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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 649 records · Page 36

Mapping of Heterogeneous Catalyst Degradation in Polymer Electrolyte Fuel Cells

Pt catalysts in polymer electrolyte fuel cells degrade heterogeneously as the catalyst particles are exposed to local variations throughout the catalyst layer during operation. State-of-the-art analytical techniques for studying degradation of Pt catalysts do not possess fine spatial resolution to elucidate such non-uniform degradation behavior at a large electrode level. A new methodology is developed to spatially resolve and quantify the heterogeneous Pt catalyst degradation over a large area (several cm ) of aged MEAs based on synchrotron X-ray microdiffraction. PEFC single cells are aged using voltage cycling as an accelerated stress test and the degradation heterogeneity at a micrometer length scale is visualized by mapping Pt catalyst particle size after voltage cycling. It is demonstrated in detail that the Pt catalyst particle size growth is non-uniform and follows the flow field geometry. The Pt particle size growth is greater in the area under the flow field land, while it is minimal in the area under the flow field channel. Additional non-uniformity is observed with the Pt particle size increasing more rapidly at the air outlet area than the Pt particle size at the inlet area.

36 MATERIALS SCIENCE↗

Spontaneous Aerosol Ejection: Origin of Inorganic Particles in Biomass Pyrolysis

At high thermal flux and temperatures of approximately 500 °C, lignocellulosic biomass transforms to a reactive liquid intermediate before evaporating to condensable bio-oil for downstream upgrading to renewable fuels and chemicals. However, the existence of a fraction of nonvolatile compounds in condensed bio-oil diminishes the product quality and, in the case of inorganic materials, catalyzes undesirable aging reactions within bio-oil. In this study, ablative pyrolysis of crystalline cellulose was evaluated, with and without doped calcium, for the generation of inorganic-transporting aerosols by reactive boiling ejection from liquid intermediate cellulose. Further, aerosols were characterized by laser diffraction light scattering, inductively coupled plasma spectroscopy, and high-speed photography. Pyrolysis product fractionation revealed that approximately 3 % of the initial feed (both organic and inorganic) was transported to the gas phase as aerosols. Large bubble-to-aerosol size ratios and visualization of significant late-time ejections in the pyrolyzing cellulose suggest the formation of film bubbles in addition to the previously discovered jet formation mechanism.

09 BIOMASS FUELS↗

Biodegradation in Soil of PLA/PBAT Blends Compatibilized with Chain Extender

This paper presents a study of biodegradation, in soil, of samples of poly(butylene adipate-co-terephthalate)(PBAT), poly(lactic acid) (PLA) and blends of these materials prepared in torque rheometer with the addition of a chain extender. Film samples of these materials were buried in soil under controlled laboratory conditions. The degraded samples were regularly taken from soil and analyzed by visual inspection, size exclusion chromatography, differential scanning calorimetry and infrared spectroscopy. Respirometry biodegradation tests were conducted to assess samples mineralization degree. Blends showed higher degree of crystallinity compared to pure polymers. Crystallinity degree enhanced during the biodegradation process in all samples, being able to causing the samples to degrade slowly. The study showed the great complexity of the biodegradation process of PLA and PBAT blends when compatibilized with a chain extender. The biodegradation rate showed different results depending on the characteristic applied to evaluate it: visual, molecular weight or mineralization. The chain extender had strong influence in PBAT and blends degradation, slowing the process as observed by the variation of molecular weight and carbonyl index. Blends showed an intermediate behavior compared to the original polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Syntheses of Colloidal F:In 2 O 3 Cubes: Fluorine-Induced Faceting and Infrared Plasmonic Response

Cube-shaped nanocrystals (NCs) of conventional metals like gold and silver generally exhibit localized surface plasmon resonance (LSPR) in the visible region with spectral modes determined by their faceted shapes. Yet, faceted NCs exhibiting LSPR response in the infrared (IR) region are relatively rare. Here, we describe the colloidal synthesis of nanoscale fluorine-doped indium oxide (F:In 2 O 3 ) cubes with LSPR response in the IR region, wherein fluorine was discovered to both direct the cubic morphology and act as an aliovalent dopant. Single-crystalline 160 nm F:In 2 O 3 cubes terminated by (100) facets and concave cubes were synthesized using a colloidal heat-up method. The presence of fluorine was found to impart higher stabilization to the (100) facets through density functional theory calculations that evaluated the energetics of F-substitution at surface oxygen sites. These calculations suggest that the cubic morphology results from surface binding of F atoms. In addition, fluorine acts as an anionic aliovalent dopant in the cubic bixbyite lattice of In 2 O 3 , introducing a high concentration of free electrons leading to LSPR. We confirmed the presence of lattice fluorine dopants in these cubes using solid-state 19 F and 115 In nuclear magnetic resonance spectroscopy. The cubes exhibit narrow, shape-dependent multimodal LSPR extinction peaks due to corner- and edge-centered modes. The spatial origin of these different contributions to the spectral response is directly visualized by electron energy loss spectroscopy in a scanning transmission electron microscope.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

ABSTRACT Our Universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work, we develop translation and rotation equivariant normalizing flow (TRENF), a generative normalizing flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise non-linear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On non-linear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries.

79 ASTRONOMY AND ASTROPHYSICS↗

Applications of Micro-Fourier Transform Infrared Spectroscopy (FTIR) in the Geological Sciences—A Review

Fourier transform infrared spectroscopy (FTIR) can provide crucial information on the molecular structure of organic and inorganic components and has been used extensively for chemical characterization of geological samples in the past few decades. In this paper, recent applications of FTIR in the geological sciences are reviewed. Particularly, its use in the characterization of geochemistry and thermal maturation of organic matter in coal and shale is addressed. These investigations demonstrate that the employment of high-resolution micro-FTIR imaging enables visualization and mapping of the distributions of organic matter and minerals on a micrometer scale in geological samples, and promotes an advanced understanding of heterogeneity of organic rich coal and shale. Additionally, micro-FTIR is particularly suitable for in situ, non-destructive characterization of minute microfossils, small fluid and melt inclusions within crystals, and volatiles in glasses and minerals. This technique can also assist in the chemotaxonomic classification of macrofossils such as plant fossils. These features, barely accessible with other analytical techniques, may provide fundamental information on paleoclimate, depositional environment, and the evolution of geological (e.g., volcanic and magmatic) systems.

58 GEOSCIENCES↗

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Chemical and Physical Drivers for Improvement in Permeance and Stability of Linker-Free Graphene Oxide Membranes

Graphene oxide (GO) is a promising membrane material for chemical separations, including water treatment. However, GO has often required postsynthesis chemical modifications, such as linkers or intercalants, to improve either the permeability, performance, or mechanical integrity of GO membranes. In this work, we explore two different feedstocks of GO to investigate chemical and physical differences, where we observe up to a 100× discrepancy in the permeability–mass loading trade-off while maintaining nanofiltration capacity. GO membranes also show structural stability and chemical resilience to harsh pH conditions and bleach treatment. Here we probe GO and the resulting assembled membranes through a variety of characterization approaches, including a novel scanning-transmission-electron-microscopy-based visualization approach, to connect differences in sheet stacking and oxide functional groups to significant improvements in permeability and chemical stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Coherent Image Layout using an Adaptive Visual Vocabulary

When querying a huge image database containing millions of images, the result of the query may still contain many thousands of images that need to be presented to the user. We consider the problem of arranging such a large set of images into a visually coherent layout, one that places similar images next to each other. Image similarity is determined using a bag-of-features model, and the layout is constructed from a hierarchical clustering of the image set by mapping an in-order traversal of the hierarchy tree into a space-filling curve. This layout method provides strong locality guarantees so we are able to quantitatively evaluate performance using standard image retrieval benchmarks. Performance of the bag-of-features method is best when the vocabulary is learned on the image set being clustered. Because learning a large, discriminative vocabulary is a computationally demanding task, we present a novel method for efficiently adapting a generic visual vocabulary to a particular dataset. We evaluate our clustering and vocabulary adaptation methods on a variety of image datasets and show that adapting a generic vocabulary to a particular set of images improves performance on both hierarchical clustering and image retrieval tasks.

Multimedia Analytics, image clustering, content ba↗

Spatiotemporal Visualization and Chemical Identification of the Metal Diffusion Layer at the Electrochemical Interface

The diffusion layer created by transition metal dissolution is ubiquitous at the electrochemical solid-liquid interface and plays a key role in determining electrochemical performance. Tracking the spatiotemporal dynamics of the diffusion layer has remained an unresolved challenge. With spatially resolved synchrotron X-ray fluorescence microscopy and micro-X-ray absorption spectroscopy, we demonstrate the in situ visualization and chemical identification of the dynamic diffusion layer near the electrode surface under electrochemical operating conditions. Finally, our method allows for direct mapping of the reactive electrochemical interface and provides insights into engineering the diffusion layer for improving electrochemical performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Role of nanocellulose in tailoring electroanalytical performance of hybrid nanocellulose/multiwalled carbon nanotube electrodes

Abstract Nanocellulose has emerged as a promising green dispersant for carbon nanotubes (CNTs), and there is an increasing trend in developing nanocellulose/CNT hybrid materials for electrochemical detection of various small molecules. However, there have been very few comprehensive studies investigating the role of nanocellulosic material properties upon the electroanalytical performance of the resultant hybrid electrodes. In this work, we demonstrate the influence of both nanocellulose functionalization and geometry, utilizing sulfated cellulose nanocrystals, sulfated cellulose nanofibers, and TEMPO-oxidized cellulose nanofibers. Transmission electron microscopy tomography enables direct visualization of the effect of nanocellulosic materials on the hybrid architectures. High resolution X-ray absorption spectroscopy verifies that the chemical nature of CNTs in the different hybrids is unmodified. Electroanalytical performances of the different nanocellulose/CNT hybrid electrodes are critically evaluated using physiologically relevant biomolecules with different charge such as, dopamine (cationic), paracetamol (neutral), and uric acid (anionic). The hybrid electrode containing fibrillar nanocellulose geometry with a high degree of sulfate group functionalization provides the highest electroanalytical sensitivity and strongest enrichment towards all studied analytes. These results clearly demonstrate for the first time, the extent of tailorability upon the electroanalytical response of nanocellulose/CNT hybrid electrodes towards different biomolecules, offered simply by the choice of nanocellulosic materials.

Durairaj, Vasuki↗

Theoretical Calculation of the Geometry and Vibrational Frequencies of Triphenylmethane

Raman spectra of polycrystalline triphenylmethane (TPM) are recorded at room temperature in the range 4000–400 cm{sup –1}. The optimum structure and vibrational (Raman and IR) spectra of TPM are calculated using the B3LYP/6-311G(d,p) approximation. Theoretical modes and bands corresponding to them in the experimental Raman spectrum are assigned to stretching and bending vibrations of certain bonds in the TPM molecule based on visualization of the vibrations. Most bands, in particular, the strongest ones in the Raman spectra of TFM, are due to vibrations in monosubstituted benzene rings. Some of the theoretical modes and the corresponding experimental bands are assigned to benzene modes in Wilson notation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Dynamic void distribution in myoglobin and five mutants

Globular proteins contain cavities/voids that play specific roles in controlling protein function. Elongated cavities provide migration channels for the transport of ions and small molecules to the active center of a protein or enzyme. Using Monte Carlo and Molecular Dynamics on fully atomistic protein/water models, a new computational methodology is introduced that takes into account the protein’s dynamic structure and maps all the cavities in and on the surface. To demonstrate its utility, the methodology is applied to study cavity structure in myoglobin and five of its mutants. Computed cavity and channel size distributions reveal significant differences relative to the wild type myoglobin. Computer visualization of the channels leading to the heme center indicates restricted ligand access for the mutants consistent with the existing interpretations. The new methodology provides a quantitative measure of cavity structure and distributions and can become a valuable tool for the structural characterization of proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Transforming Energy through Computational Science: Computing for Clean Energy

This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.

advanced computing↗

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗

Multifunctional skin-like electronics for quantitative, clinical monitoring of cutaneous wound healing

Non-invasive, biomedical devices have the potential to provide important, quantitative data for the assessment of skin diseases and wound healing. Traditional methods either rely on qualitative visual and tactile judgments of a professional and/or data obtained using instrumentation with forms that do not readily allow intimate integration with sensitive skin near a wound site. In this paper, an electronic sensor platform that can softly and reversibly laminate perilesionally at wounds to provide highly accurate, quantitative data of relevance to the management of surgical wound healing is reported. Clinical studies on patients using thermal sensors and actuators in fractal layouts provide precise time-dependent mapping of temperature and thermal conductivity of the skin near the wounds. Analytical and simulation results establish the fundamentals of the sensing modalities, the mechanics of the system, and strategies for optimized design. The use of this type of “epidermal” electronics system in a realistic clinical setting with human subjects establishes a set of practical procedures in disinfection, reuse, and protocols for quantitative measurement. Finally, the results have the potential to address important unmet needs in chronic wound management.

60 APPLIED LIFE SCIENCES↗

Revealing the Phase Separation Behavior of Thermodynamically Immiscible Elements in a Nanoparticle

Phase-separation is commonly observed in multimetallic nanomaterials, yet it is not well understood how immiscible elements distribute in a thermodynamically stable nanoparticle. Herein, we studied the phase-separation of Au and Rh in nanoparticles using electron microscopy and tomography techniques. The nanoparticles were thermally annealed to form thermodynamically stable structures. HAADF-STEM and EDS characterizations reveal that Au and Rh segregate into two domains while their miscibility is increased. Using aberration-corrected HAADF-STEM and atomic electron tomography, we show that the increased solubility of Au in Rh is achieved by forming Au clusters and single atoms inside the Rh domains and on the Rh surface. Furthermore, based on the three-dimensional reconstruction of a AuRh nanoparticle, we can visualize the uneven interface that is embedded in the nanoparticle. Finally, the results advance our understanding on the nanoscale thermodynamic behavior of metal mixtures, which is crucial for the optimization of multimetallic nanostructures for many applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Gravitation and Mesh Adaption

The Gravitation and Mesh Adaptation (GaMA) toolbox consists of a set of Matlab classes for modeling the environment around asteroids and comets. A variety of gravitational models and supporting algorithms from are consolidated alongside a custom meshing utility tailored for the application. This allows the user to work within a single streamlined environment to import and manipulate surface definitions, probe the dynamical environment, integrate trajectories, and post-process results. For other applications, modified surface meshes can be exported. Gravity Models: 1) Werner's analytic polyhedron model 2) Mascon model 3) Gottlieb's spherical harmonic model 4) Approximate polyhedron models 5) Curvilinear surface models 6) Custom composite models Meshing Features: 1) Array-based half-edge data structure 2) Supports curvilinear surface definitions up to degree 4 3) Ray-tracing 4) Coarsening 5) Feature-based refinement 6) Projection 7) Smoothing 8) Mesh quality validity tests 9) Mesh repair Additional Features: 1) Solar radiation pressure model 2) Distant 3rd body model 3) Collision detection 4) Trajectory integration and post-processing 5) Visualization of surface fields

Pearl, Jason↗