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A Simple Method for Automated Solid Phase Extraction of Water Samples for Immunological Analysis of Small Pollutants
Published on: January 1, 2016
A dual-potential self-enhancing electrochemiluminescence immunosensor for simultaneous detection of estrogenic
Romana Manzoor1, Aniqa Sehrish2, Tiantian Kong1
1College of Chemistry and Environmental Engineering, Institute for Advanced Study, School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, PR China.
Abstract:
17β-estradiol (E2) and estriol (E3), as natural estrogen contaminants in aquatic environments, have significant risks to ecosystems and human health by disrupting endocrine functions and inducing reproductive disorders even at trace levels. To address the urgent need for simultaneous monitoring of these structurally similar targets, we developed a dual-potential electrochemiluminescence (ECL) immunosensor for efficient parallel detection of E2 and E3. The sensor employs poly (1-naphthylamine)-molybdenum disulfide decorated with gold-silver bimetallic nanoclusters (PNA-MoS2@AuAg NCs) as an ECL emitter, utilizing the self-enhancing property of AuAg NCs for intermolecular charge transfer and dual-potential-responsive properties to generate two well-resolved ECL signals (-0.9 V for E2 and -1.5 V for E3). Meanwhile, reduced graphene oxide/nickel cobaltite (rGO/NiCo2O4) hollow microspheres were used as catalytic amplification platforms, enhancing antibody-antigen binding efficiency through high surface area and stability. The competitive immunoreaction enables noninterference detection by spatially isolating E2/E3 recognition events on distinct potential-triggered interfaces. This strategy achieves ultra-sensitive quantification with a broad detection range of 0.0005-1500 ng/mL and calculated limits of detection of 0.207 fg/mL (E2) and 0.011 pg/mL (E3). The proposed immunosensor demonstrates a robust and versatile ECL immunosensing strategy for multi-target estrogen detection, offering a promising tool for multiplex contaminant analysis in complex samples.
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