• 文献标题:   An ultra-sensitive acetylcholinesterase biosensor based on reduced graphene oxide-Au nanoparticles-beta-cyclodextrin/Prussian blue-chitosan nanocomposites for organophosphorus pesticides detection
  • 文献类型:   Article
  • 作  者:   ZHAO HY, JI XP, WANG BB, WANG N, LI XR, NI RX, REN JJ
  • 作者关键词:   acetylcholinesterase, electrochemical reduced graphene oxide, au nanoparticle, prussian blue, biosensor, organophosphorus pesticide
  • 出版物名称:   BIOSENSORS BIOELECTRONICS
  • ISSN:   0956-5663 EI 1873-4235
  • 通讯作者地址:   Hebei Med Univ
  • 被引频次:   118
  • DOI:   10.1016/j.bios.2014.10.007
  • 出版年:   2015

▎ 摘  要

This work reports a novel, ultrasensitive, and selective sensing platform based on a direct electrodeposition of electrochemical reduced graphene oxide (ERGO)-Au nanoparticles (AuNPs)-beta-cyclodextrin (beta-CD) and Prussian blue-chitosan (PB-CS) on glass carbon electrode (GCE) for efficiently fixed acetylcholinesterase (AChE) to fabricate organophosphorus pesticides (OPs) biosensor. The PB-CS not only effectively catalyzed the oxidation of thiocholine (TCh), but also shifted its oxidation potential from 0.68 to 0.2 V, and accordingly the sensitivity of the biosensor was obviously improved. The synergistic effect between ERGO and AuNPs significantly promoted the electron transfer between PB and GCE, and remarkably enhanced the electrochemical oxidation of TCh. Besides, beta-CD could interact with substrate by reversible bonding, which is contribute to increase the enrichment of the substrate and improve the selectivity and sensitivity of the biosensor. The integration of ERGO-AuNPs-beta-CD with PB-CS provided an advantageous and high-performance platform for sensing applications. Based on the inhibition of OPs on AChE activity, the sensor showed wide linear ranges of 7.98-2.00 x 10(3) pg mL(-1) and 4.3-1.00 x 10(3) pg mL(-1) with low detection limits of 4.14 pg mL(-1) and 1.15 pg mL(-1) for malathion and carbaryl, respectively. The proposed biosensor exhibited short response time, good stability and high sensitivity, which can be used for direct analysis of practical samples. (C) 2014 Elsevier B.V. All rights reserved.