文献类型：期刊文献

英文题名：Imaging electrocatalytic processes on single gold nanorods

作者：Jing, Chao[1,2,3];Gu, Zhen[1,2];Long, Yi-Tao[1,2]

机构：[1]East China Univ Sci & Technol, Adv Mat Lab, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Chem, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Tech Univ Munich, Phys Dept E20, James Franck Str 1, D-85748 Garching, Germany

年份：2016

卷号：193

起止页码：371

外文期刊名：FARADAY DISCUSSIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000390786000022)】；

基金：This research was supported by the National Natural Science Foundation of China (21327807, 21421004), the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (YJ0130504), and the Postdoctoral Council of China (2015 International Postdoctoral Exchange Fellowship Program).

语种：英文

摘要：Imaging electrochemical processes has attracted increasing attention in past decades. Particularly, monitoring electrochemical reactions rapidly at the nano-scale is still a challenge due to the ultra-low current detection and long scanning time required. The development of optical techniques provide a new way to demonstrate electrochemical processes through optical signals which enhance sensitivity and spatial resolution. Herein, we developed a novel method to image electrocatalytic processes on single gold nanorods (GNRs) during Cyclic Voltammetry (CV) scanning based on plasmon resonance scattering information by using dark-field microscopy. The electrocatalytic oxidation of hydrogen peroxide was selected as a typical reaction and the catalytic mechanism was revealed using the obtained spectra. Notably, observation on single GNRs avoided the averaging effects in bulk systems and confirmed that the individual nanoparticles had variable catalytic properties with different spectral change during the reaction process. Furthermore, a color-amplified system was introduced to convert light intensity into imaging information via the Matlab program which was able to image thousands of nanoparticles simultaneously. This approach offered the statistical intensity distribution of all of the nanoparticles in a dark-field image which dramatically enhanced the detection accuracy and avoided random events.