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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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NASA NTRS1998

Stability and Phase Noise Tests of Two Cryo-Cooled Sapphire Oscillators

A cryocooled Compensated Sapphire Oscillator (CSO), developed for the Cassini Ka-band Radio Science experiment, and operating in the 8K - 10K temperature range was previously demonstrated to show ultra-high stability of sigma(sub y) = 2.5 x 10 (exp -15) for measuring times 200 seconds less than or equal to tau less than or equal to 600 seconds using a hydrogen maser as reference. We present here test results for a second unit which allows CSO short-term stability and phase noise to be measured for the first time. Also included are design details of a new RF receiver and an intercomparison with the first CSO unit. Cryogenic oscillators operating below about 10K offer the highest possible short term stability of any frequency sources. However, their use has so far been restricted to research environments due to the limited operating periods associated with liquid helium consumption. The cryocooled CSO is being built in support of the Cassini Ka-band Radio Science experiment and is designed to operate continuously for periods of a year or more. Performance targets are a stability of 3-4 x 10 (exp -15) (1 second less than or equal to tau less than or equal to 100 seconds) and phase noise of -73dB/Hz @ 1Hz measured at 34 GHz. Installation in 5 stations of NASA's deep space network (DSN) is planned in the years 2000 - 2002. In the previous tests, actual stability of the CSO for measuring times tau less than or equal to 200 seconds could not be directly measured, being masked by short-term fluctuations of the H-maser reference. Excellent short-term performance, however, could be inferred by the success of an application of the CSO as local oscillator (L.O.) to the JPL LITS passive atomic standard, where medium-term stability showed no degradation due to L.O. instabilities at a level of (sigma)y = 3 x 10 (exp -14)/square root of tau. A second CSO has now been constructed, and all cryogenic aspects have been verified, including a resonator turn-over temperature of 7.907 K, and Q of 7.4 x 10 (exp 8). These values compare to a turn-over of 8.821 K and Q of 1.0 x 10 (exp 9) for the first resonator. Operation of this second unit provides a capability to directly verify for the first time the short-term (1 second less than or equal to tau less than or equal to 200 seconds) stability and the phase noise of the CSO units. The RF receiver used in earlier tests was sufficient to meet Cassini requirements for tau greater than or equal to 10 seconds but had short-term stability limited to 2-4 x 10 (exp -14) at tau = 1 second, a value 10 times too high to meet our requirements. A new low-noise receiver has been designed to provide approximately equal to 10-15 performance at 1 second, and one receiver is now operational, demonstrating again short-term CSO performance with H maser-limited stability. Short-term performance was degraded in the old receiver due to insufficient tuning bandwidth in a 100MHZ quartz VCO that was frequency-locked to the cryogenic sapphire resonator. The new receivers are designed for sufficient bandwidth, loop gain and low noise to achieve the required performance.

Dick, G. John↗

NASA NTRS1977

Strapdown gyro test program

The power spectral noise characteristic performance of the Teledyne two-degree-of-freedom dry tuned gimbal gyroscope was determined. Tests were conducted using a current configuration SDG-5 gyro in conjunction with test equipment with minor modification. Long term bias stability tests were conducted as well as some first difference performance tests. The gyro, test equipment, and the tests performed are described. Results are presented.

Irvine, R. B.↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Organic Electrochemical Transistor Channel Materials: Copolymerization Versus Physical Mixing of Glycolated and Alkoxylated Polymers

Organic electrochemical transistors (OECTs) feature a polymer channel capable of conducting both ions and electronic charges. The choice of the channel material is critical for OECT performance. Many efforts have focused on improving performance via the chemical tunability of conjugated polymers – through backbone, side chain, and molar mass engineering – leading to useful design principles for accumulation-mode OECT materials. However, tuning the chemical structure of conjugated polymers often requires time-consuming optimization of the synthesis route. Meanwhile, variations in molar mass, dispersity, structural defects, and metal content present challenges when attempting to analyze the detailed effects of structural modifications, as multiple performance-determining factors are often (unintentionally) changed at the same time. Therefore, this study explores blended channel materials obtained by physically mixing glycolated and alkoxylated polymers in different ratios, and compares their OECT performance with the corresponding statistical copolymers. It is shown that mixing two well-performing materials creates blends that enable rational tuning of the transistor properties without compromising on performance. Thus, channels based on blends of alkoxylated and glycolated polymers hold promise for OECT technology with tailored response, as only two materials are needed to achieve any desired side chain ratio, simplifying the optimization of OECT characteristics.

copolymerization↗

DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗

DOE OSTI2026

Tuning the Coordination Environment of Rh Single Atoms on Highly Dispersed Reducible Oxides for Enhanced Reverse Water-Gas Shift Performance

Controlling the dynamic mobility of catalyst surface active sites and their interactions with the surrounding environment is critical in generating active surfaces that directly influence the catalytic activity and selectivity. Here, we report a strategy for tailoring the dispersion and electronic environment of single atom Rh catalysts by decorating the alumina support with highly dispersed (HD) cerium and molybdenum oxides. The resulting catalysts exhibit markedly different behavior in the Reverse Water Gas Shift (RWGS) reaction. In particular, Rh/MoOx(HD)/Al2O3 maintains atomically dispersed Rh even at elevated temperatures (up to 400 °C), achieving CO selectivity of up to 100% and resists sintering via the formation of a newly developed structure featuring Rh single atoms embedded in MoOx clusters. In situ spectroscopy and microscopy analyses confirm the stabilization of Rh and the dynamic evolution of Rh–Mo coordination under reaction conditions. Our findings highlight the power of support modification in steering active site structure and activity, offering a pathway toward enhanced and tunable single atom catalysts for CO2 valorization.

CO selectivity↗

DOE OSTI2026

Near-adjacent heavy lanthanide separation and sensing using dimerizing lanmodulins

Explore the source record for details and available documents.

Biological and medical sciences↗

DOE OSTI2025

Developing low-cost rechargeable batteries: beyond traditional layered oxide cathodes for Li-ion and beyond Li-ion batteries

Here, the rising demand for energy storage systems, driven by the rapid adoption of electric vehicles and the global shift toward renewable energy, necessitates continuous efforts to lower the cost of current lithium-ion batteries (LIBs) and enhance the sustainability of existing battery chemistries. This feature article examines the key challenges associated with Ni- and Co-containing LIB cathodes and compares advancements in cathode development for non-traditional Li-ion and beyond Li-ion chemistries. First, a review of earth-abundant element containing disordered rock-salt cathodes is presented, with a discussion of key strategies such as compositional tuning and carbon coating to improve their electrochemical performance. Hurdles in developing oxide-based cathodes for Na- and K-ion batteries are also highlighted, followed by an in-depth overview of polyanion and Prussian blue cathodes for Na- and K-ion systems. Overall, this article provides a systematic perspective on the design of earth-abundant, low-cost, and sustainable cathode materials for both LIB and beyond LIB technologies.

Lohani, Harshita [Lawrence Berkeley National Labor↗

DOE OSTI2025

Predicting the von Neumann entanglement entropy using a graph neural network

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations such as classical mutual information (MI). We propose a machine learning approach using a graph neural network to predict the von Neumann entropy directly from experimentally accessible bitstrings. We test this approach on a Rydberg ladder system and achieve a mean absolute error of $3.6\,\times 10^{-3}$ when evaluating within the training range on a dataset with entropy values ranging from 0 to 1.9. The model achieves a mean absolute percentage error of 1.44% and outperforms MI-based bounds. When tested beyond the training range, the model maintains reasonable accuracy. Furthermore, we demonstrate that fine-tuning the model with small datasets significantly improves performance on data outside the original training range.

graph neural networks↗

DOE OSTI2024

Experimental Characterization Test of a Grid-Forming Inverter for Microgrid Applications

Standardized experimental testing protocols for grid forming (GFM) inverters to ensure expected operation under both normal and contingency conditions do not exist. Such protocols increase the confidence of system owner/operators that an inverter deployed in a proposed system will engage in typical behaviors to ensure interoperability with other units and ancillary equipment (e.g. protection equipment). This paper presents systematic and comprehensive test protocols to evaluate the performance of GFM inverters under the following operational configurations: islanded operation, heterogeneous islanded operation (parallel with a synchronous generator), grid-connected operation, and transition operation. A commercial GFM inverter is used to verify the test protocols and to understand the inverter's performance and functionalities. In particular, required configuration and tuning of the inverter will be explained in the full paper to enrich the testing protocol.

black start↗

DOE OSTI2024

Segmentation Model Distillation [Poster]

The process of training object detection (OD) or image segmentation model requires both a substantial amount of data and technical knowledge, which often creates challenges in applying these types of models to their full potential. In order to streamline the process of developing these models, we propose a new pipeline where a foundation model assists in the dataset generation. Then this resulting dataset is used to fine-tune a fast light-weight model to perform the custom segmentation or OD. This resulting model is also fit for real-time image segmentation, such as in a video stream.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗

NASA NTRS1981

Parallelism maps for optically contacted etalons

A measurement technique is described that is capable of producing accurate numerical maps of the parallelism defect of optically contacted Fabry-Perot etalons. A photoelectric raster scan of the etalon transmission intensity distribution is performed at each of three closely spaced etalon tunings, one at maximum on-axis and two at the half-maxima on-axis positions. Simple data manipulation gives numerical or contour maps of the parallelism defect. Results demonstrating the effect of mechanical constraints on etalon parallelism are presented

Killeen, T. L.↗

NASA NTRS1993

A radar altitude and line of sight attachment

This paper describes a method of overcoming much of the computational expense of finding radar altitude and determining lines of sight in flight simulations over databases built from polygons. Methods are described for quantizing polygonal databases and for searching through them quickly. Various tuning parameters are explained and run-time performance figures are offered.

Sansom, Russell↗

NASA NTRS1995

Atmospheric thermal lensing in laser resonators

Atmospheric absorption degrades laser performance both by absorbing laser energy within the laser resonator, which increases the loss, and by inducing a thermal lens in the atmosphere. Atmospheric thermal lensing can be quite severe, even when the absorption coefficient is quite modest. A model is developed which describes atmospheric thermal lensing; time constants, which are associated with the establishment and decay of the atmospheric thermal lens, are determined; experiments are performed using an injection seeded Ti:Al2O3 laser tuned to the H2O absorption lines near 0.815 microns to validate the model; dependence of the atmospheric thermal lens on the laser energy and absorption coefficient were measured and found to agree with the model. In addition, the decay of the atmospheric thermal lens with time was measured and also found to agree with the model predictions.

Barnes, Norman P.↗

NASA NTRS1997

Orthorectified High Resolution Multispectral Imagery for Application to Change Detection and Analysis

The project team has outlined several technical objectives which will allow the companies to improve on their current capabilities. These include modifications to the imaging system, enabling it to operate more cost effectively and with greater ease of use, automation of the post-processing software to mosaic and orthorectify the image scenes collected, and the addition of radiometric calibration to greatly aid in the ability to perform accurate change detection. Business objectives include fine tuning of the market plan plus specification of future product requirements, expansion of sales activities (including identification of necessary additional resources required to meet stated revenue objectives), development of a product distribution plan, and implementation of a world wide sales effort.

Benkelman, Cody A.↗

NASA NTRS1998

Charon Message-Passing Toolkit for Scientific Computations

The Charon toolkit for piecemeal development of high-efficiency parallel programs for scientific computing is described. The portable toolkit, callable from C and Fortran, provides flexible domain decompositions and high-level distributed constructs for easy translation of serial legacy code or design to distributed environments. Gradual tuning can subsequently be applied to obtain high performance, possibly by using explicit message passing. Charon also features general structured communications that support stencil-based computations with complex recurrences. Through the separation of partitioning and distribution, the toolkit can also be used for blocking of uni-processor code, and for debugging of parallel algorithms on serial machines. An elaborate review of recent parallelization aids is presented to highlight the need for a toolkit like Charon. Some performance results of parallelizing the NAS Parallel Benchmark SP program using Charon are given, showing good scalability.

VanderWijngaart, Rob F.↗

NASA NTRS1998

Charon Message-Passing Toolkit for Scientific Computations

VanderWijngarrt, Rob F.↗