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.

Surface analysis using shell-isolated nanoparticle-enhanced Raman spectroscopy

Abstract: Surface-enhanced Raman scattering (SERS) is a powerful fingerprint vibrational spectroscopy with a single-molecule detection limit, but its applications are generally restricted to 'free-electron-like' metal substrates such as Au, Ag and Cu nanostructures We have invented a shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique, using Au-core silica-shell nanoparticles (Au@SiO(2) NPs), which makes SERS universally applicable to surfaces with any composition and any morphology This protocol describes how to prepare shell-isolated nanoparticles (SHINs) with different well-controlled core sizes (55 and 120 nm), shapes (nanospheres, nanorods and nanocubes) and shell thicknesses (1-20 nm) It then describes how to apply SHINs to Pt and Au single-crystal surfaces with different facets in an electrochemical environment, on Si wafer surfaces adsorbed with hydrogen, on ZnO nanorods, and on living bacteria and fruit With this method, SHINs can be prepared for use in ~3 h, and each subsequent procedure for SHINERS measurement requires 1-2 h

Adaptive Hypergraph Learning and its Application in Image Classification

Abstract: Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.