Issue 11, 2022

A scaffold of thermally activated delayed fluorescent polymer dots towards aqueous electrochemiluminescence and biosensing applications

Abstract

To achieve the most efficient, all-exciton-harvesting organic electrochemiluminescence (ECL) for biosensing, aqueous thermally activated delayed fluorescence (TADF)-ECL (aqueous TADF-ECL) was successfully launched to provide a breakthrough for the theoretical ECL efficiency limitation of aqueous fluorescence ECL (aqueous FL-ECL). However, achieving efficient TADF emitters suitable for aqueous TADF-ECL remains challenging. A previous strategy relied on TADF small molecular nanoparticles (NPs). However, the aggregation caused quenching of such TADF molecules within NPs is intense, which renders such NPs inefficient for ECL emission. Herein, we propose developing conjugated polymer dots (Pdots) based aqueous TADF-ECL. Compared to the intrinsic TADF polymer, the Pdots achieve a comparable TADF photophysical properties in water, i.e., the comparable PL spectra, similar PL quantum efficiency (ΦPL) and intense delayed fluorescent contributions via a fast reverse intersystem crossing rate (kRISC) of 1.5 × 106 s−1. The resultant relative ECL efficiency (ΦECL) of the oxidative-reduction ECL system (C2O42− as the co-reactant) is as high as 11.73% (vs. the Ru(bpy)32+ counterpart). Additionally, satisfactory dopamine biosensing was accomplished for such TADF-Pdots/C2O42− couple. All those results are combined to highlight the promising potential of such an aqueous TADF-ECL strategy.

Graphical abstract: A scaffold of thermally activated delayed fluorescent polymer dots towards aqueous electrochemiluminescence and biosensing applications

Supplementary files

Article information

Article type
Paper
Submitted
28 Feb 2022
Accepted
19 Apr 2022
First published
21 Apr 2022

Analyst, 2022,147, 2442-2451

A scaffold of thermally activated delayed fluorescent polymer dots towards aqueous electrochemiluminescence and biosensing applications

Y. Luo, B. Zhao, B. Zhang, Y. Lan, L. Chen, Y. Zhang, Y. Bao and L. Niu, Analyst, 2022, 147, 2442 DOI: 10.1039/D2AN00352J

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