866 research outputs found

    Probing signatures of bounce inflation with current observations

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    The aim of this paper is to probe the features of the bouncing cosmology with the current observational data. Basing on bounce inflation model, with high derivative term, we propose a general parametrization of primordial power spectrum which includes the typical bouncing parameters, such as bouncing time-scale, and energy scale. By applying Markov Chain Monto Carlo analysis with current data combination of Planck 2015, BAO and JLA, we report the posterior probability distributions of the parameters. We find that, bouncing models can well explain CMB observations, especially the deficit and oscillation on large scale in TT power spectrum.Comment: 17 pages, 8 figure

    Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

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    The neutral hydrogen (HI) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure (LSS) studies. A major issue for the HI IM survey is to remove the bright foreground contamination. A key to successfully remove the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effect of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future HI IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a preprocessing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA preprocessing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on HI fluctuation amplitude.Comment: 13 pages, 13 figures; accepted for publication in MNRA

    Room-Temperature Sodium-Sulfur Batteries: A Comprehensive Review on Research Progress and Cell Chemistry

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    Room temperature sodium-sulfur (RT-Na/S) batteries have recently regained a great deal of attention due to their high theoretical energy density and low cost, which make them promising candidates for application in large-scale energy storage, especially in stationary energy storage, such as with electrical grids. Research on this system is currently in its infancy, and it is encountering severe challenges in terms of low electroactivity, limited cycle life, and serious self-charging. Moreover, the reaction mechanism of S with Na ions varies with the electrolyte that is applied, and is very complicated and hard to detect due to the multi-step reactions and the formation of various polysulfides. Therefore, understanding the chemistry and optimizing the nanostructure of electrodes for RT-Na/S batteries are critical for their advancement and practical application in the future. In the present review, the electrochemical reactions between Na and S are reviewed, as well as recent progress on the crucial cathode materials. Furthermore, attention also is paid to electrolytes, separators, and cell configuration. Additionally, current challenges and future perspectives for the RT-Na/S batteries are discussed, and potential research directions toward improving RT-Na/S cells are proposed at the end

    Capillary-Induced Ge Uniformly Distributed in N-Doped Carbon Nanotubes with Enhanced Li-Storage Performance

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    Germanium (Ge) is a prospective anode material for lithium-ion batteries, as it possesses large theoretical capacity, outstanding lithium-ion diffusivity, and excellent electrical conductivity. Ge suffers from drastic capacity decay and poor rate performance, however, owing to its low electrical conductivity and huge volume expansion during cycling processes. Herein, a novel strategy has been developed to synthesize a Ge at N-doped carbon nanotubes (Ge at N-CNTs) composite with Ge nanoparticles uniformly distributed in the N-CNTs by using capillary action. This unique structure could effectively buffer large volume expansion. When evaluated as an anode material, the Ge at N-CNTs demonstrate enhanced cycling stability and excellent rate capabilities

    Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model

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    Lake thermal stratification is important for regulating lake environments and ecosystems and is sensitive to climate change and human activity. However, numerical simulation of coupled hydrodynamics and heat transfer processes in deep lakes using one-dimensional lake models remains challenging because of the insufficient representation of key parameters. In this study, Lake Qiandaohu, a deep and warm monomictic reservoir, was used as an example to investigate thermal stratification via an improved parameterization scheme of the Weather Research and Forecast (WRF)-Lake. A comparison with in situ observations demonstrated that the default WRF-Lake model was able to simulate well the seasonal variation of the lake thermal structure. However, the simulations exhibited cold biases in lake surface water temperature (LSWT) throughout the year while generating weaker stratification in summer, thereby leading to an earlier cooling period in autumn. With an improved parameterization (i.e., via determination of initial lake water temperature profiles, light extinction coefficients, eddy diffusion coefficients and surface roughness lengths), the modified WRF-Lake model was able to better simulate LSWT and thermal stratification. Critically, employing realistic initial conditions for lake water temperature is essential for producing realistic hypolimnetic water temperatures. The use of time-dependent light extinction coefficients resulted in a deep thermocline and warm LSWT. Enlarging eddy diffusivity led to stronger mixing in summer and further influenced autumn cooling. The parameterized surface roughness lengths mitigated the excessive turbulent heat loss at the lake surface, improved the model performance in simulating LSWT, and generated a warm mixed layer. This study provides guidance on model parameterization for simulating the thermal structure of deep lakes and advances our understanding of the strength and revolution of lake thermal stratification under seasonal changes

    Heteroaromatic organic compound with conjugated multi-carbonyl as cathode material for rechargeable lithium batteries

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    The heteroaromatic organic compound, N,N\u27-diphenyl-1,4,5,8-naphthalenetetra-carboxylic diimide (DP-NTCDI-250) as the cathode material of lithium batteries is prepared through a simple one-pot N-acylation reaction of 1,4,5,8-naphthalenetetra-carboxylic dianhydride (NTCDA) with phenylamine (PA) in DMF solution followed by heat treatment in 250 °C. The as prepared sample is characterized by the combination of elemental analysis, NMR, FT-IR, TGA, XRD, SEM and TEM. The electrochemical measurements show that DP-NTCDI-250 can deliver an initial discharge capacity of 170 mAh g-1 at the current density of 25 mA g-1. The capacity of 119 mAh g-1 can be retained after 100 cycles. Even at the high current density of 500 mA g-1, its capacity still reaches 105 mAh g-1, indicating its high rate capability. Therefore, the as-prepared DP-NTCDI-250 could be a promising candidate as low cost cathode materials for lithium batteries

    Manganese based layered oxides with modulated electronic and thermodynamic properties for sodium ion batteries

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    Manganese based layered oxides have received increasing attention as cathode materials for sodium ion batteries due to their high theoretical capacities and good sodium ion conductivities. However, the Jahn–Teller distortion arising from the manganese (III) centers destabilizes the host structure and deteriorates the cycling life. Herein, we report that zinc-doped Na0.833[Li0.25Mn0.75]O2 can not only suppress the Jahn–Teller effect but also reduce the inherent phase separations. The reduction of manganese (III) amount in the zinc-doped sample, as predicted by first-principles calculations, has been confirmed by its high binding energies and the reduced octahedral structural variations. In the viewpoint of thermodynamics, the zinc-doped sample has lower formation energy, more stable ground states, and fewer spinodal decomposition regions than those of the undoped sample, all of which make it charge or discharge without any phase transition. Hence, the zinc-doped sample shows superior cycling performance, demonstrating that zinc doping is an effective strategy for developing high-performance layered cathode materials

    Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes

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    Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologicallyirrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies
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