In this study, transparent membranes containing luminescent Tb3+ and Eu3+ complex-doped silica nanoparticles were prepared via electrospinning. We prepared the electrospun fibrous membranes containing Tb(acac)3phen- (acac = acetylacetone, phen = 1,10-phenanthroline) and/or Eu(tta)3phen- (tta = 2-thenoyltrifluoroacetone) doped silica (M-Si-Tb3+ and M-Si-Eu3+) and studied their photoluminescence properties. The fibrous membranes containing the rare earth complexes were prepared by electrospinning. The surface morphology and thermal properties of the fibrous membrane were studied by atomic force microscopy (AFM), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), respectively. Fluorescence spectroscopy was used to characterize the fluorescence properties of the membranes. During the electrospinning process, the PVDF transitions from the α phase to the β phase, which exhibits a more rigid structure. The introduction of rigid materials, like PVDF and silica, can improve the fluorescence properties of the hybrid materials by reducing the rate of nonradiative decay. So the emission spectra at 548 nm (Tb) and 612 nm (Eu) were enhanced, as compared to the emission from the pure complex. Furthermore, the fluorescence lifetimes ranged from 0.6 to 1.5 ms and the quantum yields ranged from 32% to 61%. The luminescent fibrous membranes have potential applications in the fields of display panels, innovative electronic and optoelectronic devices.
With the increase of open APIs appeared on the Web, reusing or combining these APIs to develop novel applications (e.g. Mashups) has attracted great interest from developers. However, to quickly find a suitable one among a huge number of APIs to meet a developer's requirement is basically a non-trivial issue. Therefore, a highquality API recommendation system is desirable. Although a number of collaborative filtering methods have been proposed for API recommendation, their recommendation accuracy is limited and needs to be further improved. Based on the neural graph collaborative filtering technique, this paper proposes an API recommendation method that exploits the high-order connectivity between APIs and API users. To evaluate the proposed method, extensive experiments are conducted on a real API dataset and the results show that the proposed method outperforms the state-of-the-art methods in API recommendation.
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