Thickness-dependent enhanced optoelectronic performance of surface charge transfer-doped ReS2 photodetectors

Surface charge transfer doping has been widely utilized to tune the electronic and optical properties of semiconductor photodetectors based on low-dimensional materials.Although many studies have been conducted on the performance(response time,responsivity,etc.)of doped photodetectors and their mechanisms,they merely examined a specific thickness and did not systematically explore the dependence of doping effects on the number of layers.This work performs a series of investigations on ReS_(2)photodetectors with different numbers of layers and demonstrates that the p-dopant tetrafluorotetracyanoquinodimethane(F_(4)-TCNQ)converts the deep trap states into recombination centers for few-layer ReS_(2)and induces a vertical p-n junction for thicker ReS_(2).A response time of 200 ms is observed in the decorated 2-layer ReS_(2)photodetector,more than two orders of magnitude faster than the response of the pristine photodetector,due to the disappearance of deep trap states.A current rectification ratio of 30 in the F_(4)-TCNQ-decorated sandwiched ReS_(2)device demonstrates the formation of a vertical p-n junction in a thicker ReS_(2)device.The responsivity is as high as 2,000 A/W owing to the strong carrier separation of the p-n junction.Different thicknesses of ReS_(2)enable switching of the prominent operating mechanism between transforming deep trap states into recombination centers and forming a vertical p-n junction.The thicknessdependent doping effect of a two-dimensional material serves as a new mechanism and provides a scheme toward improving the performance of other semiconductor devices,especially optical and electronic devices based on low-dimensional materials.

Joint shape and color descriptors for 3D urban model retrieval

With the rapid development of photogrammetry,computer vision and three-dimensional(3D)modeling technologies,it is possible to efficiently construct detailed 3D urban models.Accordingly,large corpora of 3D models,such as the Google 3D Warehouse,are now becoming freely available on the web.How to find the proper 3D urban models is a challenging research issue.In this paper,we join shape descriptors and color descriptors for 3D urban model retrieval.The query objects are localized and segmented automatically from the input images by using a new selective search voting algorithm.Through combining the normalization with the light field descriptor,the Horizontal Light Descriptor is introduced to measure the shape similarity among the normalized urban models.The color descriptors are used to represent the color information of the urban models.The two types of descriptors are joined to search 3D urban models similar to the query objects.Experimental results have shown the effectiveness of our approach.