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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Identification of bottlenecks in the accumulation of cyclic fatty acids in camelina seed oil

Modified fatty acids (mFA) have diverse uses, e.g., cyclopropane fatty acids (CPA) are feedstocks for producing coatings, lubricants, plastics, and cosmetics. The expression of mFA-producing enzymes in crop and model plants generally results in lower levels of mFA accumulation than in their natural-occurring source plants. In order to further our understanding of metabolic bottlenecks that limit mFA accumulation, we generated transgenic Camelina sativa lines co-expressing Escherichia coli cyclopropane synthase (EcCPS) and Sterculia foetida lysophosphatidic acid acyltransferase (SfLPAT). In contrast to transgenic CPA-accumulating Arabidopsis, CPA accumulation in camelina caused only minor changes in seed weight, germination rate, oil accumulation, and seedling development. CPA accumulated to much higher levels in membrane than storage lipids, comprising more than 60% of total fatty acid in both phosphatidylcholine (PC) and phosphatidylethanolamine (PE) versus 26% in diacylglycerol (DAG) and 12% in triacylglycerol (TAG) indicating bottlenecks in the transfer of CPA from PC to DAG and from DAG to TAG. Upon coexpression of SfLPAT with EcCPS, di-CPA-PC increased by ~50% relative to lines expressing EcCPS alone with the di-CPA-PC primarily observed in the embryonic axis and mono-CPA-PC primarily in cotyledon tissue. EcCPS-SfLPAT lines revealed a redistribution of CPA from the sn-1 to sn-2 positions within PC and PE that was associated with a doubling of CPA accumulation in both DAG and TAG. Finally, the identification of metabolic bottlenecks in acyl transfer between site of synthesis (phospholipids) and deposition in storage oils (TAGs) lays the foundation for the optimizing CPA accumulation through directed engineering of oil synthesis in target crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.

36 MATERIALS SCIENCE↗
DOE OSTI2019

High-Frequency Acoustic Mode Identification of Unstable Combustors

Abstract High frequency thermoacoustic instabilities are becoming increasingly problematic in modern combustion systems. Understanding which acoustic mode is being excited is important for understanding potential mechanisms and control approaches — for example, influence of a helical shear layer mode on the flame has profoundly different effects on the first tangential acoustic mode, than a higher order axial mode of similar frequency. Nonetheless, the modal density increases with frequency and it becomes increasingly difficult to determine which acoustic mode is self-excited, based upon frequency calculations alone. Moreover, access issues and cost usually limit the number of pressure probes that can be distributed axially and azimuthally in the combustor. This paper presents a methodology for identifying the acoustic mode by using high temperature pressure transducers flush mounted in a combustion chamber. Modal identification is demonstrated with a siren under non-reacting conditions. The siren is mounted on the chamber to excite longitudinal and azimuthal waves. Five acoustic sensors at different axial and azimuthal locations measure the pressure fluctuations simultaneously. Given the forcing frequency and the speed of sound, the pressure distribution in the combustor is reconstructed in the time domain from the measured data by using a least squares method to determine its mode shapes. In addition, the finite element method (FEM) solver is used to provide the eigenfrequencies and corresponding mode shapes. The test results demonstrate that the mode shapes from the reconstructed data and corresponding frequencies are consistent with those predicted from the FEM, which validates the methodology in this study. In addition, the methodology is extended to practical reacting cases without the siren to determine the acoustic mode shapes of naturally occurring instabilities. In these cases, the modal features have strong stochastic features, such as what appear to be stochastic variations in overall amplitude and relative amplitudes of clockwise and counterclockwise waves.

Kim, J.↗
DOE OSTI2023

Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.

composites↗
DOE OSTI2019

Clutter Identification based on sparse recovery with dynamically changing dictionary sizes for cognitive radar

Existing radar algorithms assume stationary statistical characteristics for environment/clutter. In practical scenarios, the statistical characteristics of the clutter can dynamically change depending on where the radar is operating. Non-stationarity in the statistical characteristics of the clutter may negatively affect the radar performance. Cognitive radar that can sense the changes in the clutter statistics, learn the new statistical characteristics, and adapt to these changes has been proposed to overcome these shortcomings. We have recently developed techniques for detection of statistical changes and learning the new clutter distribution for cognitive radar. In this work, we will extend the learning component. More specifically, in our previous work, we have developed a sparse recovery based clutter distribution identification to learn the distribution of the new clutter characteristics after the detected change in the statistics of the clutter. In our method, we have built a dictionary of clutter distributions and used this dictionary in orthogonal matching pursuit to perform sparse recovery of the clutter distribution assuming that the dictionary includes the new distribution. In this work, we propose a hypothesis testing based approach to detect whether the new distribution of the clutter is included in the dictionary or not, and suggest a method to dynamically update the dictionary. We envision that the successful outcomes of this work will be of high relevance to the adaptive learning and cognitive augmentation of the radar systems that are used in remotely piloted vehicles for surveillance and reconnaissance operations.

Zhu, Yuansheng↗
DOE OSTI2015

SU-E-J-149: Secondary Emission Detection for Improved Proton Relative Stopping Power Identification

Purpose: This research investigates application of secondary prompt gamma (PG) emission spectra, resulting from nuclear reactions induced by protons, to characterize tissue composition along the particle path. The objective of utilizing the intensity of discrete high-energy peaks of PG is to improve the accuracy of relative stopping power (RSP) values available for proton therapy treatment planning on a patient specific basis and to reduce uncertainty in dose depth calculations. Methods: In this research, MCNP6 was used to simulate PG emission spectra generated from proton induced nuclear reactions in medium of varying composition of carbon, oxygen, calcium and nitrogen, the predominant elements found in human tissue. The relative peak intensities at discrete energies predicted by MCNP6 were compared to the corresponding atomic composition of the medium. Results: The results have shown a good general agreement with experimentally measured values reported by other investigators. Unexpected divergence from experimental spectra was noted in the peak intensities for some cases depending on the source of the cross-section data when using compiled proton table libraries vs. physics models built into MCNP6. While the use of proton cross-section libraries is generally recommended when available, these libraries lack data for several less abundant isotopes. This limits the range of their applicability and forces the simulations to rely on physics models for reactions with natural atomic compositions. Conclusion: Current end-of-range proton imaging provides an average RSP for the total estimated track length. The accurate identification of tissue composition along the incident particle path using PG detection and characterization allows for improved determination of the tissue RSP on the local level. While this would allow for more accurate depth calculations resulting in tighter treatment margins, precise understanding of proton beam behavior in tissue of various compositions is necessary requiring detailed simulations with a high degree of accuracy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-FG-204-06: A New Algorithm for Gold Nano-Particle Concentration Identification in Dual Energy CT

Purpose: Gold nano-particle (GNP) has recently attracted a lot of attentions due to its potential as an imaging contrast agent and radiotherapy sensitiser. Imaging the GNP at its low contraction is a challenging problem. We propose a new algorithm to improve the identification of GNP based on dual energy CT (DECT). Methods: We consider three base materials: water, bone, and gold. Determining three density images from two images in DECT is an under-determined problem. We propose to solve this problem by exploring image domain sparsity via an optimization approach. The objective function contains four terms. A data-fidelity term ensures the fidelity between the identified material densities and the DECT images, while the other three terms enforces the sparsity in the gradient domain of the three images corresponding to the density of the base materials by using total variation (TV) regularization. A primal-dual algorithm is applied to solve the proposed optimization problem. We have performed simulation studies to test this model. Results: Our digital phantom in the tests contains water, bone regions and gold inserts of different sizes and densities. The gold inserts contain mixed material consisting of water with 1g/cm3 and gold at a certain density. At a low gold density of 0.0008 g/cm3, the insert is hardly visible in DECT images, especially for those with small sizes. Our algorithm is able to decompose the DECT into three density images. Those gold inserts at a low density can be clearly visualized in the density image. Conclusion: We have developed a new algorithm to decompose DECT images into three different material density images, in particular, to retrieve density of gold. Numerical studies showed promising results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy

Purpose: To automatically identify and outline suspicious regions of recurrent or residual disease in the prostate bed using Dynamic Contrast Enhanced-MRI (DCE-MRI) in patients after prostatectomy. Methods: Twenty-two patients presenting for salvage radiotherapy and with identified Gross Tumor Volume (GTV) in the prostate bed were retrospectively analyzed. The MRI data consisted of Axial T2weighted-MRI (T2w) of the pelvis: resolution 1.25×1.25×2.5 mm; Field of View (FOV): 320×320 mm; slice thickness=2.5mm; 72 slices; and Dynamic Contrast Enhanced MRI (DCE-MRI)–12 series of T1w with identical spatial resolution to T2w and at 30–34s temporal resolution. Unsupervised pattern recognition was used to decompose the 4D DCE data as the product W.H of weights W of k patterns H. A well-perfused pattern Hwp was identified and the weight map Wwp associated to Hwp was used to delineate suspicious volumes. Threshold of Wwp set at mean(Wwp)+S*std(Wwp), S=1,1.5,2 and 2.5 defined four volumes labeled as DCE1.0 to DCE2.5. These volumes were displayed on T2w and, along with GTV, were correlated with the highest pre-treatment PSA values, and with pharmacokinetic analysis constants. Results: GTV was significantly correlated with DCE2.0(ρ= 0.60, p<0.003), and DCE 2.5 (ρ=0.58, p=0.004)). Significant correlation was found between highest pre-treatment PSA and GTV(ρ=0.42, p<0.049), DCE2.0(ρ= 0.52, p<0.012), and DCE 2.5 (ρ=0.67, p<<0.01)). Kruskal-Wallis analysis showed that Ktrans median value was statistically different between non-specific prostate bed tissue NSPBT and both GTV (p<<0.001) and DCE2.5 (p<<0.001), but while median Ve was statistically different between DCE2.5 and NSPBT (p=0.002), it was not statistically different between GTV and NSPBT (p=0.054), suggesting that automatic volumes capture more accurately the area of malignancy. Conclusion: Software developed for identification and visualization of suspicions regions in DCE-MRI from post-prostatectomy patients has been validated by PSA and pharmacokinetic constants analysis showing that it generates clinically relevant volumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

SINGLE: Atomic-resolution structure identification of nanocrystals by graphene liquid cell EM

Analysis of the three-dimensional (3D) structures of nanocrystals with solution-phase transmission electron microscopy is beginning to reveal their unique physiochemical properties. We developed a “one-particle Brownian 3D reconstruction method” based on imaging of ensembles of colloidal nanocrystals using graphene liquid cell electron microscopy. Projection images of differently rotated nanocrystals are acquired using a direct electron detector with high temporal (<2.5 ms) resolution and analyzed to obtain an ensemble of 3D reconstructions. Here, we introduce computational methods required for successful atomic-resolution 3D reconstruction: (i) tracking of the individual particles throughout the time series, (ii) subtraction of the interfering background of the graphene liquid cell, (iii) identification and rejection of low-quality images, and (iv) tailored strategies for 2D/3D alignment and averaging that differ from those used in biological cryo–electron microscopy. Our developments are made available through the open-source software package SINGLE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-throughput identification of spin-photon interfaces in silicon

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: Ti$^+_i$, Fe$^0_i$, and Ru$^0_i$. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Identification of proteins influencing CRISPR-associated transposases for enhanced genome editing

CRISPR-associated transposases (CASTs) hold tremendous potential for microbial genome editing because of their ability to integrate large DNA cargos in a programmable, site-specific manner. However, their widespread application has been hindered by poorly understood host factor requirements for transposition. To address this gap, we conducted the first genome-wide screen for host factors affecting Vibrio cholerae CAST (VchCAST) activity using an Escherichia coli RB-TnSeq library and identified 15 genes affecting VchCAST transposition. Of these, seven factors were validated to improve VchCAST activity, and two were inhibitory. Guided by the identification of homologous recombination effectors, RecD and RecA, we tested the λ-Red recombineering system in our VchCAST editing vectors and increased editing efficiency by 55.2-fold in E. coli, 5.6-fold in Pseudomonas putida, and 10.8-fold in Klebsiella michiganensis while maintaining high target specificity and similar insertion arrangements. This study improves the understanding of factors affecting VchCAST activity and enhances its efficiency as a bacterial genome editor.

Song, Leo C T↗
DOE OSTI2017

Identification of Glutaminyl Cyclase Genes Involved in Pyroglutamate Modification of Fungal Lignocellulolytic Enzymes

The breakdown of plant biomass to simple sugars is essential for the production of second-generation biofuels and high-value bioproducts. Currently, enzymes produced from filamentous fungi are used for deconstructing plant cell wall polysaccharides into fermentable sugars for biorefinery applications. A post-translational N-terminal pyroglutamate modification observed in some of these enzymes occurs when N-terminal glutamine or glutamate is cyclized to form a five-membered ring. This modification has been shown to confer resistance to thermal denaturation for CBH-1 and EG-1 cellulases. In mammalian cells, the formation of pyroglutamate is catalyzed by glutaminyl cyclases. Using the model filamentous fungus Neurospora crassa , we identified two genes ( qc-1 and qc-2 ) that encode proteins homologous to mammalian glutaminyl cyclases. We show that qc-1 and qc-2 are essential for catalyzing the formation of an N-terminal pyroglutamate on CBH-1 and GH5-1. CBH-1 and GH5-1 produced in a Δqc-1 Δqc-2 mutant, and thus lacking the N-terminal pyroglutamate modification, showed greater sensitivity to thermal denaturation, and for GH5-1, susceptibility to proteolytic cleavage. QC-1 and QC-2 are endoplasmic reticulum (ER)-localized proteins. The pyroglutamate modification is predicted to occur in a number of additional fungal proteins that have diverse functions. The identification of glutaminyl cyclases in fungi may have implications for production of lignocellulolytic enzymes, heterologous expression, and biotechnological applications revolving around protein stability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genome-Wide Identification of Transcription Start Sites in Two Alphaproteobacteria , Rhodobacter sphaeroides 2.4.1 and Novosphingobium aromaticivorans DSM 12444

Here, we report the genome-wide identification of transcription start sites (TSSs) from two Alphaproteobacteria grown under conditions that result in significant changes in gene expression. TSSs that were identified as present in one condition or both will be an important resource for future studies of these, and possibly other, Alphaproteobacteria .

Myers, Kevin S.↗
DOE OSTI2018

Identification of Absorption Bands of Monomers and Aggregates in a Layer of Cyanine Dye and Determination of the Orientation of Molecules

The problem of allocating bands of individual monomers or aggregates of the absorption spectrum of a multicomponent structure arises in the study of molecular layers or organic thin films. Not counting the concentrations of the components, the optical properties of this kind of samples are determined by the tilt of the molecules relative to the substrate surface. Molecular orientation can be determined from the dichroic ratio spectra recorded with the tilt probing of samples. Using the example of the tricarbocyanine dye layers, the following two independent methods for the identification of the spectra of the components from the absorption spectrum of a layer are compared: by comparing the spectra of layers of different thicknesses and based on the dichroic ratio spectra. The effect of laser radiation on the samples, which leads to a change in the dichroic ratio, is studied. Photoinduced changes of the orientation angles of molecules are calculated, which allows one to refine the range of possible values of these angles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Some challenging points in the identification of defects in floating-zone n-type silicon irradiated with 8 and 15 MeV protons

Electrical properties of defects formed in n-Si(FZ) following 8 and 15 MeV proton irradiation are investigated by Hall effect measurements over the wide temperature range of T ≈ 25 to 300 K. Close attention is paid to the damaging factor of proton irradiation, leaving aside passivation effects by hydrogen. The concept of defect production and annealing processes being accepted in the literature so far needs to be reconsidered. Contrary to expectations the dominant impurity-related defects produced by MeV protons turn out to be electrically neutral in n-type material. Surprisingly, radiation acceptors appear to play a minor role. Annealing studies of irradiated samples of such complex defects as a divacancy tied to a phosphorus atom and a vacancy tied to two phosphorus atoms. The latter defect features high thermal stability. Identification of the dominant neutral donors, however, remains unclear and will require further, more detailed, studies. The electric properties of the material after proton irradiation can be completely restored at T = 800°C.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Identification of stress biomarkers for drought and increased soil temperature in seedlings of European beech ( Fagus sylvatica )

Drought is an environmental stress that impacts plant productivity. Projections show both an increase in intense rain events and a reduction in the number of rain days, conditions that leads to increased risk of drought. Consequently, the identification of molecular biomarkers suitable for evaluating the impact of water deprivation conditions on forest plant seedlings is of significant value for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of drought stress coupled with variable soil temperature in European beech (Fagus sylvatica L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the specific activities and isoform profile of superoxide dismutases and glutathione peroxidases together with the variation of phenolic compounds enable discrimination between seedlings with different degrees of photosynthetic activity. This approach represents a promising platform for the assessment of drought stress in forest trees and could serve for enhancing selection and breeding practices, allowing for plants that are more tolerant of abiotic stress.

beach↗
DOE OSTI2020

JEDI-net: a jet identification algorithm based on interaction networks

Abstract We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Characterization of the scintillation response of water-based liquid scintillator to alpha particles, and implications for particle identification

Next-generation large-scale neutrino detectors, from EOS, at the 1 t scale, to THEIA, at the 10 s-of-kt scale, will utilize differences in both the scintillation and Cherenkov light emission for different particle species to perform background rejection. This manuscript presents measurements of the scintillation light yield and emission time profile of water-based liquid scintillator samples in response to α radiation. These measurements are used as input to simulation models used to make predictions for future detectors. In particular, we present the timing-based particle identification achievable in generic water-based scintillator detectors at the 4 t, 1 kt, and 100 kt scales. We find that α/β discrimination improves with increasing scintillation concentration and we identify better than 80% α rejection for 90% β acceptance in 10% water-based liquid scintillator, at the 4 t scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗