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End date: 2019 × Start date: 2010 × Type: Text × Publisher: Cambridge : RSC Publ. × Subject: Nanostructures ×

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    High-rate amorphous SnO2 nanomembrane anodes for Li-ion batteries with a long cycling life
    (Cambridge : RSC Publ., 2014) Liu, Xianghong; Zhang, Jun; Si, Wenping; Xi, Lixia; Oswald, Steffen; Yan, Chenglin; Schmidt, Oliver G.
    Amorphous SnO2 nanomembranes as anodes for lithium ion batteries demonstrate a long cycling life of 1000 cycles at 1600 mA g−1 with a high reversible capacity of 854 mA h g−1 and high rate capability up to 40 A g−1. The superior performance is because of the structural features of the amorphous SnO2 nanomembranes. The nanoscale thickness provides considerably reduced diffusion paths for Li+. The amorphous structure can accommodate the strain of lithiation/delithiation, especially during the initial lithiation. More importantly, the mechanical feature of deformation can buffer the strain of repeated lithiation/delithiation, thus putting off pulverization. In addition, the two-dimensional transport pathways in between nanomembranes make the pseudo-capacitance more prominent. The encouraging results demonstrate the significant potential of nanomembranes for high power batteries.
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    A manual and an automatic TERS based virus discrimination
    (Cambridge : RSC Publ., 2015) Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen
    Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.