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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗
DOE OSTI2023

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Catalytic Hydrogen Production from Methane: A Review on Recent Progress and Prospect

Natural gas (Methane) is currently the primary source of catalytic hydrogen production, accounting for three quarters of the annual global dedicated hydrogen production (about 70 M tons). Steam–methane reforming (SMR) is the currently used industrial process for hydrogen production. However, the SMR process suffers with insufficient catalytic activity, low long-term stability, and excessive energy input, mostly due to the handling of large amount of CO 2 coproduced. With the demand for anticipated hydrogen production to reach 122.5 M tons in 2024, novel and upgraded catalytic processes are desired for more effective utilization of precious natural resources. In this review, we summarized the major descriptors of catalyst and reaction engineering of the SMR process and compared the SMR process with its derivative technologies, such as dry reforming with CO 2 (DRM), partial oxidation with O 2 , autothermal reforming with H 2 O and O 2 . Finally, we discussed the new progresses of methane conversion: direct decomposition to hydrogen and solid carbon and selective oxidation in mild conditions to hydrogen containing liquid organics (i.e., methanol, formic acid, and acetic acid), which serve as alternative hydrogen carriers. We hope this review will help to achieve a whole picture of catalytic hydrogen production from methane.

08 HYDROGEN↗
DOE OSTI2022

Identifying Redox Orbitals and Defects in Lithium-Ion Cathodes with Compton Scattering and Positron Annihilation Spectroscopies: A Review

Reduction-oxidation (redox) reactions that transfer conduction electrons from the anode to the cathode are the fundamental processes responsible for generating power in Li-ion batteries. Electronic and microstructural features of the cathode material are controlled by the nature of the redox orbitals and how they respond to Li intercalation. Thus, redox orbitals play a key role in performance of the battery and its degradation with cycling. We unravel spectroscopic descriptors that can be used to gain an atomic-scale handle on the redox mechanisms underlying Li-ion batteries. Our focus is on X-ray Compton Scattering and Positron Annihilation spectroscopies and the related computational approaches for the purpose of identifying orbitals involved in electrochemical transformations in the cathode. This review provides insight into the workings of lithium-ion batteries and opens a pathway for rational design of next-generation battery materials.

Nokelainen, Johannes (ORCID:0000000328857850)↗
DOE OSTI2020

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗
DOE OSTI2025

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.

augmentation↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Suppression of Composition g -modes in Chemically Equilibrating Warm Neutron Stars

We investigate the impact of chemical equilibration and the resulting bulk viscosity on nonradial oscillation modes of warm neutron stars at temperatures up to T ≈ 5 MeV, relevant for protoneutron stars and neutron star postmerger remnants. In this regime, the relaxation rate of weak interactions becomes comparable to the characteristic frequencies of composition g-modes in the core, resulting in resonant damping. To capture this effect, we introduce the dynamical sound speed, a complex, frequency-dependent generalization of the adiabatic sound speed that encodes both the restoring force and the dissipative effects of bulk compression. Using realistic weak reaction rates and three representative equations of state, we compute the complex frequencies of composition g-modes with finite-temperature profiles. We find that bulk viscous damping becomes increasingly significant with temperature and can completely suppress composition g-modes. In contrast, the f-mode remains largely unaffected by bulk viscosity due to its nearly divergence-free character. Our results highlight the sensitivity of g-mode behavior to thermal structure, weak reaction rates, and the equation of state, and establish the dynamical sound speed as a valuable descriptor characterizing oscillation properties in dissipative neutron star matter.

Neutron stars↗
DOE OSTI2021

AIMD simulations of water over transition metal surfaces

AIMD simulations of water molecules over (111) surfaces of Ag, Au, Cu, Pt, Pd, and Rh, (211) and (100) surfaces of Ag, Au, Cu, and Pt, and (0001) surface of Ru. Data used for analysis in "OH binding energy as a universal descriptor of the potential of zero charge on transition metal surfaces," which can be found on ChemRxiv with DOI 10.26434/chemrxiv-2021-dkb6l.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1971

Geological features in Wyoming from Nimbus 1

The identification of ground features in Nimbus photography by comparisons with geology, soils, and vegetation maps is discussed for the Wyoming-Colorado area. It is concluded that in a semiarid region, high topographic relief shows good coassociation with exposed geology and with vegetation and, to a lesser degree, with soils types. In lower, flatter basins, most tonal patterns are related to subtle variations in soil color and vegetation cover. It is thought that maps based on spectral parameters as the proper descriptor of surface features may be required.

Short, N. M.↗
NASA NTRS1972

Indexing NASA programs for technology transfer methods development and feasibility

This project was undertaken to evaluate the application of a previously developed indexing methodology to ongoing NASA programs. These programs are comprehended by the NASA Program Approval Documents (PADS). Each PAD contains a technical plan for the area it covers. It was proposed that these could be used to generate an index to the complete NASA program. To test this hypothesis two PADS were selected by the NASA Technology Utilization Office for trial indexing. Twenty-five individuals indexed the two PADS using NASA Thesaurus terms. The results demonstrated the feasibility of indexing ongoing NASA programs using PADS as the source of information. The same indexing methodology could be applied to other documents containing a brief description of the technical plan. Results of this project showed that over 85% of the concepts in the technology should be covered by the indexing. Also over 85% of the descriptors chosen would be accurate. This completeness and accuracy for the indexing is considered satisfactory for application in technology transfer.

Clingman, W. H.↗
NASA NTRS1971

Elevation-relief ratio, hypsometric integral, and geomorphic area-altitude analysis.

Mathematical proof establishes identity of hypsometric integral and elevation-relief ratio, two quantitative topographic descriptors developed independently of one another for entirely different purposes. Operationally, values of both measures are in excellent agreement for arbitrarily bounded topographic samples, as well as for low-order fluvial watersheds. By using a point-sampling technique rather than planimetry, elevation-relief ratio (defined as mean elevation minus minimum elevation divided by relief) is calculated manually in about a third of the time required for the hypsometric integral.

Pike, R. J.↗
NASA NTRS1973

Combined environmental stresses

Tolerance levels, physiological effects, and performance degradation during simultaneous or sequential exposures to two environmental stresses, and also three or more simultaneous stresses are described. Environmental stress combinations are characterized by four descriptors: order of occurrence, duration of exposure, severity of exposure, and type of interaction. Combined stress data and facilities for combined stress study are briefly mentioned.

Murray, R. H.↗
NASA NTRS1973

Skylab 2: Photographic index and scene identification

A quick reference guide to the photographic imagery obtained on Skylab 2 is presented. Place names and descriptors used give sufficient information to identify frames for discussion purposes and are not intended to be used for ground nadir or geographic coverage purposes. The photographs are further identified with respect to the type of camera used in taking the pictures.

Underwood, R. W.↗
NASA NTRS1974

Summary of recent investigations of inlet flow distortion effect on engine stability

A review is presented of recent experimental results, analytical procedures and test techniques employed to evaluate the effects of inlet flow distortion on the stability characteristics of representative afterburning turbofan and turbojet compression systems. Circumferential distortions of pressure and temperature, separately and in combination are considered. Resulting engine sensitivity measurements are compared with predictions based on simplified parallel compressor models and with several distortion descriptor parameters.

Graber, E. J., Jr.↗
NASA NTRS1974

Study of stability of large maneuvers of airplanes

A predictive method of nonlinear system analysis is used to investigate airplane stability and dynamic response during rolling maneuvers. The maneuver roll-rate is not assumed to be constant, and the airplane motion is represented by a set of coupled nonlinear differential equations. The general rolling maneuver is kinematically specified by its roll-rate variation p(t). A method for relating the airplane dynamic response to p(t) is developed. The method provides analytical expressions for the motion variables in terms of the maneuver descriptor p(t). A parameterized family of rolling maneuvers is considered, for which the method is used to predict specific dynamic response information, such as the dependence of the peak angle-of-attack excursion on the maneuver parameters. The stability and motion of the airplane in response to an arbitrary actuation of aileron input is considered. Analytical expressions relating motion variables to aileron input are obtained. Explicit analytical bounds on the motion variables are derived. A stability criterion which guarantees nondivergence of motion in response to aileron actuation is presented.

Haddad, E. K.↗