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Observation of Zitterbewegung in photonic microcavities

Light(Science & Applications) 2023年第6期12卷 1084-1090页

作者： Seth Lovett Paul M.Walker Alexey Osipov Alexey Yulin pooja uday naik Charles E.Whittaker Ivan A.Shelykh Maurice S.Skolnick Dmitry N.Krizhanovskii Department of Physics and Astronomy University of SheffieldS37RH SheffieldUK Department of Physics and Technology ITMO UniversitySt.Petersburg 197101Russia Science Institute University of IcelandDunhagi 3IS-107 ReykjavikIceland

We present and experimentally study the effects of the photonic spin–orbit coupling on the real space propagation of polariton wavepackets in planar semiconductor microcavities and polaritonic analogues of *** particular,we demonstrate the appearance of an analogue Zitterbewegung effect,a term which translates as‘trembling motion’in English,which was originally proposed for relativistic Dirac electrons and consisted of the oscillations of the centre of mass of a wavepacket in the direction perpendicular to its *** a planar microcavity,we observe regular Zitterbewegung oscillations whose amplitude and period depend on the wavevector of the *** then extend these results to a honeycomb lattice of coupled microcavity *** to the planar cavity,such lattices are inherently more tuneable and versatile,allowing simulation of the Hamiltonians of a wide range of important physical *** observe an oscillation pattern related to the presence of the spin-split Dirac cones in the *** both cases,the experimentally observed oscillations are in good agreement with theoretical modelling and independently measured bandstructure parameters,providing strong evidence for the observation of Zitterbewegung.

关键词： planar versatile analogues

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MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

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Journal of Biophysical Chemistry 2011年第2期2卷 112-123页

作者： Pradeep Kumar naik Piyush Ranjan pooja Kesari Sankalp Jain Department of Biotechnology and Bioinformatics Jaypee University of Information Technology Waknaghat Solan- 173215 Hi-machal Pradesh India

Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1st layer of the prediction engine is for identifying a query protein as metalloprotein or not;the 2nd layer for the main functional class;and the 3rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://***/assets/Metallopred/.

关键词： Metalloprotein Classification Sequence Derived Parameters Neural Networks

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