report provides a summary of the program background, test campaigns, trade studies, and insights into the technical maturation required to support future commercial products.
Lunar and Planetary Science XXXV: Icy Worlds: Moving and Grooving
Reports from the conference session entitled Icy Worlds: Moving and Grooving, include:Mass Anomalies on Ganymede; Europan Chaos and Lenticulae: A Synthesis of Size, Spacing, and Areal Density Analyses; Thermal and Topographic Tests of Europa Chaos Formation Models; Flexure of Europa s Lithosphere Due to Ridge-Loading; Ridges on Europa: Origin by Incremental Ice-Wedging ; Convergent Boundaries on Europa: a Numerical Approach to Euler Pole Analysis and Its' Implications for Plate Reconstruction; Numerical Simulations of Subsolidus Convection in the Ice Shell of Europa: Implications for the Thermal Evolution and Present State; Effects of Plasticity on Convection in an Ice Shell: Implications for Europa; Non-Newtonian Convection and Compositional Buoyancy: Advances in Modeling Convection and Dome Formation on Europa; Convective Instability in Ice I: Application to Callisto and Ganymede; Crater Size Distributions on Callisto: A Galileo SSI Summary; Neutron Diffraction Studies of Planetary Ices; and H2O2 Synthesis Induced by Irradiation of H2O with Energetic H+ and Ar+ Ions at Various Temperatures.
Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning
Composite materials are increasingly being used in aerospace applications due to their superior strength-to-weight ratio compared to commonly used metals. A current limitation to widespread adoption is the certification of adhesively bonded joints. One approach to improving adhesive bonding in composites is accurately measuring the thickness of adhesive bondlines in composite laminates. Precise bondline thickness control is essential for aerospace applications where adhesive layer thickness directly affects joint fracture properties and structural performance. This study focused on implementing machine learning techniques to determine the ultrasonic time of flight (directly correlated to thickness) in adhesive bondlines throughout autoclave cure cycles. A high-temperature (use up to 180°C) ultrasonic scanning system was deployed in an autoclave to provide time of flight data through composite panels. Three experiments were conducted on the curing of 305 mm × 305 mm unidirectional composite panels. In the first experiment, a piecewise function was fit for the temperature correction factor to account for changing autoclave temperatures. Due to deficiencies in the first calibration experiment, a second experiment was run, and the results were used to train a machine learning model. The revised experiment, in combination with the machine learning model, significantly increased the accuracy of the bondline time of flight predictions (~14% error reduced to <1%). Data was processed using the Regression Learner Application in MATLAB®, with a Support Vector Machine selected for the model. The result was a machine learning algorithm capable of reliably quantifying ultrasonic time of flight through adhesive bondlines. The third experiment provided independent test data for the machine learning model, demonstrating that the model produces accurate predictions from data beyond its training set.
Mechanical Properties of Carbon Fiber Reinforced Composites Exposed to Cryogenic Conditions and Space Radiation via Simulation and Testing
As NASA missions extend beyond low Earth orbit, increasing reliance is placed on carbon fiber reinforced polymer (CFRP) composites for spacecraft structures where mass efficiency, durability, and long-term reliability are critical. In service, these materials are subjected to a combination of ultraviolet radiation, vacuum, ionizing radiation, atomic oxygen, and extreme thermal excursions under sustained mechanical loading. Flight systems such as the Boeing Starliner and SpaceX Dragon employ external composite structures that will experience these environments for extended durations. Although prior spaceflight and ground studies have reported limited changes in bulk mechanical properties, the synergistic effects of these environments on composite microstructure, particularly at the fiber matrix interphase, remain insufficiently characterized and represent a potential qualification and reliability risk. This study investigates the effects of short-term cryogenic exposure on a radiation shielding carbon epoxy composite, SC2020, as a ground-based analog for space relevant thermal extremes. The SC2020 material system has previously flown on the International Space Station under the Materials International Space Station Experiment (MISSE) program. Composite specimens were exposed to liquid nitrogen for 6 and 24 hours and evaluated using a multiscale characterization framework that combined ASTM D3039 tensile testing, Atomic Force Microscopy (AFM) based interphase analysis, and helium gas permeability measurements. Tensile testing showed no statistically significant or permanent degradation in global strength or modulus following cryogenic exposure. In contrast, AFM measurements revealed reductions in interphase modulus, weakened adhesion, and increased nanoscale heterogeneity, indicating localized degradation mechanisms not captured by conventional bulk testing. Gas permeability measurements showed a progressive increase in helium diffusion with exposure duration, consistent with micro-void formation or partial interfacial debonding. The results indicate that cryogenic exposure initiates degradation at the fiber matrix interphase while leaving global mechanical properties largely unchanged over short durations. These findings underscore the importance of multiscale diagnostics for identifying early-stage damage mechanisms that may influence long term performance and qualification margins for spaceflight composite structures. The data presented establish a cryogenic baseline for comparison with forthcoming MISSE flight exposure results and support ongoing NASA Established Program to Stimulate Competitive Research (EPSCoR) efforts aimed at improving composite qualification methodologies, risk assessment, and reliability prediction for space environments.
32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery
Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.