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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A portable fiber optic sensor for the luminescent sensing of cobalt ions using carbon dots

Cobalt is critical to energy-relevant technologies, and demand for cobalt will increase significantly with growing global adoption of renewables. However, supply chain stability is threatened by economic and geopolitical factors, incentivizing domestic cobalt production from alternative resources such as coal, coal utilization byproducts (e.g., ash, acid mine drainage) and electronic waste. Rapid, inexpensive, and portable characterization techniques are needed to reduce production costs associated with cobalt prospecting and process monitoring. Here, in this research, we develop a compact, portable fiber optic luminescent probe for cobalt using phosphorus and nitrogen co-doped carbon dots as the sensing material. The carbon dot emission overlaps well with the cobalt absorption band at ~510 nm, leading to a selective decrease in emission as a function of cobalt concentration. The system responds nearly instantly to the presence of cobalt, with detection limits of 0.7 and 3.5 ppm in water and pH 1.68 buffer, respectively, providing comparable performance to a commercial spectrometer at a significantly lower cost. Moreover, the sensor is selective for cobalt in the presence of 13 of the most common metal ions encountered in coal utilization byproducts and is responsive to cobalt when spiked into an acid mine drainage leachate sample, highlighting the sensor's potential for real-world deployment in challenging matrices. In addition, integrating the carbon dots with a filter paper substrate produced ‘test strip’ sensors that exhibited a selective and sensitive visual response to cobalt using a handheld UV lamp. Taken together, the sensing system represents a significant step forward in the development of low-cost practical sensors for high-value metals in complex streams.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Direct atomic-level insight into the active sites of a high-performance PGM-free ORR catalyst

Platinum group metal–free (PGM-free) metal-nitrogen-carbon catalysts have emerged as a promising alternative to their costly platinum (Pt)–based counterparts in polymer electrolyte fuel cells (PEFCs) but still face some major challenges, including (i) the identification of the most relevant catalytic site for the oxygen reduction reaction (ORR) and (ii) demonstration of competitive PEFC performance under automotive-application conditions in the hydrogen (H 2 )–air fuel cell. We demonstrate H 2 -air performance gains achieved with an iron-nitrogen-carbon catalyst synthesized with two nitrogen precursors that developed hierarchical porosity. In current densities recorded in the kinetic region of cathode operation, at fuel cell voltages greater than ~0.75 V, were the same as those obtained with a Pt cathode at a loading of 0.1 milligram of Pt per centimeter squared. The catalytic active site we proposed, carbon-embedded nitrogen-coordinated iron (FeN 4 ), was directly visualized with aberration-corrected scanning transmission electron microscopy, and the contributions of these active sites associated with specific lattice-level carbon structures were explored computationally.

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

Enhancing Clean Energy Innovation Ecosystem Discovery Tool

During March 2016, the DOE’s Office of Energy Policy and Systems Analysis (EPSA) sought a methodology to identify and quantify the strength of the existing clean energy innovation ecosystems (IE) in the U.S. and their characteristics. Oak Ridge National Lab (ORNL) did a pilot study (Phase 1) and demonstrated the feasibility of an application comprised of natural language processing, link analysis, and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography. The data-collection, ingest and analytics pipelines were combined with an advanced user interface, together known as the Ecosystem Discovery Tool, to enhance DOE’s understanding of existing geographic innovation clusters. During Phase 2 of this project, this tool has been further enhanced by integrating new data sets and through backend software architecture changes, validation and bug fixing, making it a much more robust and powerful application. The tool’s user interface is a lot more intuitive, which enables a user to visualize the IE rankings for various geographic regions(CBSA, state level) for different clean energy technologies and seek automated insights. ORNL also created a DOE-specific dashboard that allows the user to visualize and analyze federal funding from various offices including Energy Efficiency and Renewable Energy(EERE), Office of Electricity(OE), Fossil Energy(FE), and Nuclear Energy(NE). During Phase 1 of this project, EPSA defined a clean energy innovation ecosystem as the overlap of five Ecosystem Components: 1) nascent clean energy Indicators, 2) investors, 3) enabling environment, 4) networking assets and 5) large companies. EPSA and ORNL worked together to collect data for each component: 1) small and medium companies, ARPA-E awardees, SBIR awardees, patents, publications, ERCs for Nascent Clean Energy Indicators; 2) qualified investors; 3) the Clean Edge Policy Index/DSIRE for the Enabling Environment; 4) universities, national laboratories, ERCs and incubators for Networking Assets; and 5) large companies and a subset of the Russell 1000 list for Large Companies. The ORNL team created a visual tool based on Tableau that integrated ecosystem component data to score, rank and map IEs. The tool was created with the flexibility to allow the user to choose the weights of each of the five ecosystem components and the subcomponents. This flexibility allows the user to visualize different subsets and to use the underlying data for different types of analysis. During Phase 2 of this project, the backend database and computation process have undergone major changes to provide a much more refined user-interface and added new features into the scoring algorithm. This phase also involved addition of newer databases such as a list of Energy Research Centers (ERC) ,DSIRE, and funding data from EERE, FE, NE, and OE to provide more granular information and more accurate/better quantification.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Impact of dissolved salts on two-phase flow and boiling heat transfer in a natural circulation loop

Two-phase natural circulation and boiling experiments are conducted in this study with artificial seawater as a heat transfer fluid or process coolant. This experimental work is focused on understanding the role of dissolved salts in water on the steady state two-phase natural circulation flow rates. Mass flow rates were measured at different heat flux levels with artificial seawater, deionized water and tap water in the experimental loop. It was found that the average quasi-steady state mass flow rates were the same at any particular heat flux level regardless of the fluid being used. Artificial seawater coolant exhibited more stable natural circulation at higher heat flux as compared to the other two coolants. Mass flow measurements recorded at 2 s intervals show that magnitude of flow oscillations are much higher in case of tap water and deionized water. Also, high speed camera images revealed that the bubble departure diameters are smaller in seawater compared to tap or deionized water. This can be attributed to the increased local wettability due to the presence of dissolved salts in the fluid. Visual observations of the riser section showed that bubble coalescence and formation of slugs is inhibited in the seawater. These observations also explain the reduced pressure drop across test section when seawater was used as a coolant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of high-affinity nanobodies specific for Na V 1.4 and Na V 1.5 voltage-gated sodium channel isoforms

Voltage-gated sodium channels, Na V s, are responsible for the rapid rise of action potentials in excitable tissues. Na V channel mutations have been implicated in several human genetic diseases, such as hypokalemic periodic paralysis, myotonia, and long-QT and Brugada syndromes. Here, we generated high-affinity anti-NaV nanobodies (Nbs), Nb17 and Nb82, that recognize the Na V 1.4 (skeletal muscle) and Na V 1.5 (cardiac muscle) channel isoforms. These Nbs were raised in llama (Lama glama) and selected from a phage display library for high affinity to the C-terminal (CT) region of Na V 1.4. The Nbs were expressed in Escherichia coli, purified, and biophysically characterized. Development of high-affinity Nbs specifically targeting a given human Na V isoform has been challenging because they usually show undesired crossreactivity for different Na V isoforms. Our results show, however, that Nb17 and Nb82 recognize the CTNa V 1.4 or CTNa V 1.5 over other CTNav isoforms. Kinetic experiments by biolayer interferometry determined that Nb17 and Nb82 bind to the CTNa V 1.4 and CTNa V 1.5 with high affinity (K D ~ 40–60 nM). In addition, as proof of concept, we show that Nb82 could detect Na V 1.4 and Na V 1.5 channels in mammalian cells and tissues by Western blot. Furthermore, human embryonic kidney cells expressing holo Na V 1.5 channels demonstrated a robust FRET-binding efficiency for Nb17 and Nb82. Our work lays the foundation for developing Nbs as anti-Na V reagents to capture Na V s from cell lysates and as molecular visualization agents for Na V s.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

InteraChem: Virtual Reality Visualizer for Reactive Interactive Molecular Dynamics

Interactive molecular dynamics in virtual reality (IMD-VR) simulations provide a digital molecular playground for students as an alternative or complement to traditional molecular modeling kits or 2D illustrations. Previous IMD-VR studies have used molecular mechanics to enable simulations of macromolecules such as proteins and nanostructures for the classroom setting with considerable success. Here, we present the InteraChem molecular visualizer, intended for reactive IMD-VR simulation using semiempirical and ab initio methods. InteraChem visualizes not only the molecular geometry but also (1) isosurfaces such as molecular orbitals and electrostatic potentials and (2) two-dimensional graphs of time-varying simulation quantities such as kinetic/potential energy, internal coordinates, and user-applied force. Additionally, InteraChem employs speech recognition to facilitate user interaction and introduces a novel “atom happiness” visualization using emojis to indicate the energetic feasibility of a particular bonding arrangement. Furthermore, we include a set of accompanying exercises that we have used to teach chemical reactivity in small molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Time-resolved in situ visualization of the structural response of zeolites during catalysis

Zeolites form an important class of materials that are widely used in various fields. Their unique properties come primarily from their size, shape, and connectivity of their sub-nm crystalline voids, the Si/Al ratio of the anionic framework, and the number and the nature of charge-balancing cations.The inhomogeneous distribution of the Si/Al ratio within crystals, and hence the cation density, the presence of organic residues, and their inhomogeneous distribution within crystals sensitively affect their performances because these factors influencethe intra-crystalline diffusion rates of the reactants and products. However, the information regarding these inhomogeneities in zeolite crystals cannot be obtained by conventional analytical tools because they only provide information regardingthe average properties of zeolite crystals. Here we employin situX-ray free electron laser-based time-resolved coherent X-ray diffraction imaging to investigate the internal deformations originating from the inhomogeneous Cu ion distributions in Cu-exchanged ZSM-5 zeolite crystals during the deoxygenation of nitrogen oxides with propene. We show that the interactions between the reactants and the active sites lead to an unusual strain distribution,which is confirmed by density functional theory simulations. Moreover, this elucidates the factors responsible for distinct active regions,which are essential to maximizing some applications with enhanced performances. These observations provide insights into the role of structural inhomogeneity in zeolites during a catalytic processand will assist the future design of zeolites for their applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

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↗
DOE OSTI2018

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↗
DOE OSTI2019

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↗
DOE OSTI2022

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↗
DOE OSTI2024

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↗
DOE OSTI2023

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↗
DOE OSTI2022

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↗
DOE OSTI2022

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↗
DOE OSTI2018

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↗