Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

High-Efficiency Photoelectrocatalytic Hydrogen Generation Enabled by Palladium Quantum Dots-Sensitized TiO2 Nanotube Arrays

Abstract: TiO(2) nanotube arrays (TNTAs) sensitized by palladium quantum dots (Pd QDs) exhibit highly efficient photoelectrocatalytic hydrogen generation. Vertically oriented TNTAs were prepared by a three-step electrochemical anodization. Subsequently, Pd QDs with uniform size and narrow size distribution were formed on TiO(2) nanotubes by a modified hydrothermal reaction (i.e., yielding nanocomposites of Pd QDs deposited on TNTAs, Pd@TNTAs). By exploiting Pd@TNTA nanocomposites as both photoanode and cathode, a substantially increased photon-to-current conversion efficiency of nearly 100% at λ = 330 nm and a greatly promoted photocatalytic hydrogen production rate of 592 μmol·h(-1)·cm(-2) under 320 mW·cm(-2) irradiation were achieved. The synergy between nanotubular structures of TiO(2) and uniformly dispersed Pd QDs on TiO(2) facilitated the charge transfer of photoinduced electrons from TiO(2) nanotubes to Pd QDs and the high activity of Pd QDs catalytic center, thereby leading to high-efficiency photoelectrocatalytic hydrogen generation.

Down-Conversion Nitride Materials for Solid State Lighting: Recent Advances and Perspectives.

Abstract: Advances in solid state white lighting technologies witness the explosive development of phosphor materials (down-conversion luminescent materials). A large amount of evidence has demonstrated the revolutionary role of the emerging nitride phosphors in producing superior white light-emitting diodes for lighting and display applications. The structural and compositional versatility together with the unique local coordination environments enable nitride materials to have compelling luminescent properties such as abundant emission colors, controllable photoluminescence spectra, high conversion efficiency, and small thermal quenching/degradation. Here, we summarize the state-of-art progress on this novel family of luminescent materials and discuss the topics of materials discovery, crystal chemistry, structure-related luminescence, temperature-dependent luminescence, and spectral tailoring. We also overview different types of nitride phosphors and their applications in solid state lighting, including general ...

Cellular-Connected UAV: Potential, Challenges, and Promising Technologies

Abstract: Enabling high-rate, low-latency and ultra-reliable wireless communications between UAVs and their associated ground pilots/users is of paramount importance to realize their large-scale usage in the future. To achieve this goal, cellular- connected UAV, whereby UAVs for various applications are integrated into the cellular network as new aerial users, is a promising technology that has drawn significant attention recently. Compared to conventional cellular communication with terrestrial users, cellular-connected UAV communication possesses substantially different characteristics that present new research challenges as well as opportunities. In this article, we provide an overview of this emerging technology, by first discussing its potential benefits, unique communication and spectrum requirements, as well as new design considerations. We then introduce promising technologies to enable the future generation of 3D heterogeneous wireless networks with coexisting aerial and ground users. Last, we present simulation results to corroborate our discussions and highlight key directions for future research.

Hypergraph Neural Networks

Abstract: In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.