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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

QSL Squasher: A Fast Quasi-separatrix Layer Map Calculator

Quasi-Separatrix Layers (QSLs) are a useful proxy for the locations where current sheets can develop in the solar corona, and give valuable information about the connectivity in complicated magnetic field configurations. However, calculating QSL maps, even for two-dimensional slices through three-dimensional models of coronal magnetic fields, is a non-trivial task, as it usually involves tracing out millions of magnetic field lines with immense precision. Thus, extending QSL calculations to three dimensions has rarely been done until now. In order to address this challenge, we present QSL Squasher—a public, open-source code, which is optimized for calculating QSL maps in both two and three dimensions on graphics processing units. The code achieves large processing speeds for three reasons, each of which results in an order-of-magnitude speed-up. (1) The code is parallelized using OpenCL. (2) The precision requirements for the QSL calculation are drastically reduced by using perturbation theory. (3) A new boundary detection criterion between quasi-connectivity domains is used, which quickly identifies possible QSL locations that need to be finely sampled by the code. That boundary detection criterion relies on finding the locations of abrupt field-line length changes, which we do by introducing a new Field-line Length Edge (FLEDGE) map. We find FLEDGE maps useful on their own as a quick-and-dirty substitute for QSL maps. QSL Squasher allows construction of high-resolution 3D FLEDGE maps in a matter of minutes, which is two orders of magnitude faster than calculating the corresponding 3D QSL maps. We include a sample of calculations done using QSL Squasher to demonstrate its capabilities as a QSL calculator, as well as to compare QSL and FLEDGE maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Final Technical Report on Quantifying Dependability Attributes of Software Based Safety Critical Instrumentation and Control Systems in Nuclear Power Plants

With the current transition from analog to digital instrumentation and control systems in nuclear power plants, the number and variety of software-based systems have significantly increased. The sophisticated nature and increasing complexity of software raises trust in these systems as a significant challenge. The trust placed in a software system is typically termed software dependability. Software dependability analysis faces uncommon challenges since software systems’ characteristics differ from those of hardware systems. The lack of systematic science-based methods for quantifying the dependability attributes in software-based instrumentation as well as control systems in safety critical applications has proved itself to be a significant inhibitor to the expanded use of modern digital technology in the nuclear industry. Dependability refers to the ability of a system to deliver a service that can be trusted. Dependability is commonly considered as a general concept that encompasses different attributes, e.g., reliability, safety, security, availability and maintainability. Dependability research has progressed significantly over the last few decades. For example, various assessment models and/or design approaches have been proposed for software reliability, software availability and software maintainability. Advances have also been made to integrate multiple dependability attributes, e.g., integrating security with other dependability attributes, measuring availability and maintainability, modeling reliability and availability, quantifying reliability and security, exploring the dependencies between security and safety and developing integrated analysis models. However, there is still a lack of understanding of the dependencies between various dependability attributes as a whole and of how such dependencies are formed. To address the need for quantification and give a more objective basis to the review process -- therefore reducing regulatory uncertainty -- measures and methods are needed to assess dependability attributes early on, as well as throughout the life-cycle process of software development. In this research, extensive expert opinion elicitation is used to identify the measures and methods for assessing software dependability. Semi-structured questionnaires were designed to elicit expert knowledge. A new notation system, Causal Mechanism Graphing, was developed to extract and represent such knowledge. The Causal Mechanism Graphs were merged, thus, obtaining the consensus knowledge shared by the domain experts. In this report, we focus on how software contributes to dependability. However, software dependability is not discussed separately from the context of systems or socio-technical systems. Specifically, this report focuses on software dependability, reliability, safety, security, availability, and maintainability. Our research was conducted in the sequence of stages found below. Each stage is further examined in its corresponding chapter. Stage 1 (Chapter 2): Elicitation of causal maps describing the dependencies between dependability attributes. These causal maps were constructed using expert opinion elicitation. This chapter describes the expert opinion elicitation process, the questionnaire design, the causal map construction method and the causal maps obtained. Stage 2 (Chapter 3): Elicitation of the causal map describing the occurrence of the event of interest for each dependability attribute. The causal mechanisms for the “event of interest” were extracted for each of the software dependability attributes. The “event of interest” for a dependability attribute is generally considered to be the “attribute failure”, e.g. security failure. The extraction was based on the analysis of expert elicitation results obtained in Stage 1. Stage 3 (Chapter 4): Identification of relevant measurements. Measures for the “events of interest” and their causal mechanisms were obtained from expert opinion elicitation for each of the software dependability attributes. The measures extracted are presented in this chapter. Stage 4 (Chapter 5): Assessment of the coverage of the causal maps via measures. Coverage was assessed to determine whether the measures obtained were sufficient to quantify software dependability, and what measures are further required. Stage 5 (Chapter 6): Identification of “missing” measures and measurement approaches for concepts not covered. New measures, for concepts that had not been covered sufficiently as determined in Stage 4, were identified using supplementary expert opinion elicitation as well as literature reviews. Stage 6 (Chapter 7): Building of a detailed quantification model based on the causal maps and measurements obtained. Ability to derive such a quantification model shows that the causal models and measurements derived from the previous stages (Stage 1 to Stage 5) can form the technical basis for developing dependability quantification models. Scope restrictions have led us to prioritize this demonstration effort. The demonstration was focused on a critical system, i.e. the reactor protection system. For this system, a ranking of the software dependability attributes by nuclear stakeholders was developed. As expected for this application, the stakeholder ranking identified safety as the most critical attribute to be quantified. A safety quantification model limited to the requirements phase of development was built. Two case studies were conducted for verification. A preliminary control gate for software safety for the requirements stage was proposed and applied to the first case study. The control gate allows a cost effective selection of the duration of the requirements phase.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Refractory-based thermal energy storage for industrial process heat: one-dimensional modeling, control, and optimization

The variable and weather-dependent output of wind and solar power plants present a substantial challenge for planning and operating electricity-systems, particularly in the absence of cost-effective and dispatchable energy storage technologies. This study investigates a high-temperature, electrically heated, refractory-based thermal energy storage (RTES) system that stores electrical energy as sensible heat in dense ceramic bricks over the 950–1800 °C range. The stored heat can be discharged as a controlled hot-gas stream for industrial heating, fuel substitution in high-temperature processes, or electricity generation. The main novelty is a comprehensive modelling, control, mapping, and optimization framework that integrates one-dimensional transient gas–solid heat transfer, fan-assisted discharge, bypass-flow regulation, reheating logic, fan-power evaluation, insulation-loss assessment, and genetic-algorithm-based design optimization. The model uses feedback from outlet temperature and delivered power to regulate discharge, while a two-stage genetic algorithm optimizes brick-channel geometry, gas-flow operation, and multilayer insulation thicknesses. Storage capacities below 50 MWh and discharge powers of 5–30 MW are analyzed to evaluate hold time, thermal delivery, fan-power penalty, heat loss, state-of-charge evolution, and indicative capital cost. Results demonstrate that optimized and well-insulated refractory-based thermal energy storage units can provide stable, efficient, and repeatable heat delivery over multiple discharge cycles. The generated performance and cost maps support modular refractory thermal energy storage as a practical option for large-scale integration of wind and solar generation and for high-temperature industrial process heat.

25 ENERGY STORAGE↗
DOE OSTI2019

Process Mining in Healthcare - A Case Study for the Corporate Data Warehouse of the Veterans Affairs Office

Researchers at Oak Ridge National Laboratory are working with the Veteran’s Affairs Administration Office (VA) on a series of studies having the objective of detecting harmful anomalies (i.e., hazards) in VA health information technology systems using its Corporate Data Warehouse (CDW). This progress report describes an ORNL study focused on applying a process mining methodology to that CDW database. In the approach presented herein, process mining methodology is combined with additional software tools, metrics, and filters to permit a quick examination of large volumes of data to address specific research questions. In this work, a case study is presented in which the performance of the combined ORNL approach is evaluated by applying it to the CDW database. We performed an evidence-based study to effectively identify process models and to define metrics of frequency and performance for four health care domains: Consults, Radiology, Laboratory, and Outpatient Medication (RxOut) orders. Additionally, we classified the termination classes of the different cases by mapping to the OASIS Human Task Specification standard. We demonstrated, via process mining, that extracted raw data can aid the understanding of the flow of information in different clinical order processes. We showed that using the stepby- step approach described herein to discover processes in raw electronic health record data can be extremely effective in revealing irregular state transitions in the data and understanding clinical order information flows that are not apparent in analyzing the CDW as it is, without feature extraction.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

A Road Map for the Advanced Manufacturing of Ferritic-Martensitic Steels

Advanced manufacturing (AM) is a disruptive manufacturing process often referred to as “the next industrial revolution” because of its ability to fabricate components with complex geometries and site-specific materials and properties. While other industries, like automotive, aerospace, and fossil-fired power companies, are adopting and evaluating AM processes, the nuclear industry, including the fusion materials community, has been somewhat slow to capitalize on the seemingly beneficial aspects of AM. To address this gap, Oak Ridge National Laboratory is evaluating candidate AM techniques to fabricate nuclear-relevant materials including ferritic-martensitic (FM) steels. This paper discusses the development of a road map for AM approaches for FM steels. Specifically, the connection among alloy composition, additive processes, processing conditions, and postprocessing and the resulting microstructure using both wire-based and powder-based directed energy deposition techniques is detailed. Lastly, strategies to develop specialized alloys for additive manufacturing are outlined.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

ALMA Cycle 1 observations of the HH 46/47 molecular outflow: structure, entrainment, and core impact

We present Atacama Large Millimeter/sub-millimeter Array Cycle 1 observations of the HH 46/47 molecular outflow using combined 12 m array and Atacama Compact Array observations. The improved angular resolution and sensitivity of our multi-line maps reveal structures that help us study the entrainment process in much more detail and allow us to obtain more precise estimates of outflow properties than in previous observations. We use {sup 13}CO (1–0) and C{sup 18}O (1–0) emission to correct for the {sup 12}CO (1–0) optical depth to accurately estimate the outflow mass, momentum, and kinetic energy. This correction increases the estimates of the mass, momentum, and kinetic energy by factors of about 9, 5, and 2, respectively, with respect to estimates assuming optically thin emission. The new {sup 13}CO and C{sup 18}O data also allow us to trace denser and slower outflow material than that traced by the {sup 12}CO maps, and they reveal an outflow cavity wall at very low velocities (as low as 0.2 km s{sup −1} with respect to the core’s central velocity). Adding the slower material traced only by {sup 13}CO and C{sup 18}O, there is another factor of three increase in the mass estimate and 50% increase in the momentum estimate. The estimated outflow properties indicate that the outflow is capable of dispersing the parent core within the typical lifetime of the embedded phase of a low-mass protostar and that it is responsible for a core-to-star efficiency of 1/4 to 1/3. We find that the outflow cavity wall is composed of multiple shells associated with a series of jet bow-shock events. Within about 3000 au of the protostar the {sup 13}CO and C{sup 18}O emission trace a circumstellar envelope with both rotation and infall motions, which we compare with a simple analytic model. The CS (2–1) emission reveals tentative evidence of a slowly moving rotating outflow, which we suggest is entrained not only poloidally but also toroidally by a disk wind that is launched from relatively large radii from the source.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2020

CBRN Situational Awareness Tools for the Modern Age

CBRN Situational awareness (SA) should be a key operational requirement in the CNI area of discussion, not only for the “boots-on-the-ground” warfighter, but at a strategic level. For example, maneuver plans will need to be developed for contamination avoidance and battlefield survivability. Large datasets from instruments dispersed across the battlefield will thus need to be collected and processed into a holistic assessment of the radiological hazard. From there, hazard maps then must be distributed at the tactical level, all while continuing to effectively engage a peer adversary. This vision represents a network of sensors, software tools, data processing algorithms, communications suites, and common operational picture (COP) platforms working in concert to inform warfighters and command echelons alike of evolving nuclear and radiological hazards. This complex system-of-systems and the ultimate goal of effective engagement in a nuclear or radiological-hazard battlefield can be achieved through pursuit of improvement in collaborative SA tools.

Becker, Eric M.↗
DOE OSTI2025

Acoustophoretic Additive Manufacturing for Scalable 3D Battery Electrodes

This project focused on investigating two acoustic-based processing methods: a nozzle-based printhead and a chamber that map to two different battery electrode architectures: (1) a line-pattern electrode and (2) a grid-pattern electrode. These two parallel manufacturing and electrode geometry explorations were proposed for the project to understand the process space of acoustic-based manufacturing methods to fabricate patterned battery electrodes. This two-path exploration also allowed us to de-risk the overall project and not rely on a single process for creating patterned electrodes. This project consisted of six high-level tasks aimed at transitioning the concept of acoustic focusing for battery electrodes from a technology readiness level (TRL) of 1 to 3 by project conclusion. Overall, we believe we have developed a practical and high-impact processing method that is chemistry agnostic and suitable for large-area fabrication of both 3D LIBs and other functional material systems where structuring on the scale of tens of microns has the potential to break conventional bulk material trade-offs in performance. In the case of batteries for electric vehicles, structuring 3D LIBs with our acoustic process breaks traditional energy and power trade-offs observed with conventional flat battery packs.

25 ENERGY STORAGE↗
DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Revealing the role of electron-electron correlations by mapping dissociation of highly excited D 2 + using ultrashort XUV pulses

Understanding electron-electron correlations in matter ranging from atoms to solids represents a grand challenge for both experiment and theory. Here, these correlations occur on attosecond timescales and have only recently become experimentally accessible. In the case of highly excited systems, the task of understanding and probing correlated interactions is even greater. In this work, we combine state-of-the-art light sources and advanced detection techniques with ab initio calculations to unravel the role of electron-electron correlation in D 2 photoionization by mapping the dissociation of a highly excited D 2 + molecule. Correlations between the two electrons dictate the pathways along which the molecule dissociates and lead to a superposition of excited ionic states. Using 3D Coulomb explosion imaging and electron-ion coincidence techniques, we assess the relative contribution of competing parent ion states to the dissociation process for different orientations of the molecule with respect to the laser polarization, which is consistent with a shake-up ionization process. As a step toward observing coherent superposition experimentally, we map the relevant nuclear potentials using Coulomb explosion imaging and show theoretically that such an experiment could confirm this coherence via two-path interference.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Quantifying Transient Uncertainty in the BEAVRS Benchmark Using Time Series Analysis Methods

Advances in computation have brought about significant improvements in creating fast-running high-fidelity simulations of nuclear cores. The BEAVRS benchmark is a highly-detailed PWR specification with two cycles of measured operational data used to validate high-fidelity core analysis methods. This PWR depletion benchmark captures the fine details of the LWR fuel assemblies, burnable absorbers, in-core fission detectors, core loading and shuffling patterns. Specifically, 58 of the 193 assemblies contain in-core detectors with measurements taken over 61 axial positions every month. These detectors are U-235 fission chambers with slightly varying mass of U-235. The collected signals are normalized on a given assembly permitting full core comparisons. The fuel layout for cycle 1 and instrument tube locations for the reactor are given in figures 1 and 2 respectively. Through a series of data processing and comparisons, it was shown that axially integrated radial maps of reaction rates were in close agreement between provided detector data and calculated data. More recently, the focus of the BEAVRS project has been on quantifying uncertainty to further assess the validity of aforementioned results. A close investigation of sources of error shows that uncertainties from operational nuclear data arise primarily from data measurements and processing. While the errors in axial realignment and interpolation seem significant, these sources affect only a small subset of data points near grid spacers and do not persist throughout the entire core. Instead, measurement uncertainty from detector count rates and radial integration dominate the uncertainty in measured data are determined as the most significant sources of error. This uncertainty analysis treated each given burnup step as independent of comparable data at neighboring burnup steps. More recent work has been targeted at understanding how reaction rates vary over time, in order to determine whether calculated reaction rates follow any observable trends. This abstract hones in on transient uncertainty quantification, in order to compare observed data against models for transient behavior. The first section of the paper uses linear regression tools to fit operational data, while the latter portion of the paper explores more complex simulation tools to fit operational data. Ultimately, the BEAVRS benchmark aims to serve as a true non-proprietary international benchmark for the validation of high-fidelity tools. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Facile and high spatial resolution ratio-metric luminescence thermal mapping in microfluidics by near infrared excited upconversion nanoparticles

A local area temperature monitor is important for precise control of chemical and biological processes in microfluidics. In this work, we developed a facile method to realize micron spatial resolution of temperature mapping in a microfluidic channel quickly and cost effectively. Based on the temperature dependent fluorescence emission of NaYF{sub 4}:Yb{sup 3+}, Er{sup 3+} upconversion nanoparticles (UCNPs) under near-infrared irradiation, ratio-metric imaging of UCNPs doped polydimethylsiloxane can map detailed temperature distribution in the channel. Unlike some reported strategies that utilize temperature sensitive organic dye (such as Rhodamine) to achieve thermal sensing, our method is highly chemically inert and physically stable without any performance degradation in long term operation. Moreover, this method can be easily scaled up or down, since the spatial and temperature resolution is determined by an optical imaging system. Our method supplied a simple and efficient solution for temperature mapping on a heterogeneous surface where usage of an infrared thermal camera was limited.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗
DOE OSTI2018

Towards nanoscale electrical measurements in liquid by advanced KPFM techniques: a review

Fundamental mechanisms of energy storage, corrosion, sensing, and multiple biological functionalities are directly coupled to electrical processes and ionic dynamics at solid–liquid interfaces. In many cases, these processes are spatially inhomogeneous taking place at grain boundaries, step edges, point defects, ion channels, etc and possess complex time and voltage dependent dynamics. This necessitates time-resolved and real-space probing of these phenomena. In this review, we discuss the applications of force-sensitive voltage modulated scanning probe microscopy (SPM) for probing electrical phenomena at solid–liquid interfaces. We first describe the working principles behind electrostatic and Kelvin probe force microscopies (EFM & KPFM) at the gas–solid interface, review the state of the art in advanced KPFM methods and developments to (i) overcome limitations of classical KPFM, (ii) expand the information accessible from KPFM, and (iii) extend KPFM operation to liquid environments. We briefly discuss the theoretical framework of electrical double layer (EDL) forces and dynamics, the implications and breakdown of classical EDL models for highly charged interfaces or under high ion concentrations, and describe recent modifications of the classical EDL theory relevant for understanding nanoscale electrical measurements at the solid–liquid interface. We further review the latest achievements in mapping surface charge, dielectric constants, and electrodynamic and electrochemical processes in liquids. Lastly, we outline the key challenges and opportunities that exist in the field of nanoscale electrical measurements in liquid as well as providing a roadmap for the future development of liquid KPFM.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Cesium incorporation in hollandite-rich multiphasic ceramic waste forms

Hollandite-rich multiphase waste form compositions processed by melt-solidification and spark plasma sintering (SPS) were characterized, compared, and validated for nuclear waste incorporation. Phase identification by x-ray diffraction (XRD) and electron back-scattered diffraction (EBSD) confirmed hollandite as the major phase present in these samples along with perovskite, pyrochlore and zirconolite. Distribution of selected elements observed by wavelength dispersive spectroscopy (WDS) maps indicated that Cs formed a secondary phase during SPS processing, which was considered undesirable. On the other hand, Cs partitioned into the hollandite phase in melt-processed samples. Further analysis of hollandite structure in melt-processed composition by selected area electron diffraction (SAED) revealed ordered arrangement of tunnel ions (Ba/Cs) and vacancies, suggesting efficient Cs incorporation into the lattice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Association mapping, transcriptomics, and transient expression identify candidate genes mediating plant–pathogen interactions in a tree

Invasive microbes causing diseases such as sudden oak death negatively affect ecosystems and economies around the world. The deployment of resistant genotypes for combating introduced diseases typically relies on breeding programs that can take decades to complete. To reflect how this process can be accelerated, we employed a genome-wide association mapping of ca. 1,000 resequenced Populus trichocarpa trees individually challenged with Sphaerulina musiva, an invasive fungal pathogen. Among crucial associations, three loci associated with resistance were identified and predicted to encode one putative membrane-bound L-type receptor-like kinase and two receptor-like proteins. A susceptibility-associated locus was predicted to encode a putative G-type D-mannose–binding receptor-like kinase. Multiple lines of evidence, including allele analysis, transcriptomics, binding assays, and overexpression, support the hypothesized function of these candidate genes in the P. trichocarpa response to S. musiva.

59 BASIC BIOLOGICAL SCIENCES↗