A nanozyme-based colorimetric sensor array as electronic tongue for thiols discrimination and disease identification

硫醇 对苯二甲酸 化学 电子舌 显色的 催化作用 基质(水族馆) 检出限 组合化学 传感器阵列 比色法 水溶液中的金属离子 石墨烯 金属 分析化学(期刊) 鉴定(生物学) 线性判别分析 生物传感器 选择性 纳米技术 无机化学 离子 化学计量学
作者
Xueying Zhu,Tian Li,Xin Hai,Sai Bi
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:213: 114438-114438 被引量:94
标识
DOI:10.1016/j.bios.2022.114438
摘要

Thiol analysis is of vital significance due to the essential roles in disease diagnosis, while the highly similar structures of thiols are a major challenge in practical determination. Herein, a nanozyme-based colorimetric sensor array has been proposed as electronic tongue for excellent discrimination and sensitive quantitation of thiols. The sensing units are fabricated by integrating the terephthalic acid modified graphene quantum dots (TPA@GQDs) with three transition metal ions (Fe2+, Cu2+ and Zn2+) via coordination, respectively, which not only provide sufficient substrate binding sites but also form the metal ion-regulated catalytic active centers. In this way, disparate promotion degrees on the peroxidase-like catalytic activity have been achieved in different metal ion-TPA@GQD ensembles. Based on the strong binding affinity between metal ions and thiols, the catalytic active centers are removed from TPA@GQDs, which inhibits the catalytic activity of sensing unit to diverse degrees. Accordingly, using 3, 3', 5, 5'-tetramethylbenzidine (TMB) as chromogenic substrate in the presence of hydrogen peroxide (H2O2), each sensing unit can generate differential colorimetric signals (fingerprints) for six thiol analytes, which can be accurately discriminated through linear discriminant analysis (LDA) with a detection limit of 50 nM. In addition, the discrimination of the same thiol with different concentrations and thiol mixtures have also been achieved. Furthermore, inspired by the distinct levels of thiols in practical samples, the proposed sensor array enables the identification of thiol-associated diseases by means of machine learning algorithm, which makes a positive contribution to medical diagnosis.
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