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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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Electronically Modified Atomic Sites Within a Multicomponent Co/Cu Composite for Efficient Oxygen El
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GFCCLib: Scalable and efficient coupled-cluster Green's function library for accurately tackling many-body electronic structure problems
Coupled-cluster Green’s function (GFCC) calculation has drawn much attention in the recent years for targeting the molecular and material electronic structure problems from a many-body perspective in a systematically improvable way. However, GFCC calculations on scientific computing clusters usually suffer from expensive higher di- mensional tensor contractions in the complex space, expensive inter-process communi- cation, and severe load imbalance, which limits it’s routine use for tackling electronic structure problems. Here we present a numerical library prototype that is specifically designed for large-scale GFCC calculations. The design of the library is focused on a systematically optimal computing strategy to improve its scalability and efficiency. The performance of the library is demonstrated by the relevant profiling analysis of running GFCC calculations on remote giant computing clusters. The capability of the library is highlighted by computing a wide near valence band of a fullerene C60 molecule for the first time at the GFCCSD level that shows excellent agreement with the experimental spectrum.
A learning-based time-efficient framework for building energy performance evaluation
Not Available
Pathways of CdS Quantum Dot Degradation during Photocatalysis: Implications for Enhancing Stability and Efficiency for Organic Synthesis
CdS quantum dots (QDs) are widely employed as photocatalysts for reactions such as hydrogen evolution, and their degradation under aerobic aqueous conditions is well understood. However, despite evidence of aggregation and precipitation of CdS QD photocatalysts under anaerobic conditions, catalyst speciation and degradation under such conditions are underexplored. In this work, we demonstrate that during a reductive dehalogenation reaction, CdS QDs undergo surface ligand etching, which leads to a loss of colloidal stability and the formation of microcrystalline cadmium metal deposits. We hypothesize that this results from the accumulation of electrons on the QD surface. In addition, we demonstrate mild surface sulfur oxidation and the formation of an ammonium salt byproduct of a commonly used hole quencher. Furthermore, this work adds to our atomic-level understanding of the reactions occurring at the QD surface during photocatalysis, so that we can design more stable and efficient photocatalysts for organic synthesis.
Ultralow-Loading Pt/Zn Hybrid Cluster in Zeolite HZSM-5 for Efficient Dehydroaromatization
Minimizing Pt loading without sacrificing catalytic performance is critical, particularly for designing cost-efficient hydrocarbon transformation catalysts. Here, we show that ultralow-loading (0.001–0.05 wt %) Pt- and Zn-functionalized HZSM-5 catalysts, prepared through simple ion exchange and impregnation, are highly active and stable for light alkane dehydroaromatization (DHA). The specific activity of benzene, toluene, and xylene is up to 8.2 mol/g Pt /min (or 1592 min –1 ) over the 0.001 wt % Pt–Zn 2 /HZSM-5 catalyst during ethane DHA at 550 °C under atmospheric pressure. Additionally, such bimetallic Pt x –Zn y /HZSM-5 catalysts are highly stable in contrast to the monometallic Pt/HZSM-5 catalysts. The rate constant of deactivation (k deactiv ), according to the first-order generalized power law equation model, for the bimetallic catalysts is up to 120 times lower than that of the monometallic counterparts, depending on the Pt loading. In conclusion, this breakthrough is achieved through the formation of the [Pt 1 –Zn n ] δ+ hybrid cluster, instead of Pt 0 cluster–proton adducts, in the micropores of the ZSM-5 zeolite.
A hole-transport material that also passivates perovskite surface defects for solar cells with improved efficiency and stability
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Green syntheses of stable and efficient organic dyes for organic hybrid light-emitting diodes
Organic hybrid light-emitting diodes (hybrid-LEDs) employ organic dyes as light converters on top of commercial blue inorganic LEDs, replacing incumbent inorganic phosphor light converters synthesized from rare-earth and/or toxic metallic elements to optimize device environmental sustainability. Here, we present two naturally derived organic dyes for hybrid-LEDs, highlighting stability and efficiency enhancement based on a novel “acceptor–acceptor” molecular design. This “acceptor–acceptor” skeleton comprises theobromine and thiadiazole, two electron-withdrawing groups that lower energy levels and suppress photooxidation. This differentiates these dyes from the widely adopted “donor–acceptor” skeleton, where photooxidation is facilitated by the presence of electron-donating units. Simultaneously, sidechains on organic dyes used to enhance solution processability, crucial for film transparency, introduce an additional photooxidation pathway. With this “acceptor–acceptor” skeleton, the destabilization from sidechains was offset by the stability enhancement from the electronic effects in the backbone. Additionally, when blended within an industrial polymer, poly(styrene-butadiene-styrene) (SBS), their enhanced solubility enables the formation of highly transparent films, crucial for reducing scattering loss in LEDs. Furthermore, resultant dye-SBS films achieved photoluminescence quantum yields (PLQYs) of around 90% under ambient conditions. Taking advantage of their transparency and solution processability, we fabricated a waveguide with this theobromine-dye-SBS composite, which was subsequentially assembled into an edge-lit LED device of no glare and enhanced aesthetics.
Atomically dispersed single Ni site catalysts for high-efficiency CO[subscript 2] electroreduction a
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An efficient alkoxy-substituted polymer acceptor for efficient all-polymer solar cells with low voltage loss and versatile photovoltaic applications
All-polymer solar cells with low voltage loss for efficient outdoor and indoor photovoltaics.
Precipitation alters the CO 2 effect on water-use efficiency of temperate forests
Not provided.
Mn 2+ coordinates Cap-0-RNA to align substrates for efficient 2′- O -methyl transfer by SARS-CoV-2 nsp16
Efficient RNA capping by the SARS-CoV-2 methyltransferase requires metal ions and a unique four-residue insert.
Conductive Metallophthalocyanine Framework Films with High Carrier Mobility as Efficient Chemiresistors
Abstract The poor electrical conductivity of two‐dimensional (2D) crystalline frameworks greatly limits their utilization in optoelectronics and sensor technology. Herein, we describe a conductive metallophthalocyanine‐based NiPc‐CoTAA framework with cobalt(II) tetraaza[14]annulene linkages. The high conjugation across the whole network combined with densely stacked metallophthalocyanine units endows this material with high electrical conductivity, which can be greatly enhanced by doping with iodine. The NiPc‐CoTAA framework was also fabricated as thin films with different thicknesses from 100 to 1000 nm by the steam‐assisted conversion method. These films enabled the detection of low‐concentration gases and exhibited remarkable sensitivity and stability. This study indicates the enormous potential of metallophthalocyanine‐based conductive frameworks in advanced stand‐off chemical sensors and provides a general strategy through tailor‐make molecular design to develop sensitive and stable chemical sensors for the detection of low‐concentration gases.
Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.
Efficient photothermochemical dry reforming of methane over Ni supported on ZrO[subscript 2] with Ce
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