4.6 Article

Dual mimic enzyme properties of Fe nanoparticles embedded in two-dimensional carbon nanosheets for colorimetric detection of biomolecules

期刊

ANALYST
卷 148, 期 1, 页码 146-152

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d2an01704k

关键词

-

资金

  1. National Natural Science Foundation of China
  2. China Postdoctoral Science Foundation
  3. Jiangsu Zhongdan Group Co., Ltd.
  4. [21801132]
  5. [2021M701505]

向作者/读者索取更多资源

The development of novel nanozymes is crucial for the analysis of biomolecules in disease diagnosis. In this study, Fe@CNs-based nanozymes were constructed to detect GSH, H2O2, and glucose, demonstrating excellent reusability and potential application in biosensing.
The development of novel nanozymes is of great importance for the efficient analysis of biomolecules such as H2O2, glucose, and antioxidants in the diagnosis of some diseases. Herein, novel nanozymes based on Fe nanoparticles (NPs) encapsulated in 2D carbon nanosheets (denoted as Fe@CNs) were constructed and employed in the field of biosensing. Notably, Fe@CNs have intrinsic dual mimic enzyme properties. The colorless colorimetric substrate 3,3 ',5,5 '-tetramethylbenzidine (TMB) can be oxidized by Fe@CNs as oxidase- and peroxidase-like nanozymes, respectively. The generation of the oxidation state TMB (oxTMB) resulted from the presence of reactive oxygen species (ROS) which were produced by the catalytic decomposition of the dissolved oxygen or H2O2. Thus, a simple colorimetric biosensor was proposed to detect glutathione (GSH), H2O2, and glucose. In addition, the Fe@CN-based nanozymes also have excellent reusability in enzymatic catalysis. After separating from the sensing systems, Fe@CNs can be reused in other catalytic processes. This colorimetric method could be used as a universal sensing platform for the detection of antioxidants and H2O2-related bioanalysis. This work broadens the application of novel nanozymes in biosensing.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据