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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advancing Cross-Sectional Scanning Electron Microscopy of Perovskite Solar Cells

Organic–inorganic perovskites are an emerging class of photovoltaic materials. Despite achieving power conversion efficiencies surpassing 26%, the challenge of perovskite stability including degradation during exposure to operational conditions such as light, heat, humidity, water, oxygen, and electric fields is well known. Related, perovskite instability has limited high-resolution electron imaging and characterization techniques that can be used for understanding degradation mechanisms. Furthermore, we demonstrate perovskite device cross-section preparation using mechanical polishing in a water-free environment with cryogenic Ar ion milling. Scanning electron microscopy was then used in both backscattered electron and secondary electron imaging modes to obtain information about layer structure, grain aggregate structure, and compositional heterogeneity. Monte Carlo CASINO simulations inform optimum beam conditions and image acquisition parameters and the effects of accelerating voltage, dwell times, and frame averaging for practical image acquisition are reported.

14 SOLAR ENERGY↗
DOE OSTI2021

Prediction of Probabilistic Shock Initiation Thresholds of Energetic Materials Through Evolution of Thermal-Mechanical Dissipation and Reactive Heating

The ignition threshold of an energetic material (EM) quantifies the macroscopic conditions for the onset of self-sustaining chemical reactions. The threshold is an important theoretical and practical measure of material attributes that relate to safety and reliability. Historically, the thresholds are measured experimentally. In this work, we present a new Lagrangian computational framework for establishing the probabilistic ignition thresholds of heterogeneous EM out of the evolutions of coupled mechanical-thermal-chemical processes using mesoscale simulations. Furthermore, the simulations explicitly account for microstructural heterogeneities, constituent properties, and interfacial processes and capture processes responsible for the development of material damage and the formation of hotspots in which chemical reactions initiate. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, heat conduction, reactive chemical heating, gaseous product generation, and convective heat transfer. To determine the ignition threshold, the minimum macroscopic loading required to achieve self-sustaining chemical reactions with a rate of reactive heat generation exceeding the rate of heat loss due to conduction and other dissipative mechanisms is determined. Probabilistic quantification of the processes and the thresholds are obtained via the use of statistically equivalent microstructure sample sets (SEMSS). The predictions are in agreement with available experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Additive-Mixing Fuel Injection (AMFI): A device for robust control and improved performance of high-efficiency LTGC engines

Low-temperature gasoline combustion (LTGC) engines can provide high efficiencies with very low NOx and particulate emissions. Despite these major advantages, methods for controlling LTGC combustion timing, achieving robust autoignition, and good low-load performance have challenged the development of practical LTGC engines. A key reason is that the ideal reactivity of the fuel varies with operating conditions, with low-reactivity fuels working well at high loads and higher-reactivity fuels working better at low loads. This article introduces a new technique called Additive Mixing Fuel Injection (AMFI) that adjusts the reactivity of the gasoline to obtain good performance over the operating map. Moreover, AMFI can provide robust combustion-timing control because it precisely meters very small amounts (0.01–0.6 mm 3 ) of an ignition-enhancing additive into the fuel each engine cycle. The additive used was 2-ethylhexyl nitrate (EHN), a common, inexpensive diesel-fuel ignition improver, but other additives could be used. Because additive amounts are so small, a 2-gal reservoir would be sufficient for medium-duty applications with refilling only at service intervals of about 8000 mi. The AMFI system has been installed on a single-cylinder LTGC research engine and demonstrated to provide robust combustion-timing control over wide ranges of combustion phasing, fueling rate, intake boost, engine speed, and intake temperature. AMFI also increases the fuel’s reactivity sufficiently to greatly reduce or eliminate the need for charge heating, simplifying engine design and significantly increasing thermal efficiency and the maximum load at low-boost conditions. The nitrogen in the EHN produced a modest increase in NOx, but it was mitigated by lower intake temperatures, and NOx emissions remained very low. Finally, the additive increases the sensitivity of autoignition reactions to variations in the local fuel/air mixture within the charge. This allows the use of controlled fuel stratification for improved LTGC-engine performance, and its potential to improve low-load operation was demonstrated.

Dec, John E.↗
DOE OSTI2025

Dynamic Modeling, Trajectory Optimization, and Linear Control of Cable-Driven Parallel Robots for Automated Panelized Building Retrofits

The construction industry faces a growing need for automation to reduce costs, improve accuracy and productivity, and address labor shortages. One area that stands to benefit significantly from automation is panelized prefabricated building envelope retrofits, which can improve a building’s energy efficiency in heating and cooling interior spaces. In this paper, we propose using cable-driven parallel robots (CDPRs), which can effectively lift and handle large objects, to install these panels. However, implementing CDPRs presents significant challenges because of their nonlinear dynamics, complex trajectory planning, and precise control requirements. To tackle these challenges, this work focuses on a new application of established control and trajectory optimization theories in a CDPR simulation of a building envelope retrofit under real-world conditions. We first model the dynamics of CDPRs, highlighting the critical role of damping in system behavior. Building on this dynamic model, we formulate a trajectory optimization problem to generate feasible and efficient motion plans for the robot under operational and environmental constraints. Given the high precision required in the construction industry, accurately tracking the optimized trajectory is essential. However, challenges such as partial observability and external vibrations complicate this task. To address these issues, a Linear Quadratic Gaussian control framework is applied, enabling the robot to track the optimized trajectories with precision. Simulation results show that the proposed controller enables precise end effector positioning with errors under 4 mm, even in the presence of external wind disturbances. Through comprehensive simulations, our approach allows for an in-depth exploration of the system’s nonlinear dynamics, trajectory optimization, and control strategies under controlled yet highly realistic conditions. The results demonstrate the feasibility of CDPRs for automating panel installation and provide insights into their practical deployment.

CDPR↗
DOE OSTI2025

Analysis of Degradation and Electrochemical Behavior of Lithium–Oxygen Batteries under Lean Electrolyte Conditions

Lithium-oxygen batteries have garnered much attention in the past decades due to their high energy density and active material abundancy. Decreasing the electrolyte volume is critical to achieving a high energy density for practical applications. Recent works have demonstrated a serious performance limitation at lower electrolyte fillings. Here, in this work, we study specifically the difference between flooded and lean electrolyte lithium-oxygen batteries by systematically tracing the source of performance degradation. We find that the key contributor to the decrease in performance under lean electrolyte conditions stems from the lithium metal anode rather than the cathode. Solid electrolyte interphase (SEI) compositionally appears to be the same from X-ray photoelectron spectroscopy but the coverage is less uniform, and impedance is much higher under lean electrolyte conditions. Such an effect likely produced the observed poorer cycle performance at 10 μL cm -2 (similar to 28% cathode's pore volume filled) vs 100 μL cm -2 (278% of cathode's pore volume) at a capacity of 1.0 mAh cm -2 (0.1 mAh cm -2 ) using an electrolyte composition of 1 M LiTFSI in TEGDME.

Córdoba, Daniel [Argonne National Laboratory (ANL)↗
DOE OSTI2023

Effects of unburnt reaction progress on stretch flame dynamics under elevated temperatures

Flames in practical combustors and engines are inevitably subject to the effects of upstream chemical reaction progress and stretch, induced by elevated thermodynamic conditions and flow non-uniformities, respectively. Recent shock tube experiments and simulation studies on flame propagation have shown that flame speed under engine-relevant conditions can be enhanced with non-negligible upstream chemical reaction progress, especially when low-temperature heat release is involved in the unburnt mixture. On the other hand, depending on the mixture equivalence ratio and diluents, nonequidiffusion (including the non-unity Lewis number effect and the preferential diffusion effect) can couple with the flame stretch to fundamentally affect the flame propagation, which can manifest as either facilitation or suppression. Depending on the transport property of a reacting mixture, there hence can be either inhibition or promotion from unburnt reaction progress and stretch effects on flame propagation. In the current work, through one-dimensional numerical simulations of transient planar and spherical flames of n-heptane/air under elevated thermodynamic conditions, the combined effects of upstream chemical reaction progress and stretch on flame propagation are investigated. Results show that for both lean and rich n-heptane/air mixtures, flame speed can be substantially promoted with reaction progress, while the rich mixtures can exhibit opposite stretch dependence after first-stage ignition. Different definitions of effective Lewis number are adopted to explain the change in Markstein length for spherical flame in lean and rich mixtures reformed by the low temperature chemistry. Further, this work fills an important gap in laminar premixed flame research relevant to practical combustion systems and can provide useful insight into local turbulent flame behaviors and phi-sensitivity in engine combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Management of Risks Associated with Application of Novel Materials in Novel Operating Environments in Novel Reactor Designs

There is currently no widely agreed, detailed general method for licensing a novel plant incorporating novel materials (or materials being deployed in novel environments); in many such situations, there are no directly applicable engineering code cases for decision-makers (including regulators) to rely on. This paper discusses a framework for solving this problem that is based on the Reliability and Integrity Management (RIM) approach delineated in ASME BPVC Section XI Division 2. NRC Regulatory Guide 1.246, Rev. 0, endorses, with conditions, the subject portion of the ASME Code. The proposed framework is meant to support development of a licensing case by addressing certain technical challenges. The framework discussed here is compatible with the Licensing Modernization Project, but applying it in a specific case will call for advances in the state of practice, if not the state of the art. The RIM approach calls for applicants to (a) allocate reliability targets to plant structures, systems, and components (SSCs), (b) show that they are able to relate the currently observed physical condition of each SSC in the program to its failure probability well enough to determine whether the target reliability allocations are being satisfied, allowing for uncertainty related to the novelty of the materials/designs/operating environments, and (c) be able to demonstrate that the proposed program of surveillances will reliably detect unacceptable degradation of an SSC before SSC failure occurs. These challenges are discussed in the paper, and a potentially applicable modeling approach based on cumulative damage rather than failure rates is briefly illustrated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗
DOE OSTI2022

Extended Barton–Bandis model for rock joints under cyclic loading: Formulation and implicit algorithm

In this paper, the Barton–Bandis model for rock joints is extended to cyclic loading conditions, without any new material parameter. Also developed herein is an implicit solution algorithm for the extended Barton–Bandis model, which can also be used for the original Barton–Bandis model for which an implicit algorithm has been unavailable. To this end, we first cast the Barton–Bandis model into an incremental elasto-plastic framework, deriving an expression for the elastic shear stiffness being consistent with the original model formulation. We then extend the model formulation to cyclic loading conditions, incorporating the dependence of shear stress and dilation on the joint position and the shearing direction. The extension is achieved by introducing a few state-dependent variables which can be calculated with the existing material parameters. For robust and accurate utilization of the model, we also develop an implicit algorithm based on return mapping, which is unconditionally stable and guarantees the satisfaction of the strength criterion. We verify that the proposed model formulation and algorithm produce virtually the same results as the original Barton–Bandis model under monotonic shearing conditions. Here, we then validate the extended Barton–Bandis model against experimental data on natural rock joints under cycling loading conditions. The present work thus enables the Barton–Bandis model, which has been exceptionally popular in research and practice, to be applicable to a wider range of problems in rock mechanics and rock engineering.

58 GEOSCIENCES↗
DOE OSTI2025

Theory and numerics of subspace approximation of eigenvalue problems

Large-scale eigenvalue problems arise in various fields of science and engineering and demand computationally efficient solutions. In this study, we investigate the subspace approximation for parametric linear eigenvalue problems, aiming to mitigate the computational burden associated with high-fidelity systems. Furthermore, we provide general error estimates under non-simple eigenvalue conditions, establishing some theoretical foundations for understanding the convergence behavior of subspace approximations. Numerical examples, including problems with one-dimensional to three-dimensional spatial domain and one-dimensional to two-dimensional parameter domain, are presented to demonstrate the efficacy of reduced basis method in handling parametric variations in boundary conditions and coefficient fields to achieve significant computational savings while maintaining high accuracy, making them promising tools for practical applications in large-scale eigenvalue computations.

Eigenvalue problems↗
DOE OSTI2025

One-dimensional heterocyclic carbene–Au metal–organic frameworks bridging ultra-high vacuum models and scalable liquid-phase growth

The controlled design of molecule–metal interfaces is central to the development of functional nanomaterials for catalysis, sensing, and molecular electronics. Here we show that the adsorption of a Janus-type diimidazolium precursor on gold yields one-dimensional (1D) N-heterocyclic carbene (NHC)–Au–NHC metal organic frameworks (MOFs) featuring positively charged gold nodes. Using synchrotron X-ray photoemission spectroscopy (XPS), near edge X-ray adsorption fine structure (NEXAFS) spectroscopy and scanning tunnelling microscopy (STM), we demonstrate that thermal activation promotes counterion removal and drives the formation of extended 1D arrays, characterized by ∼1.0 nm Au–Au spacing and adatom densities up to 0.6 atom nm −2 (∼4% of surface atoms). Importantly, we translate this ultra-high vacuum (UHV) benchmark into a scalable solution-phase protocol in ethanol, enabling 1D-MOF growth under mild, base-free, open-air conditions. The resulting films retain structural and electronic signatures of UHV-grown systems, bridging model studies and practical synthesis. This approach establishes NHC–metal frameworks as accessible, tunable platforms for catalysis and materials design.

Gold adatoms↗
DOE OSTI2025

Be₂C as a neutron moderator for molten salt reactors: A first-principles study of structural, electronic, and defect properties

Beryllium carbide (Be 2 C), valued for its high neutron moderation efficiency and low absorption cross section, is a promising high-temperature neutron moderator for molten salt reactors. Its practical adoption, however, demands significant technological maturation, requiring comprehensive theoretical and experimental studies of its response to different conditions, including high temperature and irradiation. Here, we report initial results on the fundamental properties and radiation-induced defects of Be 2 C, focusing on antisites, vacancies, interstitial atoms, and Frenkel pairs in the Be and C sublattices. Using density functional theory (DFT) and ab initio molecular dynamics (AIMD), we calculate the defects formation and binding energies, evaluating their dependence on the supercell size, charge states, and chemical environment. In general, carbon defects exhibit higher formation energies, greater sensitivity to cell size, and stronger impacts on the density of states compared to beryllium defects, with charged state the effects being more pronounced. Static DFT reveals multiple metastable interstitial configurations, while AIMD identifies ground states as C-C <100> dumbbells and octahedral Be interstitials. In conclusion, the diversity of metastable configurations and defect states complicates the diffusion mechanisms, requiring further molecular dynamics analysis to elucidate the mechanisms and rates of radiation-induced atomic transport, as well as the structural stability of Be2C.

Ab initio modeling↗
DOE OSTI2023

Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory

The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced-density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary N-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as physics-based features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of-principle calculations demonstrate that the model yields substantially improved energies, relative to reference values from configuration-interaction-based calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ontologizing health systems data at scale: making translational discovery a reality

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

The impact of helium on plasma-driven hydrogen permeation and implications for direct internal recycling in the fusion fuel cycle

Abstract Metal foil pumps (MFPs) are the leading technology for direct internal recycling (DIR) of hydrogen isotopes from the plasma exhaust in future fusion plants. MFPs rely on the concept of superpermeation, where superthermal H atoms directly absorb into the metal foil, rapidly diffuse, and desorb downstream. To date, studies of superpermeation have predominantly employed either pure hydrogen or in some cases trace levels of impurities. The plasma exhaust is expected to contain just ∼1% helium, but in DIR the source gas would be enriched in helium as hydrogen isotopes are extracted. In this work, we explore the impact of helium on hydrogen superpermeation at low temperature (75 °C–200 °C) using Pd-based foils. To first order, the flux scaled linearly with the hydrogen mole fraction. Stable permeation was observed until the helium fraction reached ∼80%, where the flux began to decline slowly with time. In addition, short term (1–5 min) exposure to pure helium plasma significantly attenuated subsequent hydrogen plasma permeation, and the degree was more dramatic at elevated temperature. This attenuation was correlated with He retention in the foils, which was detected by time-of-flight secondary ion mass spectrometry at low levels (<0.1 at. %) and limited to the near surface (<10 nm). Similar trends were observed among all alloys (Pd, PdAg, PdCu), and the foils were restored to full performance with an Ar + sputter clean. The potential for helium plasma exposure to impact MFP performance under these conditions has not been previously reported, and these findings have significant implications to the design and implementation of practical DIR systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗