A robust, magnetic, and self-accelerated electrochemiluminescent nanosensor for ultrasensitive detection of copper ion.
Biosens Bioelectron
; 109: 109-115, 2018 Jun 30.
Article
em En
| MEDLINE
| ID: mdl-29544159
ABSTRACT
It is well known that the conventional electrochemiluminescence (ECL) biosensor rely on the heterogeneous assay formats that involves the immobilization of biorecognition probe on the electrode surface before signal collection, which inevitably cause the efficiency of bio-recognition reactions to be limited owing to the existence of local steric hindrance. Herein, a robust, magnetic, and self-accelerated ECL nanosensor based on the multifunctionalized cobalt ferrite magnetite nanoparticles (CoFe2O4 MNPs) was firstly designed for copper ion (Cu2+) detection in quasi-homogeneous system. The prepared nanosensor has its unique advantages compared to the iron oxide (Fe3O4) MNPs-based nanosensor for which magnetic nanoparticle just provide the reaction interface and magnetic enrichment. Specifically, the prepared CoFe2O4 MNPs-based biosensing platform could bridge the gap between aqueous phase and solid materials in homogeneous solution, achieving the expansion of reaction area and the reduction of local steric hindrance with high biorecognition efficiency. Furthermore, compared with the common magnetite nanosensors, the prepared CoFe2O4 MNPs achieved a set of magnetic collection, biorecognition probes immobilization, rapid separation and signal amplification in an ECL measurement system because it could act as a new co-reaction accelerator in ECL ternary (PTC-NH2 +â¯S2O82- +â¯CoFe2O4) system, achieving a self-accelerated biosensing platform with significant enhancement of the detection sensitivity. As expected, the prepared CoFe2O4 MNPs-based ECL nanosensors were successfully applied for ultrasensitive detection of Cu2+via click reaction with a linear range from 10-13 M to 1.0â¯×â¯10-7 M, which exhibited high sensitivity, excellent selectivity and good reproducibility.
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01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article