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

被引:0
|
作者
Zhu, Xueying [1 ]
Li, Tian [1 ]
Hai, Xin [1 ]
Bi, Sai [1 ]
机构
[1] Qingdao Univ, Coll Chem & Chem Engn, Res Ctr Intelligent & Wearable Technol, Qingdao 266071, Peoples R China
来源
BIOSENSORS & BIOELECTRONICS | 2022年 / 213卷
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Sensor array; Nanozyme; Graphene quantum dots; Thiols discrimination; Colorimetry;
D O I
暂无
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
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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页数:9
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