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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Strategies for regeneration of Pt-alloy catalysts supported on silica for propane dehydrogenation

Catalyst stability, resistance to deactivation, and regeneration remain a challenge for high temperature reaction processes. For Pt alloys used in propane dehydrogenation (PDH), the primary pathways of catalyst deactivation include coke formation and metal nanoparticle sintering over time. Recent work shows that silica-supported catalysts provide excellent selectivity for this reaction, but the regenerability of silica-supported catalysts has not been established. In this work, we study a series of Pt alloys, including PtMn, PtZn, and PtSn, for the PDH reaction at 550 °C and 600 °C, and we subject the catalysts to regeneration over multiple cycles. While oxidation in air restores the reactivity completely with minimal catalyst sintering, it is surprising to find that these catalysts can also be regenerated in pure hydrogen. Here we explore the types of coke formed on these catalysts using in situ temperature programmed oxidation (TPO). Two types of coke are found: one on the metallic NP surface, and a second on the silica support. Our work shows that treatment in hydrogen causes redistribution of the coke between the metal and support, which can restore most catalytic activity lost during a reaction run. Finally, periodic introduction of H 2 during a reaction cycle may constitute an unexplored strategy for extending the lifetime of PDH catalysts.

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

Mechanisms and site requirements for NO and NH 3 oxidation on Cu/SSZ-13

Two series of Cu/SSZ-13 catalysts were synthesized via aqueous solution and solid-state ion exchange using SSZ-13 supports of varying Si/Al ratios. The isolated and multinuclear Cu content of these catalysts were determined by H 2 temperature programmed reduction (H 2 -TPR). Multinuclear Cu in these catalysts, including in situ Cu- dimers formed from ZCu II OH coupling and permanent CuO clusters, are active species for dry NO oxidation. NH 3 oxidation on these catalysts follows an internal SCR (i-SCR) mechanism, i.e., a portion of NH 3 is first oxidized to NO, then NO is selectively reduced by the remaining NH 3 to N 2 . NH 3 oxidation displays distinct kinetic behavior below ~300 °C and above ~400 °C. At low temperature the results indicate that NH 3 -solvated mobile Cu-ions are the active centers. CuO clusters, when present, also contribute to the low temperature activity by catalyzing NH 3 oxidation to NO. At high temperature, in situ Cu-dimers and CuO clusters catalyze NH 3 oxidation to NO, and isolated Cu-ions catalyze SCR to realize the cascade turnovers. For both NO and NH 3 oxidation, Cu-dimers balanced by framework charges of close proximity appear to be more active than Cu-dimers balanced by distant framework charges. However, the former Cu-dimers are less stable than the latter and tend to split into monomers in the presence of vicinal Brønsted acid sites. Via density functional theory (DFT) calculations, the i-SCR mechanism for low temperature NH 3 oxidation, i.e., the energetic favorability for the involvement of the NO intermediate, is justified. Furthermore, the DFT results also agree with experimental data that the formation of Cu-dimers from ZCu II OH dimerization is essential for NH 3 oxidation at high temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

New bipolar host materials for high power efficiency green thermally activated delayed fluorescence $\mathrm{OLEDs}$

We report four bipolar molecules, namely m-CzPym, p-CzPym, m-CzTrz, and p-CzTrz, with carbazole (Cz) donor and a benzonitrile-substituted pyrimidine (Pym) or triazine (Trz) acceptor core were synthesized and characterized. The electron deficiency of heteroaryl cores together with the substitution pattern of benzonitrile were employed to tune the energy levels as well as the thermally activated delayed fluorescence (TADF) characteristics. The four molecules exhibited TADF behavior with inferior photoluminescent quantum yields (PLQYs) that limit their applications as emitters. These bipolar molecules were employed as TADF host materials for the benchmark TADF emitter 4CzIPN to achieve high-performing green TADF organic light-emitting diodes (OLEDs). Among the molecules, m-CzPym-hosted TADF-OLEDs achieved a maximum external quantum efficiency (EQE max ) of 31.5%, maximum power efficiency (PE max ) of 95.6 lm/W, and maximum current efficiency (CE max ) of 100.2 cd/A. Notably, p-CzPym-hosted TADF-OLEDs also achieved a PE max of 116.5 lm/W, turn-on voltage of 2.5 V, and impressive low efficiency roll-off performance (>89% of EQE max at 5000 cd/m 2 ), representing one of the highest efficiencies ever reported in 4CzIPN-doped devices. The high device efficiency can be ascribed to the balanced ambipolar carrier-transporting character of the host materials and high PLQY as well as the outstanding light outcoupling efficiency of the emitting layer.

42 ENGINEERING↗
DOE OSTI2022

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model to investigate the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8) for advanced open-source tritium transport and fuel cycle modeling

Tritium management is critical for the safety, sustainability, and economics of fusion energy systems, and advanced and reliable modeling tools help accelerate the development of tritium technologies. This paper presents the Tritium Migration Analysis Program, Version 8 (TMAP8), an open-source, MOOSE-based application developed to provide state-of-the-art tritium transport and fuel cycle modeling capabilities. TMAP8 aims to expand the capabilities of previous versions (i.e., TMAP4 and TMAP7) by leveraging modern computational techniques, ensuring high software quality assurance standards (key to building trust), and enabling multispecies, multiscale, and multiphysics simulations for integrated tritium transport modeling in complex geometries. This paper outlines TMAP8’s scope and rigorous development practices, emphasizing its transparency, accessibility, modularity, and reliability. We present the current suite of verification and validation cases based on those from TMAP4, demonstrating TMAP8’s accuracy and reliability against analytical solutions and experimental data. Additionally, the paper showcases TMAP8’s integrated fuel cycle modeling capabilities, highlighting its applicability at various scales and levels. The TMAP8 code and documentation are openly available, promoting collaborative development and widespread adoption within the fusion community. Future work will soon expand TMAP8’s verification and validation suite to include those from TMAP7 and other recent experimental studies for validation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

The Irradiated Brain Volume Within 12 Gy Is a Predictor for Radiation-Induced Changes After Stereotactic Radiosurgery in Patients With Unruptured Cerebral Arteriovenous Malformations

Our purpose was to determine whether the coverage of brain parenchyma within the 12 Gy radiosurgical volume (V12) correlates with the development of radiation-induced changes (RICs) in patients with unruptured cerebral arteriovenous malformations (AVM) after undergoing stereotactic radiosurgery (SRS).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. In conclusion, compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exceptional radiation resistance of hardened amorphous SiC under high-temperature hydrogen ion implantation

This study provides a compelling comparison of the structural and mechanical responses of single-crystal silicon carbide (sc-SiC), nanocrystalline silicon carbide (nc-SiC), and amorphous silicon carbide (am-SiC) to hydrogen ion implantation at 650 °C across three distinct fluences: low, medium, and high (LF, MF, HF). While both sc-SiC and nc-SiC exhibit blistering, microcracking, and exfoliation, am-SiC remains free of blisters, demonstrating superior resilience. Notably, nc-SiC, with its high density of stacking faults (SFs), requires a higher fluence to initiate blistering compared to sc-SiC. At high fluences, hydrogen accumulation at grain boundaries (GBs) in nc-SiC elevates internal gas pressure, intensifying exfoliation. In sc-SiC, blistering leads to increased hardness, whereas in nc-SiC, the degradation of the SF structure results in a reduction in hardness. In contrast, am-SiC undergoes structural relaxation during irradiation, resulting in a significant increase in hardness while maintaining its structural integrity, with only the formation of nano-sized spherical bubbles observed. Furthermore, these findings highlight the exceptional suitability of am-SiC for nuclear applications, where resistance to radiation-induced microcracking is critical.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A study on texture stability and the biaxial creep behavior of as-hydrided CWSR Zircaloy-4 cladding at the effective stresses from 55 MPa to 65 MPa and temperatures from 300°C to 400°C

The creep rupture of high burnup used nuclear fuel (> 45 GWD/MTU) cladding is regarded as one of the failure mechanisms during long-term dry storage. A high amount of zirconium hydride in the cladding matrix would degrade the mechanical properties of the cladding especially leading to delayed hydride cracking. To better understand the influence of zirconium hydride on the biaxial thermal creep performance and the crystalline texture stability of Zircaloy-4 cladding, the pressurized tube technique is applied to test the durability of the as-hydrided material. Tests were performed on as-received Zircaloy-4 tubular specimens as well as-hydrided ones with targeted 300 wt parts per million (wppm) or 750 wppm hydrogen. The biaxial creep experiments were conducted at temperatures from 300 degrees C to 400 degrees C and at equivalent stresses at mid-wall from 55 MPa to 65 MPa. The hydridation process prior to creep tests induces the formation of FCC delta-hydride platelets along the circumferential direction of the tube. This alignment and phase structure of hydride show no significant change after biaxial creep tests. The creep strain-rate negatively depends on the hydrogen content. The synchrotron wide-angle X-ray diffraction (WAXD) technique and electron backscatter diffraction (EBSD) analysis were applied for the study of the crystallographic orientation relationship. zirconium-to-hydride grain orientation follows Shoji-Nishiyama crystallographic relationship. This relationship is stable before and after creep deformation. These results of creep performance and texture stability of Zircaloy-4 claddings can help support the design basis of interim and long-term dry storage facilities.

creep↗
DOE OSTI2023

Bayesian uncertainty quantification of tristructural isotropic particle fuel silver release: Decomposing model inadequacy plus experimental noise and parametric uncertainties

Tristructural isotropic (TRISO) particle fuel is one of the most promising fuel concepts enabling high temperature and high burnup reactor operation. One dominant source of radioactivity released from the TRISO particles is silver (Ag), which is subject to a high release fraction and long decay life compared to other fission products. Previous modeling efforts using the fuel performance code BISON indicated nonnegligible uncertainties in modeling the diffusion process of fission products in TRISO compared to the Advanced Gas Reactor experiments. The overall uncertainties observed when modeling the fission product diffusion can result from uncertainties in model parameters, noisy experimental measurements, and deficiencies in the developed models. The three types of underlying uncertainties have not yet been properly quantified in open literature. Here, this paper presents the Bayesian uncertainty quantification (UQ) using massively parallelizable Markov chain Monte Carlo samplers. The uncertainties due to model parameters, model inadequacy, and experimental measurement noise are quantified, with the σ term used to represent the sum of the model inadequacy and measurement noise uncertainties. It is worth noting that this is the first time the σ term is inferred for nuclear fuel experiments, as compared to using prescribed values for uncertainty quantification in previous work. The parallelizable Markov chain Monte Carlo samplers efficiently infer the model parameters and the σ term, giving insight into physical parameters like diffusion coefficients and the combined model discrepancy and measurement noise. A subsequent forward uncertainty quantification (UQ) is also performed based on the calibration results to generate more accurate predictions of the Ag release. The model inadequacy plus experimental noise is the most dominant source of uncertainty compared to the parametric uncertainty. All the UQ analyses presented in this work are based on the second series of the irradiation experiments in the Advanced Gas Reactor program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications

Here, this paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational expense and uncertainties inherent in complex multiphysics simulations, this paper integrates Gaussian process (GP) variants, active learning, Bayesian inverse UQ, adaptive forward UQ, Bayesian optimization, evolutionary optimization, and Markov chain Monte Carlo (MCMC) within MOOSE. It also elaborates on the interaction among key MOOSE systems — Sampler, MultiApp, Reporter, and Surrogate — in enabling these capabilities. The modularity offered by these systems enables development of a multitude of probabilistic ML and UQ algorithms in MOOSE. Example code demonstrations include parallel active learning and parallel Bayesian inference via active learning. The impact of these developments is illustrated through five applications relevant to computational energy applications: UQ of nuclear fuel fission product release, using parallel active learning Bayesian inference; very rare events analysis in nuclear microreactors using active learning; advanced manufacturing process modeling using multi-output GPs (MOGPs) and dimensionality reduction; fluid flow using deep GPs (DGPs); and tritium transport model parameter optimization for fusion energy, using batch Bayesian optimization. These capabilities are part of the MOOSE framework.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithiumion batteries, complicating accurate health diagnosis and prognosis. Separate diagnostic cycles are often used to untangle the battery’s current state of health from prior complex aging patterns. However, these same diagnostic cycles alter the battery’s degradation trajectory, are time-intensive, and cannot be practically performed in onboard applications. Here, in this work, we leverage portions of operational measurements in combination with an interpretable machine learning model to enable rapid, onboard battery health diagnostics and prognostics without offline diagnostic testing and the requirement of historical data. We integrate mechanistic constraints within an encoder-decoder architecture to extract electrode states in a physically interpretable latent space and enable improved reconstruction of the degradation path. The health diagnosis model framework can be flexibly applied across diverse application interests with slight fine-tuning.

battery aging reconstruction↗
DOE OSTI2023

Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements

Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li 0.375 Sr 0.4375 )(Ta 0.375 Nb 0.375 Zr 0.125 Hf 0.125 )O 3-δ compared with the (Li 0.375 Sr 0.4375 )(Ta 0.75 Zr 0.25 )O 3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.

36 MATERIALS SCIENCE↗