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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 OSTI2023

Microstructural evolution and intermetallic formation in Zn-3Mg (wt%) powder mixture processed by high-pressure torsion

Severe plastic deformation (SPD) techniques have been used extensively over the past 40 years for producing strong metals and alloys. High-pressure torsion (HPT) is one of the most promising SPD techniques for achieving high strength through nanoscale grain refinement and phase transformation. In this research, a mixture of pure zinc (Zn) and magnesium (Mg) powders, Zn-3Mg (wt%), was HPT-processed under a pressure of 6 GPa for 1, 5, 10, 20, and 30 turns at room temperature to achieve a high strength biodegradable material. In order to understand the effects of pre-consolidation on the resulting microstructure and hardness, HPT processing was performed on loose powders placed in the die and also on a pre-compacted powder mixture and the resulting HPT disks were characterized by X-ray diffraction, scanning electron microscopy, atom probe tomography, and Vickers microhardness. In both cases, the HPT disk microstructures contained nanoscale grains, and stable and metastable strain-induced intermetallics, but an unusual softening appeared at large shear strains. Grain size, grain morphology, and the formation of different intermetallics were analyzed to explain the unusual hardness distribution, and it was found that an inverse Hall-Petch relationship between hardness and grain size exists. It is suggested that thermally-activated phenomena such as grain boundary sliding contributed to the strain-induced softening of this nano-structured biomaterial due to its low melting point. Finally, the current results are compared with those for HPT-processed cast alloys and hybrids of the same composition.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characterization of two distinct neutrophil serine protease–binding modes within a Staphylococcus aureus innate immune evasion protein family

Extracellular adherence protein domain (EAPs) proteins are a class of innate immune evasion proteins secreted by the human pathogen Staphylococcus aureus. EAPs are potent and selective inhibitors of cathepsin-G (CG) and neutrophil elastase (NE), which are the two most abundant neutrophil serine proteases (NSPs). Previous work from our group has shown that the prototypical EAP, EapH1, relies on plasticity within a single inhibitory site to block the activities of CG and NE. However, whether other EAPs follow similar structure–function relationships is unclear. To address this question, we studied the inhibitory properties of the first (Eap1) and second (Eap2) domains of the modular extracellular adherence protein of S. aureus and determined their structures when bound to CG and NE, respectively. We observed that both Eap1 and Eap2 displayed time-dependent inhibition of CG (on the order of 10 -9 M) and of NE (on the order of 10 -10 M). We also found that whereas the structures of Eap1 and Eap2 bound to CG showed an overall inhibitory mode like that seen previously for EapH1, the structures of Eap1 and Eap2 bound to NE revealed a new inhibitory mode involving a distal region of the EAP domain. Using site-directed mutagenesis of Eap1 and Eap2, along with enzyme assays, we confirmed the roles of interfacial residues in NSP inhibition. Taken together, our work demonstrates that EAPs can form structurally divergent complexes with two closely related serine proteases and further suggests that certain EAPs may be capable of inhibiting two NSPs simultaneously.

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

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R series (GOES-R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light-use efficiency model, (b) a linear relationship between the product of near-infrared reflectance of vegetation and photosynthetically active radiation (LIN-NIR v P) and EC tower GPP, and (c) a light response curve (LRC-NIR v P) between NIR v P and EC GPP. The LRC-NIR v P achieved the lowest mean absolute error for winter (2 µmol CO 2 m –2 s –1 ), spring (2.51 µmol CO 2 m –2 s –1 ), summer (1.43 µmol CO 2 m –2 s –1 ), and fall (1.35 µmol CO 2 m –2 s –1 ). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC-NIRvP and the light-use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures

Abstract Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials. The lattice mismatch between the core and shell materials can cause strong interface strain, which affects the surface structures. Therefore, surface functional properties such as catalytic activities can be designed by fine-tuning the misfit strain at the interface. To precisely control the core-shell effect, it is essential to understand how the surface and interface strains are related at the atomic scale. Here, we elucidate the surface-interface strain relations by determining the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which can lead to direct control over the resulting catalytic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training. Here, we demonstrate that our neural network, trained solely on megahertz-scale turbulence measurements from one machine (DIII-D), forecasts Type-I edge localized mode (ELM) onsets in a different tokamak (KSTAR) through zero-shot weight transfer following physics-consistent preprocessing without device-specific retraining. Through an explainable AI framework combining gradient-weighted class activation mapping with physics validation, we reveal that our network can internalize physics relationships governing the ELM instabilities rather than memorizing device-specific patterns. The network perceives spatiotemporal features that correlate consistently with independently calculated instability growth rates, magnetohydrodynamic stability limits, and pedestal structure dynamics. Statistical analyses of dimensionally-reduced saliency features reveal the identical triangular features between the saliency representations, instability growth rates, and prediction probability across tokamaks, providing evidence that our forecasting system can show tokamak-agnostic generalization. This work contributes to a foundation for explainable scientific AI systems, where cross-system developments are essential for transcending traditional domain-specific constraints.

AI↗
DOE OSTI2023

OzDES Reverberation Mapping Program: Hβ lags from the 6-yr survey

ABSTRACT Reverberation mapping measurements have been used to constrain the relationship between the size of the broad-line region and luminosity of active galactic nuclei (AGN). This R–L relation is used to estimate single-epoch virial black hole masses, and has been proposed to use to standardize AGN to determine cosmological distances. We present reverberation measurements made with Hβ from the 6-yr Australian Dark Energy Survey (OzDES) Reverberation Mapping Program. We successfully recover reverberation lags for eight AGN at 0.12 < z < 0.71, probing higher redshifts than the bulk of Hβ measurements made to date. Our fit to the R–L relation has a slope of α = 0.41 ± 0.03 and an intrinsic scatter of σ = 0.23 ± 0.02 dex. The results from our multi-object spectroscopic survey are consistent with previous measurements made by dedicated source-by-source campaigns, and with the observed dependence on accretion rate. Future surveys, including LSST, TiDES, and SDSS-V, which will be revisiting some of our observed fields, will be able to build on the results of our first-generation multi-object reverberation mapping survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

CRADA Number NFE-24-10495 with Algaeo, LLC (CRADA Final Report)

This study investigated the potential for a synthetic consortium of mutualistic terrestrial microbes— comprising the fungi Laccaria bicolor and Serendipita indica alongside bacterial Pseudomonas strains—to influence the growth and productivity of the freshwater microalgae Chlorella vulgaris. The project aimed to determine if microbial complexes engineered to enhance terrestrial plant growth could provide similar growth-promoting benefits or pathogen resistance within an aquatic algal system. Using a quantitative experimental design, C. vulgaris was co-cultured with the microbial mix under controlled laboratory conditions, with growth rates, biomass density, and metabolic activity monitored over a standard cultivation period. The results demonstrated no significant symbiotic relationship or growth enhancement between these terrestrial microbes and the microalgae, as the C. vulgaris maintained independent growth trajectories unaffected by the fungal or bacterial inoculants. We conclude that the specialized mutualisms of these fungi and bacteria are likely niche-specific to vascular plants and do not readily translate to the phycosphere of C. vulgaris. These findings are valuable to synthetic biologists and bioenergy researchers, as they define the functional boundaries of inter-kingdom microbial engineering and underscore the necessity of selecting niche-compatible species when designing consortia for industrial algal cultivation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Quantitative Characterization of the Microstructure-Property Relationships in Ni and MIEC Nanocatalyst-Infiltrated Ni/YSZ Anodes

The performance of solid oxide fuel cell (SOFC) anodes can be improved by infiltration of nanoscale electrocatalysts, which increases triple phase boundary (TPB) density, but only when the added TPBs are active. Quantitative characterization of the microstructure of infiltrated nanocatalysts remains a critical challenge in understanding their role in improved performance. This paper explores the relationship between electrochemical performance and microstructure for Ni/YSZ anodes infiltrated with Ni, gadolinium-doped-ceria (GDC), and Ni/GDC nanocatalysts using a combination of electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis, scanning electron microscopy (SEM) microstructural characterization, and three-dimensional (3-D) anode reconstruction. Estimated polarization resistance improvements as a result of nickel nanoparticle infiltration are discussed.

Rix, Jillian↗
DOE OSTI2025

Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data

Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Higher-order factorization machine for accurate surrogate modeling in material design

Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.

Factorization machine↗
DOE OSTI2024

The correlation between Arctic sea ice, cloud phase and radiation using A-Train satellites

Abstract. Climate warming has a stronger impact on Arctic climate and sea ice cover (SIC) decline than previously thought. Better understanding and characterization of the relationship between sea ice and clouds and the implications for surface radiation is key to improving our confidence in Arctic climate projections. Here we analyze the relationship between sea ice, cloud phase and surface radiation over the Arctic, defined as north of 60° N, using active- and passive-sensor satellite observations from three different datasets. We find that all datasets agree on the climatology of and seasonal variability in total and liquid-bearing (liquid and mixed-phase) cloud covers. Similarly, our results show a robust relationship between decreased SIC and increased liquid-bearing clouds in the lowest levels (below 3 km) for all seasons (strongest in winter) but summer, while increased SIC and ice clouds are positively correlated in two of the three datasets. A refined map correlation analysis indicates that the relationship between SIC and liquid-bearing clouds can change sign over the Bering, Barents and Laptev seas, likely because of intrusions of warm air from low latitudes during winter and spring. Finally, the increase in liquid clouds resulting from decreasing SIC is associated with enhanced radiative cooling at the surface. Our findings indicate that the newly formed liquid clouds reflect more shortwave (SW) radiation back to space compared to the surface, generating a cooling effect of the surface, while their downward longwave (LW) radiation is similar to the upward LW surface emission, which has a negligible radiative impact on the surface. This overall cooling effect should contribute to dampening future Arctic surface warming as SIC continues to decline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stepwise impregnation of bimetallic catalysts for carbon nanotube synthesis

Supported bimetallic catalysts are widely used for carbon nanotube (CNT) synthesis, yet the effects of impregnation procedure remain underexplored. Here, we investigated how the sequence of metal impregnation affects CNT synthesis. We prepared cobalt-molybdenum (Co-Mo) bimetallic catalysts on alumina supports via co- and stepwise impregnation, then compared their CNT synthesis performance under identical conditions. Stepwise-impregnated catalysts exhibited higher carbon yields than those prepared by co-impregnation. Notably, impregnating Mo after Co achieved the highest yield despite the lowest BET surface area. Synchrotron X-ray diffraction and visible/UV-Raman spectroscopy revealed that only this catalyst contained Al 2 (MoO 4 ) 3 with a MoO 4 structure. X-ray photoelectron spectroscopy clarified the surface chemistry: the catalyst with the highest CNT productivity exposed Mo entirely as Mo 6+ in Al 2 (MoO 4 ) 3 and CoMoO 4 , whereas the others contained both Mo 4+ and Mo 6+ . The MoO x species like Al 2 (MoO 4 ) 3 is known to suppress Co sintering during CNT synthesis, delaying catalyst deactivation and enhancing the carbon yield. Moreover, an inverse relationship was observed between carbon yield and the CoMoO 4 content. This is attributed to the consumption of catalytically active Co during CoMoO 4 formation, which reduces active sites for CNT growth. ICP-OES further confirmed higher Co and Mo loadings for stepwise catalysts, contributing to superior catalyst performance. The extent of CoMoO 4 formation strongly depended on the metal introduction sequence. In conclusion, this trend is illustrated by considering the relationship between the point of zero charge of support and the pH of the metal precursor solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The water–climate nexus: Intersections across sectors

Water security and climate change are important priorities for communities and regions worldwide. The intersections between water and climate change extend across many environmental and human activities. This Primer is intended as an introduction, grounded in examples, for students and others considering the interactions between climate, water, and society. In this Primer, we summarize key intersections between water and climate across four sectors: environment; drinking water, sanitation, and hygiene; food and agriculture; and energy. We begin with an overview of the fundamental water dynamics within each of these four sectors, and then discuss how climate change is impacting water and society within and across these sectors. Emphasizing the relationships and interconnectedness between water and climate change can encourage systems thinking, which can show how activities in one sector may influence activities or outcomes in other sectors. We argue that to achieve a resilient and sustainable water future under climate change, proposed solutions must consider the water–climate nexus to ensure the interconnected roles of water across sectors are not overlooked. Toward that end, we offer an initial set of guiding questions that can be used to inform the development of more holistic climate solutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Differential Organic Carbon Mineralization Responses to Soil Moisture in Three Different Soil Orders Under Mixed Forested System

Soil microbial respiration is one of the largest sources of carbon (C) emissions to the atmosphere in terrestrial ecosystems, which is strongly dependent on multiple environmental variables including soil moisture. Soil moisture content is strongly dependent on soil texture, and the combined effects of texture and moisture on microbial respiration are complex and less explored. Therefore, this study examines the effects of soil moisture on the mineralization of soil organic C Soil organic carbon in three different soils, Ultisol, Alfisol and Vertisol, collected from mixed forests of Georgia, Missouri, and Texas, United States , respectively. A laboratory microcosm experiment was conducted for 90 days under different moisture regimes. Soil respiration was measured weekly, and destructive harvests were conducted at 1, 15, 60, and 90 days after incubation to determine extractable organic C (EOC), phospholipid fatty acid based microbial community, and C-acquiring hydrolytic extracellular enzyme activities (EEA). The highest cumulative respiration in Ultisol was observed at 50% water holding capacity (WHC), in Alfisol at 100% water holding capacity, and in Vertisol at 175% WHC. The trends in Extractable Organic Carbon were opposite to that of cumulative microbial respiration as the moisture levels showing the highest respiration showed the lowest EOC concentration in all soil types. Also, extracellular enzyme activities increased with increase in soil moisture in all soils, however, respiration and EEA showed a decoupled relationship in Ultisol and Alfisol soils. Soil moisture differences did not influence microbial community composition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chemo-Mechanical Behavior and Stability of High-Loading Cathodes in Solid-State Batteries

Solid-state batteries can offer higher energy density and improved safety compared to lithium ion batteries, which use flammable liquid electrolytes. Increasing the ratio of cathode active materials in composite cathodes enhances the energy density and reduces manufacturing costs. Changes in the ratio of cathode active materials alter the microstructure and chemo-mechanical response of a cathode during operation. Understanding the relationship between composition, microstructure, and chemo-mechanical interactions is critical for optimizing solid-state cathodes. Here, in this study, we engineered composite cathodes with varying ratios of LiNi 0.8 Co 0.1 Mn 0.1 O 2 and Li 6 PS 5 Cl to systematically investigate the role of microstructural evolution in long-term chemo-mechanical transformations. Chemo-mechanical stresses resulting from the volume changes of the cathode active materials led to degradation mechanisms, such as fracture and interfacial delamination. Active material fracture and delamination led to underutilization of active material and significant capacity decay during cycling. Coatings that suppress active material-active material interactions during cycling may aid in suppressing the generation of local stress hotspots.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models

Substrate specificity is an essential characteristic of any enzyme's function and an understanding of the factors that determine this specificity is crucial for enzyme engineering. Unlike the structure of an enzyme which is directly impacted by its sequence, substrate specificity as an enzyme attribute involves a rather indirect relationship with sequence as it also depends on structural aspects that dictate substrate accessibility and active site dynamics. In this study, we explore the performance of classifier-based machine learning models trained on curated sequence and structural data for a class of glycosyltransferases (GTs), namely GT-Bs, to understand their substrate specificity determining factors. GTs enable the transfer of sugar moieties to other biomolecules such as oligosaccharides or proteins and are found in all kingdoms of life. In plants, GTs participate in the biosynthesis of plant cell wall biopolymers (e.g.: hemicelluloses and pectins) and are an integral part of the enzymatic machinery that enables the storage of carbon and energy as plant biomass. To elucidate the substrate specificity of uncharacterized GT-Bs, we constructed multi-label machine learning models (Support Vector Classifier, K-Nearest Neighbors, Gaussian Naïve-Bayes, Random Forest) that incorporate both sequence and structural features. These models achieve good predictive accuracies on test datasets. However, despite our use of structural information, we highlight that there is further scope for improvement in training these models to draw interpretable relationships between sequence, structure and substrate specificity determining motifs in GT-Bs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

In-situ determination of strain during transient burst testing and the temperature dependence of Zircaloy-4 claddings

Understanding fuel system behavior during postulated loss-of-coolant accidents is pertinent for continued safe and efficient operation of light water reactors, particularly as higher burnups are being pursued and safety margins re-evaluated. Conventional mechanical models for the incumbent Zr alloys typically rely on the assumption that steady-state creep is the dominant fuel cladding response during transient accident conditions. To investigate this assumption, simulated accident burst testing was performed on Zircaloy-4 claddings with balloon behavior measured in-situ. Here, two distinct loading conditions were utilized during burst testing: (1) constant-gas-inventory where pressure was allowed to increase with temperature and (2) constant pressure. In-situ strains and strain rates were measured via 2-dimensional digital image correlation techniques and synchronized with temperature to determine deformation dependencies. The temperature dependence of strain rate was characterized by a two segment Arrhenius relationship, with a distinct transition between the high and low temperature/strain regimes. The average activation energy of the lower temperature/strain regime was 328 ± 25 kJ/mol, in agreement with the ~320 kJ/mol used for conventional LOCA models. However, the higher temperature/strain segment, which encompassed most of ballooning, showed increased activation energies as well as a dependence on whether the burst region was in view. For tests that burst away from the camera view, the average high temperature/strain segment activation energy was 635 ± 150 kJ/mol. For samples where the rupture opening formed in view, the average activation energy was 1015 ± 179 kJ/mol. This observed shift in temperature dependence indicates a transition in deformation mechanism at the end of life, possibly to time independent failure mechanisms, which has not yet been visualized in the literature for Zr alloys. Parameters at the transition points were analyzed to determine thresholds for this change in behavior, which occurred at an average hoop strain of 6.9 ± 2.1 %.

36 MATERIALS SCIENCE↗