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At least 721 records · Page 40

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DOE OSTI2024

Effects of processing conditions on flash sintering of commercial ZrN

Flash sintering (FS) is a potentially rapid and low-cost manufacturing technique for advanced ceramics. There are still many unknowns about ceramic FS, especially for highly conductive high-temperature ceramics (HTC), which often display decreased bulk conductivity with increasing temperature. This study qualitatively characterizes the flash behavior of highly conductive HTC materials using zirconium nitride (ZrN) as an example. The effects of processing parameters (e.g., DC voltage/electrical field strength, voltage ramp rate, post-flash holding time, mechanical pressure, sample milling, and choice of electrode materials) on ZrN flash behavior are also qualitatively studied and linked to samples microstructure and hardness. It is observed that best densification was achieved using 5 min Spex-milled ZrN powder under 25 MPa of applied pressure and constant 8 V DC. The potential mechanism for ZrN FS and the similarity and difference from FS of conventional oxides like YSZ have been proposed based on experimental observations, and the directions for future research are also pointed out.

Flash Sintering↗

DOE OSTI2023

Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population

Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.

Azevedo, Ana↗

DOE OSTI2023

Metabolic and transcriptomic study of pennycress natural variation identifies targets for oil improvement

Pennycress (Thlaspi arvense L.), a member of the Brassicaceae family, produces seed oil high in erucic acid, suitable for biodiesel and aviation fuel. Although pennycress, a winter annual, could be grown as a dedicated bioenergy crop, an increase in its seed oil content is required to improve its economic competitiveness. The success of crop improvement relies upon finding the right combination of biomarkers and targets, and the best genetic engineering and/or breeding strategies. In this work, we combined biomass composition with metabolomic and transcriptomic studies of developing embryos from 22 pennycress natural variants to identify targets for oil improvement. The selected accession collection presented diverse levels of fatty acids at maturity ranging from 29% to 41%. Pearson correlation analyses, weighted gene co‐expression network analysis and biomarker identifications were used as complementary approaches to detect associations between metabolite level or gene expression and oil content at maturity. The results indicated that improving seed oil content can lead to a concomitant increase in the proportion of erucic acid without affecting the weight of embryos. Processes, such as carbon partitioning towards the chloroplast, lipid metabolism, photosynthesis, and a tight control of nitrogen availability, were found to be key for oil improvement in pennycress. Besides identifying specific targets, our results also provide guidance regarding the best timing for their modification, early or middle maturation. Thus, this work lays out promising strategies, specific for pennycress, to accelerate the successful development of lines with increased seed oil content for biofuel applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Pulsed laser deposition nickel oxide on crystalline silicon as hole selective contacts

In this work, the authors report the use of pulsed laser deposition to fabricate nickel oxide layers on silicon and assess the applicability of this structure as a hole selective contact. Films were deposited at temperatures of 300, 500, 700, and 900 °C. Results from reflective high energy electron diffraction patterns indicate increasing crystallinity with temperature up to 500 °C. However, layers deposited at 900 °C are amorphous. Nevertheless, results from atomic force microscopy, contact resistance measurements, and photoluminescence lifetime imaging indicate that films deposited at 900 °C have the best film smoothness, lowest specific contact resistivity, and highest lifetimes, and therefore, they are best suited for hole selective contact applications. Kelvin probe force microscopy and x-ray photoelectron spectroscopy were also carried out to study the effects of deposition temperature.

42 ENGINEERING↗

DOE OSTI2023

Heteroepitaxial growth of Ga2O3 on 4H-SiC by liquid-injection MOCVD for improved thermal management of Ga2O3 power devices

We report on the growth of monoclinic β- and orthorhombic κ-phase Ga2O3 thin films using liquid-injection metal-organic chemical vapor deposition on highly thermally conductive 4H-SiC substrates using gallium (III) acetylacetonate or tris(2,2,6,6-tetramethyl-3,5-heptanedionato) gallium (III). Both gallium precursors produced the β phase, while only the use of the latter led to growth of κ-Ga2O3. Regardless of the used precursor, best results for β-Ga2O3 were achieved at a growth temperature of 700 °C and O2 flows in the range of 600–800 SCCM. A relatively narrow growth window was found for κ-Ga2O3, and best results were achieved for growth temperatures of 600 °C and the O2 flow of 800 SCCM. While phase-pure β-Ga2O3 was prepared, κ-Ga2O3 showed various degrees of parasitic β phase inclusions. X-ray diffraction and transmission electron microscopy confirmed a highly textured structure of β- and κ-Ga2O3 layers resulting from the presence of multiple in-plane domain orientations. Thermal conductivities of 53 nm-thick β-Ga2O3 (2.13 + 0.29/−0.51 W/m K) and 45 nm-thick κ-Ga2O3 (1.23 + 0.22/−0.26 W/m K) were determined by transient thermoreflectance and implications for device applications were assessed. Presented results suggest great potential of heterointegration of Ga2O3 and SiC for improved thermal management and reliability of future Ga2O3-based high power devices.

Materials Science↗

DOE OSTI2026

Nb 3 Sn coating of SRF cavity by cosputtering from a composite target

Here, we deposited an Nb 3 Sn film on the inner surface of a 2.6 GHz Nb superconducting radio frequency cavity by cosputtering using a composite of Nb and Sn tube targets in a DC cylindrical magnetron sputtering system, followed by the thermal annealing of the coated cavity. An aluminum mockup cavity, replicating a 2.6 GHz Nb SRF cavity geometry, was utilized to optimize deposition parameters, resulting in cosputtered Nb–Sn films with an Sn content of 32–42 at. % on the beam tubes and equator positions. Several annealing conditions were investigated to improve the surface homogeneity of the Nb 3 Sn film. The best cosputtered Nb–Sn film was achieved after annealing at 600 °C for 6 h, followed by annealing at 950 °C for 1 h. The best process was applied to a Nb cavity, which was RF tested in a cryogenic dewar. RF testing of the Nb 3 Sn-coated cavity demonstrated a superconducting transition temperature of 17.78 K. The Nb 3 Sn cavity underwent light Sn recoating, followed by additional RF testing, resulting in the enhancement of the RF performance, primarily due to the improved surface homogeneity of the Nb 3 Sn coating.

Nb3Sn film↗

DOE OSTI2024

Deriving the Infrared Complex Refractive Indices of Organic Powders for Optical Modeling: Comparison of Methods

Identification of solids via infrared reflection spectroscopy requires a spectral library of all solids likely to be encountered. A confounding factor in populating such a spectral library is that the reflectance spectra of solids vary with their form, including particle size, film thickness, and substrate. To reduce the efforts of experimentally constructing such a library, an alternate strategy is to use the wavelength-dependent optical constants, n and k, of a solid to calculate a series of reflectance spectra corresponding to each scenario or morphology. Because most n/k measurements are best performed on mm-sized crystals, however, the challenge of determining the optical constants increases when a solid is only readily available as a powder, as is often the case. Some organic solids, such as caffeine, are both unavailable in large crystals and difficult to press into pellets. In this study, the infrared optical constants, or complex refractive indices, of caffeine were determined using three different methods: single-angle reflectance, infrared spectroscopic ellipsometry, and quantitative absorbance measurements of KBr pellets. The n and k values derived through each method were used to model the hyperspectral imaging reflectance spectrum of a caffeine film on a steel planchet. Over 1,110 – 870 cm-1, the single-angle reflectance-derived n and k had the best correlation with the experimental spectrum. These results suggest different organic solids may require different methods to determine the most accurate infrared complex refractive indices for synthetic spectral libraries.

FTIR, hyperspectral imaging, infrared, infrared (I↗

DOE OSTI2020

Combining scaling relationships overcomes rate vs. overpotential tradeoffs in O2 molecular electrocatalysis

The development of advanced chemical-to-electrical energy conversions requires fast and efficient electrocatalysis of multi-electron/multi-proton reactions, such as the oxygen reduction reaction 10 (ORR). Using molecular catalysts, correlations between the reaction rate and energy efficiency have recently been identified. Improved catalysis requires circumventing the rate vs. overpotential tradeoffs implied by such ‘scaling relationships.’ Described here is an ORR system—using a soluble iron-porphyrin and weak acids—with the best reported combination of rate and efficiency for a soluble ORR catalyst. This advance is achieved not by “breaking” scaling relationships but 15 rather by combining two of them. Key to this behavior is a polycationic ligand, which enhances anionic ligand binding and changes the catalyst E1/2. These results show how combining scaling relationships is powerful way towards improved electrocatalysis.

Martin, Daniel J.↗

DOE OSTI2020

Combining Random Mutagenesis and Metabolic Engineering for Enhanced Tryptophan Production in Synechocystis sp. Strain PCC 6803

Tryptophan (Trp) is an essential aromatic amino acid that has value as an animal feed supplement, as the amount found in plant-based sources is insufficient. An alternative to production by engineered microbial fermentation is to have tryptophan biosynthesized by a photosynthetic microorganism that could replace or supplement both the plant and industrially used microbes. We selected Synechocystis sp. strain PCC 6803, a model cyanobacterium, as the host and studied metabolic engineering and random mutagenesis approaches. Previous work on engineering heterotrophic microbes for improved Trp titers has targeted allosteric feedback regulation in enzymes 3-deoxy-d-arabinoheptulosonate 7-phosphate synthase (DAHPS) and anthranilate synthase (AS) as major bottlenecks in the shikimate pathway. In this work, the genes encoding feedback-resistant enzymes from Escherichia coli, aroG fbr and trpE fbr , were overexpressed in the host wild-type (WT) strain. Separately, the WT strain was subjected to random mutagenesis and selection using an amino acid analog to isolate tryptophan-overproducing strains. The randomly mutagenized strains were sequenced in order to identify the mutations that resulted in the desirable phenotypes. Interestingly, the tryptophan overproducers had mutations in the gene encoding chorismate mutase (CM), which catalyzes the conversion of chorismate to prephenate. The best tryptophan overproducer from random mutagenesis was selected as a host for metabolic engineering where aroG fbr and trpE fbr were overexpressed. The best strain developed produced 212 ± 23 mg/liter of tryptophan after 10 days of photoautotrophic growth under 3% (vol/vol) CO2. We demonstrated that a combination of random mutagenesis and metabolic engineering was superior to either individual approach.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA’s GenomeTrakr network

ABSTRACT Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA‘s genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject,PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA’s GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA. IMPORTANCE This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA’s laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Microbiology↗

DOE OSTI2019

A Comparison of Classical and Aggregation-Based Algebraic Multigrid Preconditioners for High-Fidelity Simulation of Wind Turbine Incompressible Flows

This paper presents a comparison of parallel strong scaling performance of classical and aggregation algebraic multigrid (AMG) preconditioners in the context of wind turbine simulations. Fluid motion is governed by the incompressible Navier--Stokes equations, discretized in space with control-volume finite elements and in time with an inexact projection scheme using an implicit integrator. A discontinuous-Galerkin sliding-mesh algorithm captures rotor motion. The momentum equations are solved with iterative Krylov methods, preconditioned by symmetric Gauss--Seidel (SGS) in Trilinos and $\ell_1$ SGS in hypre. The mass-continuity equation is solved with GMRES preconditioned by AMG and can account for the majority of simulation time. Reducing this continuity solve time is crucial. Wind turbine simulations present two unique challenges for AMG preconditioned solvers: the computational meshes include strongly anisotropic elements, and mesh motion requires matrix reinitialization and computation of preconditioners at each time step. Detailed timing profiles are presented and analyzed, and best practices are discussed for both classical and aggregation-based AMG. Results are presented for simulations of two different wind turbines with up to 6 billion grid points on two different computer architectures. For moving mesh problems that require linear-system reinitialization, the well-established strategy of amortizing preconditioner setup costs over a large number of time steps to reduce the solve time is no longer valid. Instead, results show that faster time to solution is achieved by reducing preconditioner setup costs at the expense of linear-system solve costs. Standard smoothed aggregation with Chebyshev relaxation was found to perform poorly when compared with classical AMG in terms of solve time and robustness. However, plain aggregation was comparable to classical AMG.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Ratio method for estimating uncertainty in calculated gamma cascades

The assessment of uncertainty on deduced quantities obtained through both measurement and modeling must include contributions from both components. There are several methods to estimate the uncertainty due to modeling, such as the parametric uncertainty and that stemming from model bias. However, in the case where experimental data exists for partial cross sections, such as discrete gammas emitted in the de-excitation of the product nucleus following the reaction, the discrepancy between the measured and modeled gamma cascades provides more information and allows for uncertainty estimation that can account for all types of model and data uncertainty. This work presents a method for estimating that uncertainty, using ratios of gammas to get a measure of the accuracy of different parts of the modeled gamma cascade. The gamma with the lowest intensity uncertainty is shown to be the best for determining the channel cross section with realistic uncertainties, indicating that it should be used rather than the most intense gamma or a sum of gammas. In conclusion, this method provides both a simple procedure for calculating realistic uncertainties and identifies the best gamma for use in converting a set of measured partial gamma cross sections to the deduced total channel cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Determination of asymptotic normalization coefficients for the channel 16 O→ α+ 12 C: excited state 16 O(0 + ; 6.05 MeV)

Asymptotic normalization coefficients (ANC) determine the overall normalization of cross sections of peripheral radiative capture reactions. In the present paper, we treat the ANC C for the virtual decay 16 O(0 + ; 6.05 MeV)→ α+ 12 C(g.s.), the known values of which are characterized by a large spread (0.29 - 1.65) × 10 3 fm -1/2 . The ANC C is found by analytic continuation in energy of the α 12 C s-wave scattering amplitude, known from the phase-shift analysis of experimental data, to the pole corresponding to the 16O bound state and lying in the unphysical region of negative energies. To determine C, two different methods of analytic continuation are used. In the first method, the scattering data are approximated by the sum of polynomials in energy in the physical region and then extrapolated to the pole. The best way of extrapolation is chosen on the basis of the exactly solvable model. Within the second approach, the ANC C is found by solving the Schrödinger equation for the two-body α 12 C potential, the parameters of which are selected from the requirement of the best description of the phase-shift analysis data at a fixed experimental binding energy of 16 O(0 + ; 6.05 MeV) in the α+ 12 C channel. The values of the ANC C obtained within these two methods lie in the interval (886–1139) fm -1/2 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters

Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. Processing a large volume of the pathology reports in a timely manner is a continuing challenge for cancer registries. The purpose of this study is to develop an information extraction pipeline to reliably and efficiently extract reportable information. Method: We have developed a novel inverse-regression (IR) based information extraction pipeline. The inverse-regression based supervised filter has been successfully applied to many application domains. However, its application to the information extraction from unstructured text is hindered primarily by the extreme high-dimensionality of n-gram representations of text. In this study, we attempt to overcome the obstacles by a novel bootstrapping strategy. First, we use an information-theoretic mutual information based filter to discard the excessive and redundant n-gram features. This step reduces the size and improves the condition number of the sample covariance matrix, thus reducing the computational cost and improving the numerical stability of the subsequent inverse-regression step. Then we use localized sliced inverse-regression (LSIR) to learn a low-dimensional discriminatory subspace for information inference. In particular, we use the k-nearest neighbors of an unlabeled pathology report in the learned representation to infer the desired information from the labeled data in a supervised manner. Results: The experiments were conducted on a set of de-identified pathology reports with human expert labels as the ground truth. Our pipeline consistently performed better than or comparable to the best performing state-of-the-art methods while reducing the training and inference times substantially. Conclusion: Our results demonstrate the potential of inverse-regression based information extraction pipeline for reliable and efficient information extraction from unstructured text. The information extracted from the pathology reports can be used along with clinical information, medical imaging, and genomic information to instigate discoveries in cancer research.

Dubey, Abhishek↗

DOE OSTI2021

MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation

Neuromorphic computing is emerging as a promising Beyond Moore computing paradigm that employs event-triggered computation and non-von Neumann hardware. Spike Timing Dependent Plasticity (STDP) is a well-known bio-inspired learning rule that relies on activities of locally connected neurons to adjust the weights of their respective synapses. In this work, we analyze a basic STDP rule and its sensitivity on the different hyperparameters for training spiking neural networks (SNNs) with supervision, customized for a neuromorphic hardware implementation with integer weights. We compare the classification performance on four UCI datasets (iris, wine, breast cancer and digits) that depict varying levels of complexity. We perform a search for optimal set of hyperparameters using both grid search and Bayesian optimization. Through the use of Bayesian optimization, we show the general trends in hyperparameter sensitivity in SNN classification problem. With the best sets of hyperparameters, we achieve accuracies comparable to some of the best performing SNNs on these four datasets. With a highly optimized supervised STDP rule we show that these accuracies can be achieved with just 20 epochs of training.

Kulkarni, Shruti↗

DOE OSTI2023

Mastering HPC Runtime Prediction: From Observing Patterns to a Methodological Approach

The continual expansion of high-performance computing (HPC) brings with it an increasing need for efficiency. Heavy investment in energy, hardware, and software infrastructure to support peta- and exascale computing requires the optimization of existing systems and, wherever possible, the discernment and adoption of best-practices towards these goals. Such is the case for runtime prediction. When a job is submitted to an HPC system, an estimate of its runtime is provided by the user in the form of "requested wallclock". Error in this user-provided estimate can lead to jobs being prematurely killed by the scheduler, increased wait time on the queue, and decreased system utilization. More than fifteen years of research has been directed at mitigating these effects by using data-driven runtime predictions. Codified here is a set of commonalities and insights emerging from this body of work, which we present as recommendations and best practices. These practices are combined into a methodological approach described and evaluated on an 11-million-job dataset from the National Renewable Energy Laboratory's petascale HPC system, Eagle. This dataset and the accompanying codebase have been released to the public domain for the benefit of the wider HPC research community.

high performance computing↗