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.

Oxidation resistance of graphene-coated Cu and Cu/Ni alloy

Abstract: The ability to protect refined metals from reactive environments is vital to many industrial and academic applications. Current solutions, however, typically introduce several negative effects, including increased thickness and changes in the metal physical properties. In this paper, we demonstrate for the first time the ability of graphene films grown by chemical vapor deposition to protect the surface of the metallic growth substrates of Cu and Cu/Ni alloy from air oxidation. In particular, graphene prevents the formation of any oxide on the protected metal surfaces, thus allowing pure metal surfaces only one atom away from reactive environments. SEM, Raman spectroscopy, and XPS studies show that the metal surface is well protected from oxidation even after heating at 200 °C in air for up to 4 h. Our work further shows that graphene provides effective resistance against hydrogen peroxide. This protection method offers significant advantages and can be used on any metal that catalyzes graphene growth.

Nanostructure-based plasmon-enhanced Raman spectroscopy for surface analysis of materials

Abstract: Since 2000, there has been an explosion of activity in the field of plasmon-enhanced Raman spectroscopy (PERS), including surface-enhanced Raman spectroscopy (SERS), tip-enhanced Raman spectroscopy (TERS) and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS). In this Review, we explore the mechanism of PERS and discuss PERS hotspots — nanoscale regions with a strongly enhanced local electromagnetic field — that allow trace-molecule detection, biomolecule analysis and surface characterization of various materials. In particular, we discuss a new generation of hotspots that are generated from hybrid structures combining PERS-active nanostructures and probe materials, which feature a strong local electromagnetic field on the surface of the probe material. Enhancement of surface Raman signals up to five orders of magnitude can be obtained from materials that are weakly SERS active or SERS inactive. We provide a detailed overview of future research directions in the field of PERS, focusing on new PERS-active nanomaterials and nanostructures and the broad application prospect for materials science and technology. Assisted by rationally designed novel plasmonic nanostructures, surface-enhanced Raman spectroscopy has presented a new generation of analytical tools (that is, tip-enhanced Raman spectroscopy and shell-isolated nanoparticle-enhanced Raman spectroscopy) with an extremely high surface sensitivity, spatial resolution and broad application for materials science and technology.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework

Abstract: In this paper, we designed an end-to-end spectral–spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.

Surface-Enhanced Raman Spectroscopy for Bioanalysis: Reliability and Challenges

Abstract: Surface-enhanced Raman spectroscopy (SERS) inherits the rich chemical fingerprint information on Raman spectroscopy and gains sensitivity by plasmon-enhanced excitation and scattering. In particular, most Raman peaks have a narrow width suitable for multiplex analysis, and the measurements can be conveniently made under ambient and aqueous conditions. These merits make SERS a very promising technique for studying complex biological systems, and SERS has attracted increasing interest in biorelated analysis. However, there are still great challenges that need to be addressed until it can be widely accepted by the biorelated communities, answer interesting biological questions, and solve fatal clinical problems. SERS applications in bioanalysis involve the complex interactions of plasmonic nanomaterials with biological systems and their environments. The reliability becomes the key issue of bioanalytical SERS in order to extract meaningful information from SERS data. This review provides a comprehensive over...