Lattice fringe signatures of epitaxy on nanotubes

Carbon nanotubes are of potential interest as heterogeneous catalysis supports, in part because they offer a high surface area hexagonal array of carbon atoms for columnar or epitaxial attachment. Fringe visibility modeling of electron microscope lattice images allows one to investigate the relationship between individual nanoparticles and such nanotube supports. We show specifically how (111) columnar or epitaxial growth of FCC metal lattices, on carbon nanotubes viewed side-on, results in well-defined patterns of (111)-fringe orientations with respect to the tube axis. In the epitaxial case, the observations also provide information on chirality of the nanotube's outermost graphene sheet.Comment: 4 pages, 5 figures, 9 refs, cf. http://newton.umsl.edu/~run/nano/epitaxy.htm

Spin Hydrodynamic Generation in the Charged Subatomic Swirl

Recently there have been significant interests in the spin hydrodynamic generation phenomenon from multiple disciplines of physics. Such phenomenon arises from global polarization effect of microscopic spin by macroscopic fluid rotation and is expected to occur in the hot quark-gluon fluid (the ``subatomic swirl'') created in relativistic nuclear collisions. This was indeed discovered in experiments which however revealed an intriguing puzzle: a polarization difference between particles and anti-particles. We suggest a novel application of a general connection between rotation and magnetic field: a magnetic field naturally arises along the fluid vorticity in the charged subatomic swirl. We establish this mechanism as a new way for generating long-lived in-medium magnetic field in heavy ion collisions. Due to its novel feature, this new magnetic field provides a nontrivial explanation to the puzzling observation of a difference in spin hydrodynamic generation for particles and anti-particles in heavy ion collisions.Comment: 10 pages, 3 figures, title changed according to published versio

Measurement Errors and their Propagation in the Registration of Remote Sensing Images (?)

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be ?perfect?. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. In this paper, we introduce the consistent adjusted least squares (CALS) estimator and propose a relaxed consistent adjusted least squares (RCALS) method, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The validity of the CALS and RCALS estimators are first demonstrated by applying them to perform geometric corrections of controlled simulated images. The conceptual arguments are further substantiated by a real-life example. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performances in estimating the regression coefficients and variance of measurement errors. Keywords: error propagation, geometric correction, ordinary least squares, registration, relaxed consistent adjusted least squares, remote sensing images.