Nanozyme sensor array based on Fe, Se co-doped carbon material for the discrimination of Sulfur-containing compounds

硫黄 传感器阵列 检出限 比色法 主成分分析 显色的 鉴定(生物学) 化学 色谱法 计算机科学 有机化学 人工智能 生物 植物 机器学习
作者
Enxiang Ren,Haochen Qiu,Zhixuan Yu,Min Cao,Muhammad Sohail,Guoping Lu,Xing Zhang,Yamei Lin
出处
期刊:Journal of Hazardous Materials [Elsevier BV]
卷期号:470: 134127-134127 被引量:12
标识
DOI:10.1016/j.jhazmat.2024.134127
摘要

Developing methods for the accurate identification and analysis of sulfur-containing compounds (SCCs) is of great significance because of their essential roles in living organisms and the diagnosis of diseases. Herein, Se-doping improved oxidase-like activity of iron-based carbon material (Fe-Se/NC) was prepared and applied to construct a four-channel colorimetric sensor array for the detection and identification of SCCs (including biothiols and sulfur-containing metal salts). Fe-Se/NC can realize the chromogenic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) by activating O2 without relying on H2O2, which can be inhibited by different SCCs to diverse degrees to produce different colorimetric response changes as "fingerprints" on the sensor array. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that nine kinds of SCCs could be well discriminated. The sensor array was also applied for the detection of SCCs with a linear range of 1-50 μM and a limit of detection of 0.07-0.2 μM. Moreover, colorimetric sensor array inspired by the different levels of SCCs in real samples were used to discriminate cancer cells and food samples, demonstrating its potential application in the field of disease diagnosis and food monitoring. In this work, a four-channel colorimetric sensor array for accurate SCCs identification and detection was successfully constructed. The colorimetric sensor array inspired by the different levels of SCCs in real samples were also used to discriminate cancer cells and food samples. Therefore, this Fe-Se/NC based sensor array is expected to be applied in the field of environmental monitoring and environment related disease diagnosis.
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