A versatile signal-enhanced ECL sensing platform based on molecular imprinting technique via PET-RAFT cross-linking polymerization using bifunctional ruthenium complex as both catalyst and sensing probes

Abstract

Molecularly imprinted technique (MIT) has proven to be a significant tool in the analyzing area in virtue of its obvious advantages such as specific recognition, favorable stability to high temperature and higher sensitivity. Electrochemiluminescence (ECL) technology has also been receiving enormous attention as a powerful tool in sensing fields. However, sensors based on the combination of MIT and ECL technologies have seldom been reported yet. Herein, we find that Ru(bpy)32+ cannot only work as an efficient catalyst for photo-induced electron transfer–reversible addition–fragmentation chain transfer (PET-RAFT) polymerization, but also as a sensing probe for ECL sensor. Based on this, we successfully construct ECL sensors via the combination of MIT and ECL techniques. In details, poly(methacrylic acid) (PMAA) and cross-linked PMAA were synthesized first via a well-controlled PET-RAFT polymerization using Ru(bpy)32+ as catalyst under illumination of visible light with a wavelength of 460 nm, as confirmed by 1H NMR and gel permeation chromatography (GPC). Then, negatively-charged Au nanoparticles (AuNPs) with average sizes of 20 nm were prepared and modified with Ru(bpy)32+ via electrostatic incorporation. MIPs were prepared on the surface of AuNPs using melamine (MEL) as the template via PET-RAFT controlled cross-linking polymerization. The MIPs modified AuNPs (AuNPs-MIPs) were then fixed on the surface of working electrode with Nafion to achieve a solid-state ECL sensing platform employing Ru(bpy)32+ as the ECL probes. The as-prepared sensor showed a wide detection range of 5.0 × 10–13 − 5.0 × 10−6 mol/L and a low detection limit of 1.0 × 10–13 mol/L (S/N ≥ 3) was reached in the detection of MEL. Moreover, further tests for analyzing MEL structural analogues proved that the constructed ECL sensing platform could be utilized to detect various substances via specific recognitions.