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DOE OSTI2025

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Characterization of Complex Interactions at the Gas–Solid Interface with in Situ Spectroscopy: The Case of Nitrogen-Functionalized Carbon

Interactions at the gas-solid interface drive physicochemical processes in many energy and environmental applications; yet, the challenges associated with characterization and development of these dynamic interactions in complex systems limit progress in developing effective materials. Thus, structure-property-performance correlations greatly depend on the development of advanced techniques and analysis methods for the investigation of gas-solid interactions. In this work, adsorption behavior of O 2 and humidified O 2 on nitrogen-functionalized carbon (N-C) materials was investigated to provide a better understanding of the role of nitrogen species in the oxygen reduction reaction (ORR). N-C materials were produced by solvothermal synthesis and N-ion implantation, resulting in a set of materials with varied nitrogen amount and speciation in carbon matrices with different morphologies. Adsorption behavior of the N-C samples was characterized by in situ diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and ambient pressure X-ray photoelectron spectroscopy (AP-XPS) experiments. A novel analysis approach for the interpretation of AP-XPS data was developed, allowing both the determination of overall adsorption behavior of each N-C material and identification of which nitrogen species were responsible for adsorption. The complementary information provided by in situ DRIFTS and AP-XPS suggests that O 2 adsorption primarily takes place on either electron-rich nitrogen species like pyridine, hydrogenated nitrogen species, or graphitic nitrogen. Adsorption of O 2 and H 2 O occurs competitively on solvothermally prepared N-Cs, whereas adsorption of H 2 O and O 2 occurs at different sites on N-ion implanted N-Cs, highlighting the importance of tuning the composition of N-C materials to promote the most efficient ORR pathway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Characterization of Lignin Streams during Bionic Liquid-Based Pretreatment from Grass, Hardwood, and Softwood

Delignification as a function of ionic liquid (IL) pretreatment has potential in terms of recovering and converting the fractionated lignin streams to renewable products. Renewable biogenic ionic liquids, or bionic liquids (eg. cholinium lysinate, ([Ch][Lys])), provide opportunities in terms of effective, economic and sustainable lignocellulosic biomass pretreatment. We have evaluated [Ch][Lys] pretreatment in terms of sugar and lignin yields for three different feedstocks: switchgrass, eucalyptus, and pine. Four lignin streams isolated during [Ch][Lys] pretreatment and enzymatic hydrolysis were comprehensively analyzed, tracking their changes in physical-chemical structures. We observed changes in major lignin linkages and lignin aromatics units (p-hydroxyphenyl (H), guaiacyl (G), and syringil (S)) that occurred during pretreatment. A compositional analysis of the different process streams and a comprehensive mass balance in conjunction with multiple analytical techniques (Nuclear Magnetic Resonance (NMR), Mass Spectroscopy, Fourier Transform Infrared Spectroscopy (FT-IR), Gel Permeation Chromatography (GPC)) is presented. Qualitative and quantitative analyses indicates that there are significantly more lignin-carbohydrate interactions for G-rich lignin in pine. The lignin removal and extent of lignin depolymerization for switchgrass and eucalyptus were higher than pine, and follows the order of switchgrass > eucalyptus > pine. The recovered lignin from pretreated liquid contained a lower relative amount of carbohydrate signals than raw biomass, indicating a high degree of dissociation of lignin carbohydrate complex (LCC) linkages for all samples analyzed. The insights gained from this work contribute to better understanding of physiochemical properties of lignin streams generated during [Ch][Lys] pretreatment, offering a starting point for lignin valorization strategies.

lignin, ionic liquids, NMR↗

DOE OSTI2025

Precision Local Burnup Assessment Through Dynamic Peak Fitting in Atom Probe Tomography for Depleted, Enriched, and Irradiated Metallic and Ceramic Fuels

Abstract Burnup estimation in nuclear fuels is vital for evaluating fuel performance, transportation, and safe fuel storage. Accurate assessments of burnup from service period and spent fuels involve tracking the consumption of fissile isotopes of uranium (U) offering a direct insight into energy changes within the fuels especially for thermal spectrum reactors. In current approach, mass spectroscopic technique in atom probe tomography (APT) is utilized for accurate quantification of U isotopes. Quantification of U peaks in mass spectrum is performed on asymmetric shapes due to delayed signals, known as thermal tails, particularly for poorly conducting samples analyzed in laser mode. In this study, we introduce a novel quantification tool for isotopic analysis from APT datasets by developing a fitting algorithm based on shapes of the peaks. A MATLAB-based dynamic peak fitting toolbox is developed and designed to adapt to various peak shapes, ensuring accurate quantification of U isotopes. The effectiveness of this approach is demonstrated in standard Ni-Cr sample, depleted and enriched U samples, and U-based fuels with different burnup levels. The viability of this approach for isotopic quantification is demonstrated on both metallic and ceramic fuels.

Burnup↗

DOE OSTI2019

Plutonium Particles in Samples Extracted from Hanford Tank Waste TX-118 and SY-102 (Interim Report)

This is an interim report on the characterization of plutonium particles from Hanford wastes. Plutonium-bearing particles were located in Hanford Waste Tank solids, SY-102 and TX-118. The particles in SY-102 were small, typically < 5 to 10 µm in length, and were crystalline plutonium oxide with an association of other elements at minor levels, including bismuth, phosphate, and iron based on Sanning Electron Microscopy (SEM) observations. Plutonium particles from SY-102 were extracted from the surface mount using the SEM-Focused Ion Beam (FIB) and prepared for Scanning Transmission Electron Microscopy (STEM). This was the first time that this type of analysis of Hanford tank waste solids has been attempted. The STEM analysis indicated a complex co-precipitation of plutonium oxide with bismuth and phosphate. Diffraction and imaging only partially matched with a PuO 2 phase. Pockets of phosphate and bismuth reside within the plutonium oxide suggesting that these phases formed at the same time. The SEM analysis indicated that the plutonium particles from TX-118 were a mixed bismuth-phosphate phase and plutonium oxide. These particles will be examined with SEM-FIB at a later time and then will undergo more detailed examination in the STEM.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

A Sulfoxide-Based Isobaric Labelling Reagent for Accurate Quantitative Mass Spectrometry

Modern proteomics requires reagents for exact quantification of peptides in complex mixtures. Peptide labelling is most typically achieved with isobaric tags that consist of a balancer and a reporter part that separate in the gas phase. An ingenious distribution of stable isotopes offers multiple reagents with identical molecular weight but a different mass of the reporter groups, allowing relative quantification of multiple samples in one measurement. In this work, we discuss a new isobaric labelling reagent, where the balancer and the reporter are linked by a sulfoxide group, which, based on the sulfoxide pyrolysis, leads to easy and asymmetric cleavage at low fragmentation energy. The fragmentation of our new design is significantly improved, yielding more intense complementary ion signals, allowing complementary ion cluster analysis as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Faraday detector uranium isotope ratio measurement: Insights from solution- and laser ablation-based sampling methodologies on the Neoma MC-ICP-MS

In this study we utilize an all-faraday cup detector configuration on the Neoma MC-ICP-MS to perform uranium isotope ratio determinations from certified reference materials and a nuclear fuel precursor material via solution and laser ablation based sampling methodologies. The goal in performing these measurements is primarily to demonstrate that the all-faraday cup method can produce highly precise and accurate isotope ratios even at relatively low signal intensity levels while also developing a preliminary understanding of how the complex interplay between different amplifier resistor levels (e.g. 10 11 vs 10 13 Ω) and integration times impacts the precision and accuracy of different types of measurements. Our results show that the faraday cups with 1013 Ω resistors in their amplifier feedback loops can be used to produce accurate and relatively precise isotope ratios for ion beams down to ~5 K cps. However, at the lower end of the signal intensities observed in this study, longer integration times are necessary to mitigate the impact of ion beam instability that can occur during laser ablation based sampling. Despite this potential limitation, the all-faraday cup method is highly versatile as exemplified by the analysis of a collection of uranium reference materials with highly variable uranium isotopic compositions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Best practices and benchmarks for intact protein analysis for top-down mass spectrometry

One gene can give rise to many functionally distinct proteoforms, each of which has a characteristic molecular mass. Many laboratories, however, forgo the challenging mass analysis of intact proteins. Instead, these researchers resort to well-established methods for proteolytic digests. In this work, the Consortium for Top-Down Proteomics (CTDP) addresses the unmet need for robust protocols for obtaining intact protein mass spectra. We quantify and rationalize the signal suppression associated with common interfering substances. A decision tree, based upon sample composition and experimental goals, guides researchers to a given protocol. Cross-platform analytical benchmarks are provided to allow users to gauge their proficiency and to promote method standardization. This work should permit users at any experience level to obtain high-quality intact mass spectra of native and denatured proteins from mixtures of varying complexity, including intact antibodies and membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Morphology and property investigation of primary particulate matter particles from different sources

Particulate matter (PM) pollution has become a major environmental concern in many developing countries. PM pollution control remains a great challenge owing to the complex sources and evolution processes of PM particles. There are two categories of PM, i.e., primary and secondary PM particles, and the primary PM emissions play a key role in the formation of PM pollution. Knowledge of primary PM particle compositions, sources, and evolution processes is of great importance to the effective control of PM pollution. In order to characterize PM particles effectively, their fundamental properties including the morphology, concentration distribution, surface chemistry, and composition must be systematically investigated. In this study, we collected and analyzed six types of PM 10 and PM 2.5 particles from different sources using an in situ sampling approach. The concentration distributions of PM particles were analyzed and comparative analysis of the morphologies, distributions, capture mechanisms, and compositions of PM particles was conducted using scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and energy-dispersive X-ray spectroscopy. We found that there were significant differences in the structures, morphologies, and capture mechanisms of PM 2.5 and PM 10 particles. As a result, the systematic comparative investigation in this work will benefit the study of evolution processes and the effective control of PM pollution in the future.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Role of scan strategies and heat treatment on grain structure evolution in Fe-Si soft magnetic alloys made by laser-powder bed fusion

A major goal in printing soft magnetic Fe-Si steels using additive manufacturing is to take advantage of the potential for complex geometric designs and site-specific grain control. One major step in the processing of these alloys is understanding how processing parameters might impact how the as-built microstructure responds to annealing (i.e. the annealing response). The impact of scan strategy on the annealing response for thin wall geometries is specifically explored. Two scan strategies were explored for a thin wall geometry that produced a strongly columnar grain structure and equiaxed grain structure. Additionally, samples from both scan strategies annealed at 1200 °C showed a marked difference in annealing response with the more equiaxed sample seeing full recrystallization and grain growth, while the more columnar grain structure saw little change in microstructure. After analysis through characterization techniques and thermal-mechanical simulations Differences in internal energy within the grains were ruled out because calculated GND density values were similar for both samples. The formation of secondary particles was ruled out as a contributing factor due to the type of oxide formations and their size. It was concluded that the contributing factor to the difference in the annealing response were a difference in the resulting grain size and the density of high angle grain boundaries. These two differences were largely attributed to differences in the thermal gradient conditions due to grains preferentially growing in the direction of the steepest thermal gradient.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the feasibility of using physics-informed machine learning for underground reservoir pressure management

In this work, we evaluate the feasibility of using physics-informed machine learning (PIML) for underground energy-related pressure management. To this end, we develop a PIML framework to manage underground reservoir pressures by training neural networks to determine fluid extraction rates for dedicated extraction wells during fluid injection operations given a range of reservoir conditions (e.g., transmissivity and storativity). We implement an automatically-differentiable analytical physics model of fluid flow in porous media within the PIML framework as a proxy for more complicated models. This allows us to execute a sufficient number of training scenarios to fully evaluate the feasibility of using PIML to support pressure management activities. We quantify the number of physics-model parameters required for automatic differentiation to become more efficient than finite-difference gradient calculations. We use a simple scenario with a single injector, extractor, and critical location for our feasibility analysis. We evaluate the effect of the size of the training dataset (i.e., the number of reservoir condition samples) on the accuracy and efficiency of the PIML framework. For an equivalent number of model evaluations, the larger training dataset took less time to train and produced a neural network that was able to more accurately manage reservoir pressures. We also evaluate the effect of the training dataset batch size (i.e., number of reservoir condition samples used to update the neural network coefficients during training; i.e., how the training dataset is partitioned). While training ran faster with larger batch sizes, they produced neural networks that managed pressures less accurately. We demonstrate the approach on a more complex scenario involving 10 injectors, 10 extractors, and 4 critical locations (a relatively high well density of 20 wells/km2). We provide the number of forward and adjoint model evaluations required in each case as an indication of the feasibility of using PIML for pressure management when more complicated physics models with longer execution times are used.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Typical aqueous rare earth element behavior in co-produced Brines, Wyoming

Normalization of Rare Earth Elements (REEs) is important to remove the distracting effects of the Oddo–Harkins rule and provide a meaningful baseline. Normalizations for rocks are well developed and include chondritic meteorites, UCC, PM, PAAS, and NASC. However normalizations for aqueous REEs are limited to oceanic regions such as the North Pacific Deep Water or North Atlantic Surface Water. This leaves water in contact with continental lithologies without a suitable normalization. We present a preliminary continental aqueous REE normalization derived from 38 deep basin hydrocarbon brines in Wyoming. The REEs in these waters are seven orders of magnitude more dilute than NASC but with significant europium enrichment. Gromet 1984 reports NASC Eu/Eu* is 0.2179, whereas in the normalization offered here, Eu/Eu* is 3.868. These waters also are free from the distracting reduction-oxidation cerium behavior found in ocean normalizations. Because these samples exhibit both the uniform behavior of NASC and the absolute concentration of seawater, a normalization based upon them offers a unique combination of the advantages of both. We used single-peak gaussian analysis to quantify the mean values for each REE and estimate the distribution variability. Additional sample collection during the last year revealed that the Powder River Basin (PRB) is atypical relative to the other sampled basins of Wyoming. Those other basins are the Wind River Basin (WRB) Green River Basin (GRB) and Wamsutter Area (WA). A pre-normalization gadolinium anomaly (Gd/Gd*) of between 4 and 23 with a mean of 11.5, defines the PRB samples. Other basins in this study range from 1 to 7 with a mean of 2.8. Finally, we present a preliminary model for ligand-based behavior of REEs in these samples. This model identifies bicarbonate, bromide, and chloride as forming significant complexes with REEs contributing to REE solubility. The ligand model explains observed REEs in the sampled Cretaceous and Paleocene clastic reservoirs. However, the presence of more REEs than predicted in six samples suggests that there is an additional, unconsidered ligand contributing to REE dissolution. Further work will identify this ligand, which appears to be confined to calcium-cemented and dolostone systems.

02 PETROLEUM↗

DOE OSTI2017

Numerical Uncertainties vs. Model Uncertainties in Two-phase Flow Simulations

In recent years, verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. In general, these activities deal with propagations of uncertainties in computer code simulations, e.g., through system analysis codes. However, most existing such activities in thermal-hydraulics analysis have been primarily focused on model uncertainties, while numerical uncertainties were largely overlooked. Numerical uncertainties (or numerical errors) can appear in many different forms, e.g., round-off error, statistical sampling error, and discretization error. As pointed out by Oberkampf and Roy, discretization error is usually the largest however the most difficult type of error to estimate reliably. In thermal-hydraulics analysis, especially two-phase flow simulations commonly encountered in reactor safety analysis, the lack of consideration on discretization error is mainly due to the difficulty in estimating them. Accurate estimations on discretization error require continuous mesh refinement and/or implementation of high-order numerical schemes in system analysis code, both of which are unfortunately difficult to achieve for existing codes. At first, it should be emphasized that mesh refinement (or mesh sensitivity study) is a well-understood concept in the thermal-hydraulics field. However, it is not commonly practiced for many reasons, e.g., lack of understanding on numerical errors by code users, consideration on computational cost, deteriorated numerical stability performance with refined meshes, and etc. Secondly, high-order (e.g., second-order) numerical schemes are simply unavailable in almost any existing system analysis code. Implementation of high-order numerical schemes in these codes is difficult, if not impossible, given the complexity of these codes. On the other hand, people tend to argue that numerical uncertainties (or discretization error as we have emphasized) are not as important as the commonly large uncertainties in two-phase flow models, e.g., closure correlations. As a result, nodalization is sometimes played unintentionally, or maybe intentionally, to match experimental data, which is referred as 'user effects' or 'compensating errors' in thermal-hydraulics community. Such a practice is highly criticized by Levy. In this work, we will at first build a computer code that incorporates both first-order and second-order numerical methods to solve the two-phase flow problems. The first-order method resembles the one used in many existing system analysis codes; and the second-order method works as the reference to estimate numerical errors. Numerical verifications on spatial discretization schemes will be presented in the form of mesh convergence study. It will also be demonstrated that, in practical scenarios, discretization errors can be as large as, or even larger than, model uncertainties via case studies.

42 ENGINEERING↗

DOE OSTI2023

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

Abstract As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how “microbiomes within microbiomes” affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Analysis of Material Properties of Aged Rigid Polyurethane Foam

Polyurethane foams are organic materials which can be classified as mechanically rigid or flexible. They are chemically inert, relatively unaffected by the presence of water (cracking, swelling, absorption), and can be manufactured in densities from 3-40 lb/ft 3 . In addition, polyurethane foams have exceptional mechanical and thermal properties, which support use of polyurethane foams in the 9977 shipping packages. These packages are designed for shipment of special nuclear material and are approved for storage in the K-area complex (KAC) at Savannah River Site. Through the analysis of conditioned foams, a service life ofFR-3700 can be estimated for the storage of the 9977 packages. Thus, considering anticipated temperatures of the 9977 packages in the KAC, FR-3700 was conditioned at 160°F, 185°F, 215°F, 250°F, and 160°F with 50% relative humidity, which remains within the service temperature of-320°F - 250°F of the FR-3700 foam in the 9977 packages. Prior studies have analyzed foams after 7, 20, and 30 months of conditioning with the current study analyzing foams up to 55 months of conditioning. Considering the limited information on the effects of radiation on FR-3700, samples were irradiated with a cobalt-60 source at a high and low dose rate of 90.9 krad/hr and 16.67 krad/hr, respectively, to examine the effects of irradiation and dose rate on thermal properties. It was found that foams conditioned up to 55 months had comparable heat capacity values to FR-3700, with a slight increase due to humidity. Results suggest that fire additives began decomposing at higher conditioning temperatures, reducing the average percent mass loss. Overall, irradiation and humidity had minimal effects on thermal properties of FR-3700.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Nd{sub 2±x}Zr{sub 2∓x}O{sub 7±x/2} (−0.2≤x≤0.4) complex oxides: Effect of anion disorder on ionic conductivity

In the present work, a series of Nd{sub 2±x}Zr{sub 2∓x}O{sub 7±x/2} (−0.2≤x≤0.4) was prepared by self assisted gel-combution method followed by high temperature sintering at 1673 K. Thorough structural characterizations were done by X-ray diffraction and Raman spectroscopic techniques. The nominal compositions Nd{sub 1.6}Zr{sub 2.4}O{sub 7.2} and Nd{sub 1.8}Zr{sub 2.2}O{sub 7.1} were found to possess single-phasic pyrochlore structure whereas Nd{sub 2.0}Zr{sub 2.0}O{sub 7} and Nd{sub 2.2}Zr{sub 1.8}O{sub 6.9} consisted of a pyrochlore phase and a small amount of hexagonal Nd{sub 2}O{sub 3} as an impurity phase. Electrical behavior of the samples was examined by AC impedance analysis. Even though the activation energies of all the samples are not very different, a high pre-exponential factor for the Nd{sub 1.6}Zr{sub 2.4}O{sub 7.2} composition resulted in high ionic conductivity (3.37 × 10{sup −3} Scm{sup −1} at 773 K). This high ionic conductivity value makes it a superior candidate as an electrolyte material for SOFC applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗