Highly efficient near-infrared electrochemiluminescence resonance energy transfer system for biosensing: Nonmetallic plasmon Mediated well-matched energy donor-acceptor pair

电化学发光 表面等离子共振 材料科学 接受者 生物传感器 纳米技术 光电子学 纳米颗粒 化学 检出限 凝聚态物理 色谱法 物理
作者
Wei Meng,Xiaojiao Du,Ding Jiang,Yude Zhang,Xueling Shan,Wenchang Wang,Hiroshi Shiigi,Zhidong Chen
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:236: 115420-115420 被引量:13
标识
DOI:10.1016/j.bios.2023.115420
摘要

Herein, a well-matched energy donor-acceptor pair strategy was designed to construct highly efficient NIR ECL-RET system. In detail, an ECL amplification system consisting of SnS2 quantum dots decorated Ti3C2 MXene nanocomposites (SnS2 QDs-Ti3C2) as the energy donor were developed via a one-pot method, and the nanocomposites exhibited highly efficient NIR ECL emission due to the surface-defect effect generated by the oxygen-containing functional groups in MXene. Nonmetallic plasmon hydrated defective tungsten oxide nanosheets (dWO3•H2O) were utilized as energy acceptors because of its strong surface plasmon resonance effect in Vis-NIR absorption range. Compared with non-defective tungsten oxide hydrate nanosheets (WO3•H2O), the overlapping area between ECL spectrum of SnS2 QDs-Ti3C2 and UV-vis spectrum of dWO3•H2O was increased by 2.1 times, and the results showed that more efficient quenching effect was obtained. As a proof of concept, tetracycline (TCN) aptamer and its complementary chain were served as a bridge to connect the energy donor and acceptor, achieving the successful construction of NIR ECL-RET aptasensor. The as-fabricated ECL sensing platform exhibited a low detection limit of 6.2 fM (S/N = 3) within a wide linear range from 10 fM to 10 μM. Besides, the NIR ECL-RET aptasensor also showed excellent stability, reproducibility and selectivity, providing a promising tool to detect TCN in real samples. This strategy offered a universal and effective method in constructing highly efficient NIR ECL-RET system for developing rapid, sensitive and accurate biological detection platform.
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