化学
电化学发光
微囊藻毒素
劈理(地质)
微囊藻毒素
组合化学
蓝藻
色谱法
检出限
断裂(地质)
遗传学
生物
工程类
岩土工程
细菌
作者
Binnan Shi,Yue Jia,Dehao Jia,Tian Tian,Jingui Chen,Xiang Ren,Jianping Lei,Hongying Jia,Huan Wang,Qin Wei
出处
期刊:Analytical Chemistry
[American Chemical Society]
日期:2024-09-05
卷期号:96 (37): 15050-15058
被引量:24
标识
DOI:10.1021/acs.analchem.4c03495
摘要
An efficient electrochemiluminescence (ECL) emitter, Ir(ppy)3-based molecules has recently been reported to exhibit aggregation-induced electrochemiluminescence (AIECL) phenomenon. However, it remains a significant challenge to control the aggregation states of these molecules and achieve uniform aggregates with intense ECL emission. In this work, a biosensor was developed to detect microcystin-LR (MC-LR) based on Ir(ppy)3-functionalized zeolitic imidazolate framework-8 (Ir-ZIF-8) as the ECL emitter and the trans-cleavage activity of CRISPR-Cas12a as the methodological strategy. The Ir-ZIF-8, a functional metal–organic framework (MOF), exhibited the AIECL phenomenon via the spatial domain-limiting effect of encapsulating Ir(ppy)3 into the mesopores of ZIF-8, while the porosity and highly ordered topological structure of ZIF-8 effectively limited the molecular motion of Ir(ppy)3. CRISPR-Cas12a was employed to indiscriminately cleave double-stranded DNA decorated with carboxy tetramethylrhodamine (TAMRA), which quenched the ECL signal of Ir-ZIF-8 by resonance energy transfer and then separated the quencher from Ir-ZIF-8 to reactivate the signal. The concentration of MC-LR was designed to correlate with both the quencher amount and the activity of Cas12a. Then, two linear regression equations for MC-LR detection were constructed to improve the accuracy of the biosensor, and the constructed biosensor showed remarkable reproducibility, stability, and selectivity. The accurate detection of MC-LR with limits of detection of 1.2 and 5.9 pg/mL was made possible by the high quenching efficiency of TAMRA and the effective cutting ability of the editable CRISPR-Cas12a system.
科研通智能强力驱动
Strongly Powered by AbleSci AI