Terbium(III) and dysprosium(III) 8-connected 3D networks containing 2,5-thiophenedicarboxylate anion: Crystal structures and photoluminescence studies

Two novel coordination polymers with the formula {[Ln(2)(2,5-tdc)(3)(dmso)(2)].H2O}(n) (Ln = Tb(III) for (1) and Dy(III) for (2)), (2,5-tdc(2-) = 2,5-thiophenedicarboxylate and dmso = dimethylsulfoxide) have been synthesized by the diffusion method and characterized by thermal analysis, vibrational spectroscopy and single crystal X-ray diffraction analysis. Structure analysis reveals that 2,5-tdc(2-) play a versatile role toward different lanthanide ions to form three-dimensional metal-organic frameworks (MOFs) in which the lanthanides ions are heptacoordinated. Photophysical properties were studied using excitation and emission spectra, where the photoluminescence data show the high emission intensity of the characteristic transitions D-5(4 ->) F-7(J) (J= 6, 5, 4 and 3) for (1) and (F9/2 -> HJ)-F-4-H-6 (J = 15/2, 13/2 and 11/2) for (2), indicating that 2,5-tdc(2-) is a good sensitizer. (C) 2012 Elsevier Ltd. All rights reserved.

Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures.