费斯特共振能量转移
生物传感器
光子上转换
共振(粒子物理)
发光
级联
双模
荧光
共振感应耦合
纳米技术
能量转移
电子线路
光电子学
化学
材料科学
电子工程
物理
光学
色谱法
工程类
粒子物理学
量子力学
分子物理学
作者
Jiali Huang,Kang Cui,Lin Li,Xu Li,Fengyi Wang,Yangyang Wang,Yan Zhang,Shenguang Ge,Jinghua Yu
出处
期刊:Langmuir
[American Chemical Society]
日期:2023-11-02
卷期号:39 (45): 16048-16059
被引量:5
标识
DOI:10.1021/acs.langmuir.3c02187
摘要
Near-infrared (NIR)-responsive bioassays based on upconversion nanoparticle (UCNP) incorporating high-performance semiconductors have been developed by researchers, but most lack satisfactory ultrasensitivity for exceedingly trace amounts of target. Herein, for the first time, the CRISPR/Cas13a system is combined with cascade DNA circuits, fluorescent resonance energy transfer (FRET) effect, and luminescence-confined UCNPs-bonded CuInS2/ZnO p-n heterostructures-functionalized paper-working electrode to construct dual-signal-on paper-supported NIR-irradiated photoelectrochemical (PEC) (NIR-PEC) and upconversion luminescence (UCL) bioassay for high-sensitive quantification of miRNA-106a (miR-106a). By constructing an ideal FAM-labeled aminating molecular beacon (FAM-H2) model, a relatively good FRET ratio between the UCNP and FAM (≈85.3%) can be achieved. In the existence of miR-106a, the hairpin-structure FAM-H2 was unwound, bringing about the distance increase of UCNP and FAM and the restraint of FRET. Accordingly, both the NIR-PEC signal and the UCL intensity gradually recovered distinctly. Unlike conventional single-mode PEC sensors, with NIR excitation, the designed dual-mode sensing system could implement minimized misdiagnose assay and quantitative miR-106a determination with low detection limits, that is, 76.54 and 51.36 aM for NIR-PEC and UCL detection, respectively. This work not only broadens the horizon of application of the CRISPR/Cas13a strategy toward biosensing but also constructs a new structure of the UCNP-semiconductor in the exploration of efficient NIR-responsive tools and inspires the construction of a no-misdiagnosed and novel biosensor for dual-mode liquid biopsy.
科研通智能强力驱动
Strongly Powered by AbleSci AI