67 research outputs found

    Biomass-derived carbon–silicon composites (C@Si) as anodes for lithium-ion and sodium-ion batteries: A promising strategy towards long-term cycling stability: A mini review

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    The global need for high energy density and performing rechargeable batteries has led to the development of high-capacity silicon-based anode materials to meet the energy demands imposed to electrify plug-in vehicles to curtail carbon emissions by 2035. Unfortunately, the high theoretical capacity (4200 mA h g−1) of silicon by (de-)alloy mechanism is limited by its severe volume changes (ΔV ∌ 200% − 400%) during cycling for lithium-ion batteries (LIBs), while for sodium-ion batteries (NIBs) remain uncertain, and hence, compositing with carbons (C@Si) represent a promising strategy to enable the aforementioned practical application. The present review outlines the recent progress of biomass-derived Si-carbon composite (C@Si) anodes for LIBs and NIBs. In this perspective, we present different types of biomass precursors, silicon sources, and compositing strategies, and how these impact on the C@Si physicochemical properties and their electrochemical performance are discussed

    Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

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    We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

    Recurrent gross mutations of the PTEN tumor suppressor gene in breast cancers with deficient DSB repair

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    Basal-like breast cancer (BBC) is a subtype of breast cancer with poor prognosis. Inherited mutations of BRCA1, a cancer susceptibility gene involved in double-strand DNA break (DSB) repair, lead to breast cancers that are nearly always of the BBC subtype; however, the precise molecular lesions and oncogenic consequences of BRCA1 dysfunction are poorly understood. Here we show that heterozygous inactivation of the tumor suppressor gene Pten leads to the formation of basal-like mammary tumors in mice, and that loss of PTEN expression is significantly associated with the BBC subtype in human sporadic and BRCA1-associated hereditary breast cancers. In addition, we identify frequent gross PTEN mutations, involving intragenic chromosome breaks, inversions, deletions and micro copy number aberrations, specifically in BRCA1-deficient tumors. These data provide an example of a specific and recurrent oncogenic consequence of BRCA1-dependent dysfunction in DNA repair and provide insight into the pathogenesis of BBC with therapeutic implications. These findings also argue that obtaining an accurate census of genes mutated in cancer will require a systematic examination for gross gene rearrangements, particularly in tumors with deficient DSB repair

    Room temperature erosion behaviour of a precipitation hardened stainless steel

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    The main objective of the present investigation was to compare the room temperature erosion behaviour of precipitation hardened (PH) stainless steel in the solution-treated, aged, and overaged conditions and also to evaluate the effect of erodent size and hardness on the erosion behaviour of the steel. The erosion rates of PH stainless steel in all the three heat-treated conditions were determined over a range of impact angles and velocities and with SiC and SiO2 (of two sizes) as erodent. The heat treatment of PH stainless steel had only a marginal effect on the erosion rate irrespective of impact angle and velocity even though such a heat treatment altered the mechanical properties considerably. The erosion rate always exhibited a maximum value at an impact angle of 30° irrespective of the erosion test conditions indicating a ductile erosion response. It was also observed that the erosion rate was independent of size and hardness of the erodent. The marginal effect of material strength and ductility on the erosion rates of PH stainless steel in different heat-treated conditions were rationalized qualitatively on the basis of the localization model

    Experimental Investigation of Swirling Air Flows in a Multipoint LDI Combustor

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