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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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ISE-ammonium sensors subject to fouling display an increase in response time and an inexorable degradation of the measurement accuracy. The fouling morphology and composition were also studied by EDX analysis.
Behind-the-meter storage (BTMS) systems - a viable method to minimize potential risk of blackout events and stabilize the grid - require a different type of cost-effective energy storage with excellent safety, ultra-long (>20 years) cycle life and reasonable energy density compared that of electric vehicles. To increase the energy density and reduce the cost of a long-term cyclable lithium-titanate-based cell, it is required to employ a critical-material-free high voltage cathode and an electrolyte with good electrochemical and transport properties. In this report the long-term electrochemical performance and behaviors of selected critical-material-free Li 4 Ti 5 O 12 (LTO)//LiNi 0.9 Mn 0.1 O 2 (LNMO) full cells for BTMS applications are evaluated and analyzed in the optimized voltage range of 1.4-2.7 V at 45 degrees C with different fluorinated carbonate-based electrolytes. The fluoroethylene carbonate (FEC)-based electrolyte cell shows the highest capacity retention of 57.9% and Coulombic efficiency (CE) of 99.96% after 1000 cycles, potentially attributed to a dense, homogenous and less resistive LiF-rich solid-electrolyte interphase (SEI) layer formed on the surface of LTO that may mitigate electrolyte decomposition and maintain relatively low cell impedance during cycling. The 3,3,3-fluoroethylmethyl carbonate (F-EMC)-based electrolyte cell, however, presents the worst performance with lower capacity and a sharp decrease of CE, due to unstable and non-uniform SEI formation and continuous oxidative electrolyte decomposition. This mechanistic understanding of cell aging behaviors and failure mechanisms with detailed analysis of surface chemistry and electrode morphology can guide design of new electrode chemistries and electrolyte formulations for the development of BTMS batteries.
While low-temperature combustion (LTC) strategies have been found to mitigate nitrogen oxides and particulate matter emissions in diesel engines, studies have also associated LTC with an increase in unburnt hydrocarbons. With more recent studies on diesel after end-of-injection (AEOI), combustion recession is identified as a phenomenon where at near nozzle region, high-temperature ignition (HTI) combustion can propagate back to the nozzle tip consuming the unburnt hydrocarbons AEOI. Current literature has suggested that combustion recession is controlled by auto-ignition. However, high-fidelity simulations and detailed analysis of such a mechanism are missing. In this study, comprehensive Large Eddy Simulations of a reacting spray at “Spray A” conditions are performed, where detailed analysis of combustion recession concerning flame morphology and propagation modes are included. In particular, this study demonstrated for the first time that while combustion recession is mainly auto-ignition dominated (consistent with the literature), a cool flame was found to deflagrate towards the richer regions of the mixture, promoting mixing and increasing the mixture temperature. This leads to HTI kernels, which then grow and develop as deflagrative waves, therefore sustaining the combustion recession process. The study also detailed the extinction mechanism of combustion: the entrainment wave will overlean the near-nozzle mixtures, rendering it unable to support HTI, which leads to the extinction of the upstream flame AEOI in lower reactivity mixtures. Combustion recession is also observed to be contingent on the chemical and diffusion processes, even at low scalar dissipation rates. Finally, a new criterion for combustion recession based on chemical explosive mode is proposed and validated with previous combustion recession index to quantify the extent of HTI in near-nozzle mixtures AEOI. The newly developed metric combined with a previous experimentally-based metric can provide simple but valuable measurements of the degree and propensity of the upstream flame AEOI.
Converting CO 2 into solid carbon materials provides both long-term carbon sequestration and the production of value-added products. Here, this Comment compares reactor configurations guided by thermodynamic analysis, evaluates the market volume and morphology control of different carbon products, and identifies hurdles and opportunities for scaled-up deployment.
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Great strides have been made in improving the quality of x-ray radiographs in high energy density plasma experiments, enabled in part by innovations in engineering and manufacturing of integrated circuits and materials. As a consequence, the radiographs of today are filled with a great deal of detail, but few of these features are extracted in a systematic way. Analysis techniques familiar to plasma physicists tend toward brittle 1D lineout or Fourier transform type analyses. The techniques applied to process our data have not kept pace with improvements in the quality of our data. Fortunately, the field of computer vision has a wealth of tools to offer, which have been widely used in industrial imaging and, more recently, adopted in biological imaging. We demonstrate the application of computer vision techniques to the analysis of x-ray radiographs from high energy density plasma experiments, as well as give a brief tutorial on the computer vision techniques themselves. These tools robustly extract 2D contours of shocks, boundaries of inhomogeneities, and secondary flows, thereby allowing for increased automation of analysis, as well as direct and quantitative comparisons with simulations.
ABSTRACT We recall evidence that long gamma-ray bursts (GRBs) have binary progenitors and give new examples. Binary-driven hypernovae (BdHNe) consist of a carbon–oxygen core (COcore) and a neutron star (NS) companion. For binary periods ∼5 min, the COcore collapse originates the subclass BdHN I characterized by (1) an outstanding supernova (SN; the ‘SN-rise’); (2) a black hole (BH), born from the NS collapse by SN matter accretion, leading to a GeV emission with luminosity $L_{\rm GeV} = A_{\rm GeV}\, t^{-\alpha _{\rm GeV}}$, observed only in some cases; and (3) a new NS (νNS), born from the SN, originating from the X-ray afterglow with $L_\mathrm{ X} = A_{\rm X}\, t^{-\alpha _{\rm X}}$, observed in all BdHN I. We record 378 sources and present for four prototype GRBs 130427A, 160509A, 180720B, and 190114C: (1) spectra, luminosities, SN-rise duration; (2) AX, αX = 1.48 ± 0.32, and (3) the νNS spin time evolution. We infer (i) AGeV, αGeV = 1.19 ± 0.04 and (ii) the BdHN I morphology from time-resolved spectral analysis, three-dimensional simulations, and the GeV emission presence/absence in 54 sources within the Fermi-Large Area Telescope boresight angle. For 25 sources, we give the integrated and time-varying GeV emission, 29 sources have no GeV emission detected and show X/gamma-ray flares previously inferred as observed along the binary plane. The 25/54 ratio implies the GeV radiation is emitted within a cone of half-opening angle ≈60° from the normal to the orbital plane. We deduce BH masses of 2.3–8.9 M⊙ and spin of 0.27–0.87 by explaining the GeV emission from the BH rotational energy extraction, while their time evolution validates the BH mass–energy formula.
Abstract not provided.
Carboxysomes are a class of bacterial microcompartments that form proteinaceous organelles within the cytoplasm of cyanobacteria and play a central role in photosynthetic metabolism by defining a cellular microenvironment permissive to CO2 fixation. Critical aspects of the assembly of the carboxysomes remain relatively unknown, especially with regard to the dynamics of this microcompartment. Progress in understanding carboxysome dynamics is impeded in part because analysis of the subtle changes in carboxysome morphology with microscopy remains a low-throughput and subjective process. Here we use deep learning techniques, specifically a Rotationally Invariant Variational Autoencoder (rVAE), to analyze fluorescence microscopy images of cyanobacteria bearing a carboxysome reporter and quantitatively evaluate how carboxysome shell remodelling impacts subtle trends in the morphology of the microcompartment over time. Toward this goal, we use a recently developed tool to control endogenous protein levels, including carboxysomal components, in the model cyanobacterium Synechococcous elongatus PCC 7942. By utilization of this system, proteins that compose the carboxysome can be tuned in real time as a method to examine carboxysome dynamics. We find that rVAEs are able to assist in the quantitative evaluation of changes in carboxysome numbers, shape, and size over time. Further, we propose that rVAEs may be a useful tool to accelerate the analysis of carboxysome assembly and dynamics in response to genetic or environmental perturbation and may be more generally useful to probe regulatory processes involving a broader array of bacterial microcompartments.
Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes.
Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called a Vector Quantizing Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology to attain >85% accuracy in identifying uranium oxide processing routes. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO 2 ) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO 2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO 2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE, then further reduce the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO 2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.
In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.
ABSTRACT Marine fungi remain poorly covered in global genome sequencing campaigns; the 1000 fungal genomes (1KFG) project attempts to shed light on the diversity, ecology and potential industrial use of overlooked and poorly resolved fungal taxa. This study characterizes the genomes of three marine fungi: Emericellopsis sp. TS7, wood-associated Amylocarpus encephaloides and algae-associated Calycina marina. These species were genome sequenced to study their genomic features, biosynthetic potential and phylogenetic placement using multilocus data. Amylocarpus encephaloides and C. marina were placed in the Helotiaceae and Pezizellaceae (Helotiales) , respectively, based on a 15-gene phylogenetic analysis. These two genomes had fewer biosynthetic gene clusters (BGCs) and carbohydrate active enzymes (CAZymes) than Emericellopsis sp. TS7 isolate. Emericellopsis sp. TS7 ( Hypocreales , Ascomycota ) was isolated from the sponge Stelletta normani . A six-gene phylogenetic analysis placed the isolate in the marine Emericellopsis clade and morphological examination confirmed that the isolate represents a new species, which is described here as E. atlantica . Analysis of its CAZyme repertoire and a culturing experiment on three marine and one terrestrial substrates indicated that E. atlantica is a psychrotrophic generalist fungus that is able to degrade several types of marine biomass. FungiSMASH analysis revealed the presence of 35 BGCs including, eight non-ribosomal peptide synthases (NRPSs), six NRPS-like, six polyketide synthases, nine terpenes and six hybrid, mixed or other clusters. Of these BGCs, only five were homologous with characterized BGCs. The presence of unknown BGCs sets and large CAZyme repertoire set stage for further investigations of E. atlantica . The Pezizellaceae genome and the genome of the monotypic Amylocarpus genus represent the first published genomes of filamentous fungi that are restricted in their occurrence to the marine habitat and form thus a valuable resource for the community that can be used in studying ecological adaptions of fungi using comparative genomics.
Here, we report results of dynamical simulations according to an experimentally validated surface morphological evolution model on the formation of regular arrays of quantum dot molecules (QDMs) consisting of 1D arrays of smaller interacting quantum dots (QDs). These QD arrays form along the sides of each pit rim on the surface of a coherently strained thin film epitaxially deposited on a semiconductor substrate, the surface of which consists of a periodic pattern of inverted prismatic pits with rectangular pit openings. We find that this complex QDM pattern results from the decomposition of the pit rim from a “quantum fortress” with four elongated QDs into four 1D arrays of multiple smaller QDs arranged along each side of the pit rim. Systematic parametric analysis indicates that varying the pit opening dimensions and the pit wall inclination directly impacts the number of QDs in the resulting QDM pattern, while varying the pit depth only affects the dimensions of the QDs in the nanostructure pattern. Therefore, the number, arrangement, and sizes of QDs in the resulting pattern of QDMs on the film surface can be engineered precisely by proper tuning of the pit design parameters. Our simulation results are supported by predictions of morphological stability analysis, which explains the pit rim decomposition into multiple QDs as the outcome of a tip-splitting instability and provides a fundamental characterization of the post-instability nanostructure pattern. Our theoretical findings can play a vital role in designing optimal semiconductor surface patterns toward enabling future nanofabrication technologies.
We report a facile method of ordering block copolymer (BCP) morphologies in which the conventional two-step casting and annealing steps are replaced by a single-step process where microphase separation and grain coarsening are seamlessly integrated within the casting protocol. This is achieved by slowing down solvent evaporation during casting by introducing a non-volatile solvent into the BCP casting solution what effectively prolongs the duration of the grain-growth phase. We demonstrate the utility of this solvent evaporation annealing (SEA) method by producing well-ordered large molecular-weight BCP thin films in a total processing time shorter than 3 minutes without resorting to any extra laboratory equipment other than a basic casting-device i.e., spin- or blade-coater. By analyzing the morphologies of the quenched samples, we identify a relatively narrow range of polymer concentration in the wet film, just above the order-disorder concentration, to be critical for obtaining large-grained morphologies. This finding is corroborated by the analysis of the grain-growth kinetics of horizontally-oriented cylindrical domains where relatively large growth exponents (1/2) are observed, indicative of a more rapid defect-annihilation mechanism in the concentrated BCP solution than in thermally-annealed BCP melts. Furthermore, the analysis of temperature-resolved kinetics data allows us to calculate the Arrhenius activation energy of the grain-coarsening in this one-step BCP ordering process.
A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook . Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.
Access to multimaterial polymers with spatially localized properties and robust interfaces is anticipated to enable new capabilities in soft robotics, such as smooth actuation for advanced medical and manufacturing technologies. Here, orthogonal initiation is used to create interpenetrating polymer networks (IPNs) with spatial control over morphology and mechanical properties. Base catalyzes the formation of a stiff and strong polyurethane, while blue LEDs initiate the formation of a soft and elastic polyacrylate. IPN morphology is controlled by when the LED is turned “on”, with large phase separation occurring for short time delays (≈1–2 min) and a mixed morphology for longer time delays (>5 min), which is supported by dynamic mechanical analysis, small angle X-ray scattering, and atomic force microscopy. Through tailoring morphology, tensile moduli and fracture toughness can be tuned across ≈1–2 orders of magnitude. Furthermore, a simple spring model is used to explain the observed mechanical behavior. Photopatterning produces “multimorphic” materials, where morphology is spatially localized with fine precision (<100 µm), while maintaining a uniform chemical composition throughout to mitigate interfacial failure. As a final demonstration, the fabrication of hinges represents a possible use case for multimorphic materials in soft robotics.
Many petroleum-derived plastic materials are highly recalcitrant and persistent in the environment, posing significant threats to human and ecological receptors due to their accumulation in ecosystems. In recent years, research efforts have focused on advancing biological methods for polymer degradation. Enzymatic depolymerization has emerged as particularly relevant for biobased plastic recycling, potentially scalable for industrial use. Biodegradation involves adsorption to the plastic solid surface, followed by an interfacial reaction, resulting in cleavage of bonds of polymer chains exposed on the surface. Here, widely varying substrate-specific kinetics are observed, with the polymer’s properties possessing a significant impact on the rate of this interfacial catalysis. Thus, there is a critical need for sensitive and accurate characterization of the material surface during and after interfacial depolymerization to fully understand the reaction mechanisms. Here, we provide a critical review of a range of techniques used in the analysis of material surfaces to characterize the chemical, topological, and morphological features relevant to the study of enzymatic biocatalysis, including microscopy techniques, spectroscopic techniques (e.g., X-ray diffraction analysis, Fourier transform infrared attenuated total reflectance spectroscopy, and mass spectrometry detection of analytes associated with degradation). Techniques for evaluation of surface energy and topology in their relevancy for sensitive detection of biological surface modifications are also discussed. In addition, this paper provides an overview of the strengths of these techniques and compares their performance in both sensitivity and throughput, including emerging techniques, which can be useful, particularly for the rapid analysis of the surface properties of polymeric materials in high-throughput screening of candidate biocatalysts. This research serves as a starting point in selecting and applying appropriate methodologies that provide direct evidence to the ongoing biotic degradation of polymeric materials.