- Using silver nanocluster/graphene nanocomposite to enhance photoelectrochemical activity of CdS:Mn/TiO2 for highly sensitive signal-on immunoassay
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- 论文类型： 期刊论文
- 发表时间： 2016-06-15
- 发表刊物： BIOSENSORS & BIOELECTRONICS
- 收录刊物： SCIE、EI、PubMed
- 卷号： 80
- 页面范围： 614-620
- ISSN号： 0956-5663
- 关键字： Silver nanocluster and graphene nanocomposite; Photoelectrochemical immunoassay; Signal-on; Enhanced photo-to-current conversion efficiency; CdS:Mn/TiO2
- 摘要： A highly sensitive signal-on photoelectrochemical (PEC) immunosensor was fabricated here using CdS:Mn/TiO2 as photoelectrochemical sensing platform, and silver nanoclusters and graphene naocomposites (AgNCs-GR) as signal amplification tags. The immunosensor was constructed based on the specific sandwich immunoreaction, and the photo-to-current conversion efficiency of the isolated protein modified CdS:Mn/TiO2 matrix was improved based on the synergistic effect of AgNCs-GR. Under irradiation, the photogenerated electrons from the AgNCs at a higher conduction band edge level could be transport to the CdS:Mn/TiO2 matrix with the assistance of highly conductive graphene nanosheets, as well as recycle the trapped excitons in the defects-rich CdS:Mn/TiO2 interface. The electron transport and exciton recycle reduced the possibility of electron-hole recombination and greatly improved the photo-to-current conversion efficiency of the sensing matrix. Based on the signal enhancement, a signal-on PEC immunosensors was fabricated for the detection of carcinoembryonic antigen (CEA), a model analyte related to many malignant diseases. Under optimal conditions, the as-prepared immunosensor showed excellent analytical performance, with a wide linear range from 1.0 pg/mL to 100 ng/mL and a low limit of detection of 1.0 pg/mL. The signal-on mode provided 2.48 times higher sensitivity compared with signal-off mode. This strategy demonstrated good accuracy and high selectivity for practical sample analysis, thus may have great application prospective in the prediction and early diagnosis of diseases. (C) 2016 Elsevier B.V. All rights reserved.
