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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Anion-exchange membrane water electrolysis: insights from round-robin testing

As research and industrial interest in anion-exchange membrane water electrolysis (AEMWE) grows, there is an increasing need for reliable baselines and cross-lab validation of results. The wide variety of material sets and operating conditions under consideration for AEMWE has thus far limited efforts for standardization. In this study, round-robin testing was conducted in deionized water and KOH-based supporting electrolyte by 5 institutions from academia, national laboratories, and industry to provide baseline performance data and identify sources of cross-lab variability. Baseline membrane electrode assemblies were fabricated with commercial catalysts, membranes, and transport layers using standard techniques and tested using reagent-grade electrolytes, aiming for accessibility rather than state-of-the-art performance. From all tests, the average voltage at 1 A/cm 2 was 2.72 ± 0.17 V and 1.87 ± 0.03 V in deionized water and 0.1 M KOH, respectively. The maximum in-house and cross-lab variations at this current density were 118 mV and 476 mV in water and 60 and 88 mV in 0.1 M KOH. The KOH purity, station contamination, and temperature control were identified as possible factors affecting performance between labs, with in-house specific variation attributed to sample-to-sample differences in fabrication, cell assembly, and station contamination. This work provides a commercial baseline for the field and highlights the need for improved standardization and reproducibility in AEMWE research.

08 HYDROGEN↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗
DOE OSTI2026

Production and performance of a 172 Hf/ 172 Lu generator

A 172 Hf/ 172 Lu radionuclide generator system to produce 172 Lu for laboratory scale applications in lutetium-based radiochemistry development was established and evaluated. The parent 172 Hf radionuclide was produced through 35.2 MeV proton irradiation of natural lutetium metal foil at the Brookhaven Linac Isotope Producer. Four resins were investigated for 172 Hf separation from bulk Lu target material: LN resin, ZR resin, in-house synthesized hydroxamate, and methyl-substituted hydroxamate resins, all with comparable performance. Separated 172 Hf was consolidated and used to create a ZR resin-based 4.9(3) MBq 172 Hf/ 172 Lu generator which was eluted 49 times over two years with no observed breakthrough of 172 Hf, and an average elution efficiency of 98(1)%. The eluted 172 Lu was used to radiolabel the macrocyclic chelator DOTA with an apparent molar activity of 8(2)x10 2 kBq/nmol.

172Hf/172Lu radionuclide generator↗
DOE OSTI2026

Rolling Root Mean Square Based Multimodal Anomaly Detection for Real Time Monitoring of Smart Grid

Reliable real-time monitoring is valuable for maintaining the operational integrity of modern electrical smart grids. Deployment of heterogeneous sensing technologies in substations has enabled high-resolution, multichannel waveform monitoring, but also introduces challenges for anomaly detection due to noise, baseline drift, and modality-dependent signal characteristics. In this work, we present a computationally efficient unsupervised method for multimodal event detection based on Rolling Root Mean Square based Event Detection (RRMSED). The method is developed using in-house, field deployed sensors collecting data at a utility substation. The sensing system comprises voltage and current sensors, triaxial accelerometers, and magnetometers, collectively capturing electrical, vibrational, and magnetic waveform measurements at high temporal resolution. RRMSED operates by extracting rolling RMS energy features and their first-order temporal differences from consecutive waveform segments for each channel and then applying channel-specific statistical thresholds learned from historical data. A persistence-based exceedance logic is employed to robustly identify transient events while suppressing impulsive noise, and to provide precise temporal localization with high resolution. The framework is designed for continuous server-side operation and can be deployed in real time without requiring complex models. Experiments on simulated waveform data with known ground truth demonstrate low false positive (FP) and false negative (FN) rates. Application to real substation data shows RRMSED to identify events that are not captured by conventional monitoring indicators including fast transient detection algorithm currently deployed in the system. These results indicate that rolling RMS based features provide an effective and practical basis for real-time multimodal event detection in smart-grid substations.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2026

PyJMAK: An Open-Source Python Toolkit for Modeling Solid-State Metallurgical Phase Transformations

Accurate prediction of metallurgical phase transformations is an essential basis for autonomous optimization and rapid part qualification. Several methods can be used to estimate the evolution of phase fractions such as JMAK kinetics-based models, phase-field models, thermodynamic models, and data-driven machine learning models. Thermodynamic and phase-field-based methodologies solve multiphysics equations requiring numerous calibration parameters and significant computational resources. As a result, the computation domain is limited to a point or on order of micron-meters. The data-driven models rely on large datasets from experiments and simulations. While the JMAK model only provides information about phase fraction evolution, it can predict this evolution in near real-time using thermal history and thermodynamic data without restriction on the domain. JMAK models have been popularly used by researchers to model phase transformations occuring during additive manufacturing or over arbitrary temperature profiles. Commercial proprietary software such as Abaqus and Ansys or closed-source in-house implementations offer the ability to model JMAK based kinetics to predict phase transformation. However, these software packages are not open-source or freely available for use and development in conjunction with manufacturing machines, sensors, and machine learning algorithms. In addition, the use of the model is restricted by a license token. In contrast, given temperature profiles at multiple points in the domain, this Python-based PyJMAK model can compute phase evolution in parallel due to its stand-alone modular, voxel-based structure, and it can be executed on high-performance computing resources without any license restrictions.

Prabhune, Bhagya [Oak Ridge National Laboratory (O↗
DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗
DOE OSTI2026

Optimization of boron carbide density using direct current sintering for use as sputtering sources at the National Ignition Facility

Several experimental trials were conducted to determine optimal sintering parameters and compositions for purchased boron carbide powder using spark plasma sintering/direct current sintering. Sputtering targets used to create ablators for the National Ignition Facility have been unable to survive post processing procedures suggesting that in-house fabrication may yield better results due to extended control.

36 MATERIALS SCIENCE↗
DOE OSTI2026

BIL High Speed Fuel Cell Stack Manufacturing

General Motors LLC (GM) was awarded a project to develop and implement technologies for manufacturing 20,000 units of Fuel Cell Stacks per year on two shifts per day basis. GM leveraged prior in-house expertise in designing the Fuel Cells, deploying the manufacturing process steps in the laboratory environment as well as the deployment in the industrial environment on a smaller scale. The project focus was to design, build, and deploy a manufacturing line consisting of an anode and cathode electrode processing, unitized electrode assembly, fuel cell stacking, compression, testing, and final assembly of the fuel cell stack. The project was terminated in the first budget period.

08 HYDROGEN↗
DOE OSTI2026

Scalable Solar Fuels Production in A Reactor Train System by Thermochemical Redox Cycling of Novel Nonstoichiometric Perovskites

Hydrogen production via two-step thermochemical water splitting redox cycles using nonstoichiometric redox-active metal oxides has the potential to dramatically increase fuel production rates. At moderate-to-low water splitting temperatures, surface reaction kinetics co-limit the process. In such cases, stable and high surface area microstructures that allow exploitation of the full thermodynamic potential of the materials are essential as is tight thermal integration of the reactor module. This project’s goals were the development of novel nonstoichiometric perovskite oxides with high stability and favorable thermodynamic and kinetic properties, to optimize their microstructure for maximizing the fuel productivity, and to build a prototype reactor train system (RTS) comprising at least one reactor to meet specific performance targets: (1) capable of an in-house solar thermochemical hydrogen (STCH) productivity ≥ 12 mL g -1 for stable continuous operation ≥ 20 cycles; and (2) demonstration of scalable solar fuels production at practical solar reactor level in an industrial-scale concentrated solar tower (CST) using developed perovskites to achieve a hydrogen production rate ≥ 1 g h -1 .

08 HYDROGEN↗
DOE OSTI2026

Grid Operator Analytics and Assessment Tools for Inverter- Based Resources Dominated Grid (GOAAT-IBR) Project Update

This presentation provides an update on the OPTIMA GOAAT project, with emphasis on the cloud-native data platform developed in-house to ingest, manage, and operationalize high-resolution power system data. Since our last NASPI presentation, accessible via OSTI ID #2671437, the project team advanced the design and deployment of a scalable architecture capable of handling both synchronized and non-synchronized streams, including PMU, point-on-wave (POW), COMTRADE, and SCADA data. These materials review the project status, recent progress, and key lessons learned. The core of the presentation examines the architecture and engineering of our cloud-native ingestion and data management platform. We then explain how pipelines were designed to collect, normalize, time-align, store, and serve heterogeneous data at scale. We will discuss design choices such as data models, streaming versus batch ingestion, storage tiers, and interoperability with analytics applications. Practical experiences with cloud-native technologies were shared during the event, including benefits, limitations, and integration challenges in a utility environment, along with methods used to improve performance, reduce latency, and optimize resource usage. The presentation also showcases user interface designs and visualization tools that convert raw measurements and analytics results into intuitive, actionable insights for operators and engineers. During the presentation examples were provided demonstrating how visualization, event views, and summarized analytics enhance situational awareness and support operational decision-making. These use cases illustrate how a well-designed data infrastructure can bridge the gap between high-volume measurements and practical grid operations.

Aminifar, Farrokh↗
DOE OSTI2026

A new capability for investigating the structure and dynamics of liquids at high pressures

Water and aqueous solutions are critical to many areas of science and technology. As a result, tremendous effort has been devoted to understanding them in detail. Despite over a century of research, fundamental questions about water and aqueous solutions remain unanswered. However, an intriguing possibility – that liquid water can exist in two thermodynamically distinct states – has emerged as the most likely explanation. The problem is that experimental confirmation of this hypothesis requires experiments on water at high pressures and low temperatures, conditions in which liquid water only exists briefly before turning into crystalline ice. This project investigated the feasibility of developing a new capability for study water and aqueous solutions under these challenging conditions. The physical constraints, such as the timescales for crystallization and thermal diffusion in supercooled water, were evaluated along with their impact on the design criteria for the instrument. Several basic design options were considered that could meet the technical requirements. The options were also evaluated with respect to their use of commercially available equipment versus the need for custom designs or in-house development. The project identified two viable options to pursue. The first option would use a high-pressure syringe pump in conjunction with fused silica (or sapphire) capillaries. The second option would use a diamond anvil cell. These options can both be used with optical spectroscopies, such as Raman or Infrared.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Characterization of Digital Silicon Photomultipliers

Digital Silicon photomultipliers (Digital SiPMs) have become an increasingly viable option for single-photon detection due to their compact size and precise timing and resolution. The following work characterizes the noise performance of a diSiPM designed and tested in-house at Fermilab. The SiPM consists of a 26x32 array of single-photon avalanche diode (SPAD) cells. Through the implementation of a shift register masking circuit, individual SPAD cells can be enabled and disabled at will, allowing for precise detection measurements of single columns (accumulator channels) and cells. The provided circuit design operates on voltage clock pulses and collects data on clock edges allowing for precise tuning of the data collection process by varying the clock frequency, pulse width, and integration period.

Shoemaker, Oliver [U. Chicago (main)]↗
NASA NTRS1999

International Ultraviolet Explorer (IUE) Battery History and Performance

The "International Ultraviolet Explorer (IUE) Battery History and Performance" report provides the information on the cell/battery design, battery performance during the thirty eight (38) solar eclipse seasons and the end-of-life test data. It is noteworthy that IUE spacecraft was an in-house project and that the batteries were designed, fabricated and tested (Qualification and Acceptance) at the Goddard Space Flight Center. A detailed information is given on the cell and battery design criteria and the designs, on the Qualification and the Acceptance tests, and on the cell life cycling tests. The environmental, thermal, and vibration tests were performed on the batteries at the battery level as well as with the interface on the spacecraft. The telemetry data were acquired, analyzed, and trended for various parameters over the mission life. Rigorous and diligent battery management programs were developed and implemented from time to time to extend the mission life over eighteen plus years. Prior to the termination of spacecraft operation, special tests were conducted to check the battery switching operation, battery residual capacity, third electrode performance and battery impedance.

Rao, Gopalskrishna M.↗
NASA NTRS2026

NASA Talks: Space Shuttle Columbia - Lessons Learned: Columbia Launch and Recovery

On February 25, 2026, this NASA Talk was held regarding the Space Shuttle Columbia (Columbia Launch & Recovery). Crew & Thermal System Division (EC) partnered with Kennedy Space Center (KSC) and Engineering Directorate (EA) to bring this talk to Johnson Space Center (JSC) centering on what led up to and transpired after the Space Shuttle Columbia accident. Some recovered debris was on exhibit during the talk. Retired NASA Columbia Vehicle Manager Scott Thurston presented this talk. Mr. Thurston brings a wealth of knowledge from his mission experience. This talk was timely and a solemn reminder of the critical importance of diligence, safety, and excellence in our work. EA Director Julie Kramer White welcomed the 400+ center-wide in-person audience, and EC Division Chief Rubik Sheth introduced the speakers. This event was held in the JSC Teague Auditorium, recorded, and executed by the EC in-house STAR Productions team. This record includes an mp4 video presentation with a run time of 36 min. 20 sec.; in color; with sound.

NASA Talks↗
NASA NTRS2026

NASA Talks: Space Shuttle Columbia - Lessons Learned

On February 25, 2026, two NASA Talks were held regarding the Space Shuttle Columbia (Columbia Launch & Recovery, Columbia Reconstruction, Investigation, Causes & Key Takeaways). Crew & Thermal System Division (EC) partnered with Kennedy Space Center (KSC) and Engineering Directorate (EA) to bring these talks to Johnson Space Center (JSC) centering on what led up to and transpired after the Space Shuttle Columbia accident. Some recovered debris was on exhibit during the talks. NASA Mishap Program Specialist David Erickson and retired NASA Columbia Vehicle Manager Scott Thurston presented these talks. Mr. Erickson supports NASA mishap investigations and assists with the development of NASA's new Columbia Learning Center at KSC. Mr. Thurston brought a wealth of knowledge from his mission experience. These talks were timely and a solemn reminder of the critical importance of diligence, safety, and excellence in our work. EA Director Julie Kramer White welcomed the 400+ center-wide in-person audience, and EC Division Chief Rubik Sheth introduced the speakers. This event was held in the JSC Teague Auditorium, recorded, and executed by the EC in-house STAR Productions team.

Space Shuttle Columbia↗