Recognizing single phospholipid vesicle collisions on carbon fiber nanoelectrode

作     者：Yueyue Zhang Min Li Zhenhua Li Qian Li Ali Aldalbahi Jiye Shi Lihua Wang Chunhai Fan Xiaolei Zuo 

作者机构：Division of Physical BiologyShanghai Institute of Applied PhysicsChinese Academy of SciencesShanghai 201800China University of Chinese Academy of SciencesBeijing 100049China Chemistry DepartmentKing Saud UniversityRiyadh 11451Saudi Arabia 

出 版 物：《Science China Chemistry》 (中国科学（化学英文版）)

年 卷 期：2017年第60卷第11期

页      面：1474-1480页

核心收录：

学科分类：081704[工学-应用化学] 07[理学] 070205[理学-凝聚态物理] 08[工学] 0817[工学-化学工程与技术] 070302[理学-分析化学] 080501[工学-材料物理与化学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation of China(21422508,31470960) Chinese Academy of Sciences support by the Deanship of Scientific Research,College of Science Research Center at King Saud University 

主　　题：single vesicle carbon fiber nanoelectrode high resolution electrochemical detection 

摘      要：We recognize the stochastic collisions of dopamine contained phospholipid vesicle on carbon fiber nanoelectrode, extending the observation of discrete collision events on nanoelectrode to biologically relevant analytes. To decrease noise interference to the technique, the dimensions of nanoelectrode was systematically investigated and optimized. Scanning electron microscopy(SEM) further supported the comparable sizes of nanoelectrode and vesicles(~100 nm in diameter). Vesicles collision and rupture on the surface of nanoelectrode led to the dopamine release from vesicles, which could be electrochemically oxidized to dopamine-o-quinone and detected via voltammetry. The comparable size of the nanoelectrode with vesicles and fast voltammetry allowed differentiation of single collision events from the current magnitudes and peak widths in the electrochemical collision experiments, which shows the efficacy of the method to characterize vesicle samples. This work provides a foundation upon which quantitative sensor technology might be built for the detection of dopamine contained vesicles with high spatial and temporal resolution.