Issue 12, 2017

An ultrasensitive multi-walled carbon nanotube–platinum–luminol nanocomposite-based electrochemiluminescence immunosensor

Abstract

An ultrasensitive electrochemiluminescence (ECL) immunosensor for carbohydrate antigen 19-9 (CA19-9) detection using multi-walled carbon nanotube–platinum–luminol nanocomposites (MWCNT–Pt–luminol) as nanointerface and signal tags was designed. First, the MWCNT–Pt–luminol nanocomposite was decorated on the surface of a glassy carbon electrode (GCE) through the film-forming properties of chitosan (Chi). Then, the CA19-9 antibody (anti-CA19-9) was attached to the modified electrode surface via crosslinking with glutaraldehyde (GA). When CA19-9 was captured by its antibody immobilized on the immune platform via immunoreaction, the ECL signal intensity decreased. Under optimal conditions, the proposed ECL immunosensor showed excellent performance for CA19-9 ranging from 0.0001 U mL−1 to 10.0 U mL−1, with a detection limit of 0.000046 U mL−1 (S/N = 3) and a correlation coefficient of R = 0.9980. This is attributed to the fact that the MWCNTs–Pt nanomaterial has excellent conductivity and it could facilitate the decomposition of H2O2 to generate various reactive oxygen species (ROSs); thus, the ECL signals of luminol were effectively amplified and the sensitivity of the sensor was greatly increased. The prepared ECL immunosensor displayed simple, fast analysis, excellent stability, good reproducibility, and high specificity. Moreover, the developed ECL immunosensor provided satisfactory results in the determination of CA19-9 in real human serum samples.

Graphical abstract: An ultrasensitive multi-walled carbon nanotube–platinum–luminol nanocomposite-based electrochemiluminescence immunosensor

Article information

Article type
Paper
Submitted
09 Ube 2017
Accepted
02 Mot 2017
First published
25 Mot 2017

Analyst, 2017,142, 2253-2260

An ultrasensitive multi-walled carbon nanotube–platinum–luminol nanocomposite-based electrochemiluminescence immunosensor

X. Zhang, H. Ke, Z. Wang, W. Guo, A. Zhang, C. Huang and N. Jia, Analyst, 2017, 142, 2253 DOI: 10.1039/C7AN00417F

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