College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha, 410082, Hunan, China.
College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China; Key Laboratory of Environmental Biology and Pollution Control (Hunan University), Ministry of Education, Changsha, 410082, Hunan, China.
Biosens Bioelectron. 2020 Sep 15;164:112328. doi: 10.1016/j.bios.2020.112328. Epub 2020 May 28.
Quantum-sized cerium dioxide (CeO) show high catalytic capability as well as strong light absorption ability owing to its redox couple Ce/Ce and abundant oxygen vacancies, which making it a potential material for designing superior photoelectrochemical (PEC) sensors. However, it has scarcely been applied in the field of PEC sensing, because its wide band gap and aggregation effect can restrict the photoelectric conversion efficiency. Herein, we address these two obstacles by coupling CeO quantum dots (QDs) with graphitic carbon nitride (g-CN) and Au nanoparticles (NPs). The electron transfer path in this proposed heterojunction was proved by density functional theory (DFT) calculation for the first time, which provided theoretical support for the detection of MC-LR. The as-obtained PEC aptasensor exhibited excellent analytical performance with a wide liner response of 0.05-10 pM, and the detection limit was 0.01 pM. By designing appropriate sensing system and specific recognition mechanism, this work may pave a unique avenue for constructing ultrasensitive and selective analysis of MC-LR in complex environment without any external electric source.
量子尺寸的二氧化铈(CeO)由于其氧化还原对 Ce/Ce 和丰富的氧空位,表现出高催化能力和强的光吸收能力,使其成为设计优异光电化学(PEC)传感器的潜在材料。然而,由于其宽能带隙和聚集效应会限制光电转换效率,因此它在 PEC 传感领域的应用很少。在此,我们通过将 CeO 量子点(QDs)与石墨相氮化碳(g-CN)和金纳米颗粒(NPs)耦合来解决这两个障碍。通过密度泛函理论(DFT)计算,首次证明了该异质结中的电子转移路径,为 MC-LR 的检测提供了理论支持。所获得的 PEC 适体传感器表现出优异的分析性能,具有 0.05-10 pM 的宽线性响应,检测限为 0.01 pM。通过设计合适的传感系统和特定的识别机制,这项工作可能为在没有外部电源的情况下构建复杂环境中 MC-LR 的超灵敏和选择性分析铺平独特的道路。