ABSTRACT
Copper-based halide perovskites have shown great potential in lighting and photodetection due to their excellent photoelectric properties, good stability and lead-free nature. However, as an important piece of copper-based perovskites, the synthesis and application of RbCu2I3have never been reported. Here, we demonstrate the synthesis of high-quality RbCu2I3microwires (MWs) by a fast-cooling hot saturated solution method. The prepared MWs exhibit an orthorhombic structure with a smooth surface. Optical measurements show the RbCu2I3MWs have a sharp ultraviolet absorption edge with 3.63 eV optical band gap and ultra-large stokes shift (300 nm) in photoluminescence. The subsequent photodetector based on a single RbCu2I3MW shows excellent ultraviolet detection performance. Under the 340 nm illumination, the device shows a specific detectivity of 5.0 × 109Jones and a responsivity of 380 mA·W-1. The synthesis method and physical properties of RbCu2I3could be a guide to the future optoelectronic application of the new material.
ABSTRACT
Optical assembly as a multiple optical trapping technique enables patterned arrangements of matter ranging from atoms to microparticles for diverse applications in biophysics, quantum physics, surface chemistry, and cell biology. Optical potential energy landscapes based on evanescent fields are conventionally employed for optical assembly of subwavelength particles, but are typically limited to predefined patterns and lacking in tunability. Here we present a microfiber photonic crystal cavity applicable for tunable optical assembly of subwavelength particles along a flexible path. This is enabled by excellent mechanical flexibility of the microfiber cavity as well as its broadband photonic crystal reflectors. By virtue of the broadband reflectors, the lattice constant of the assembled particles is precisely tunable via altering the wavelength of input light. Three-dimensional optical assembly is also realized by making use of the high-order transverse mode of the microfiber cavity. Moreover, the optical assembly process is detectable by simply monitoring the reflection/transmission spectrum of the microfiber cavity. The design of the microfiber cavity heralds a new way for tunable optical assembly of subwavelength particles, potentially applicable for development of tunable photonic crystals, metamaterials, and sensors.
ABSTRACT
Metal tungstates, expressed by the general formula of MWO4, have important properties and applications in photoluminescence, microwave applications, optical fibers, scintillator materials, humidity sensors, magnetic properties, and catalysts. In this paper, we report a successful synthesis of CaWO4:Eul+ crystals with various morphologies in mild hydrothermal conditions with surfacntant including sodium citrate, CTAB, PEG and citrate acid (CA). The formation of the crystals are strongly dependent on the employment of surfactant. The surfactant concentration has been found significant influence in the resulting morphologies due to different properties of each one. Extensive characterization have been performed by using X-ray diffraction (XRD), field emission scanning electron microscope (FE-SEM) in search of the formation mechanism of multi-morphological CaWO4:Eu3+ crystals. The growth mechanism of monodispersed CaWO4:EuS+ crystal are proposed. And the photoluminescence properties were investigated.
ABSTRACT
The function of the protein is closely correlated with its subcellular localization. Probing into the mechanism of protein sorting and predicting protein subcellular location can provide important clues or insights for understanding the function of proteins. In this paper, we introduce a new PseAAC approach to encode the protein sequence based on the physicochemical properties of amino acid residues. Each of the protein samples was defined as a 146D (dimensional) vector including the 20 amino acid composition components and 126 adjacent triune residues contents. To evaluate the effectiveness of this encoding scheme, we did jackknife tests on three datasets using the support vector machine algorithm. The total prediction accuracies are 84.9%, 91.2%, and 92.6%, respectively. The satisfactory results indicate that our method could be a useful tool in the area of bioinformatics and proteomics.
