Unveiling Generally-ignored Co-substrate Effect of Catalase-inherent Peroxidase Mimic for Self-verifiable Detection of High-concentration Hydrogen Peroxide.
Haiwei Hou, Weijuan Jia, Aoxue Zhang, Minyang Su, Yazhong Bu, Lan Liu, Baoji Du
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引用次数: 0
Abstract
One nanoparticle possessing both peroxidase (POD) and catalase (CAT) activities is a prevalent co-substrate nanozyme system, distinct from the extensively researched cascade nanozyme system. During the sensing of hydrogen peroxide by POD, the impact of CAT is actually ignored in most studies. In this study, the CAT effect on hydrogen peroxide detection is thoroughly investigated based on POD catalysis by finely tuning the relative activity of POD and CAT. It is discovered that the CAT effect can be changed by delaying the injection of chromogenic substrate after adding hydrogen peroxide and that the linear range grows with the delayed time. Then, a theoretical mechanism showed that the time-delay mediated CAT effect magnification does not change the Vmax, but it causes Km to linearly increase with delayed time, consistent with the experiment results. Furthermore, the detection of high concentrations of hydrogen peroxide is successfully realized in contact lens care solutions by utilizing time-delay-mediated POD/CAT nanozyme. On the other hand, its linear range-tunable characteristic is used to produce multiple standard curves, then enabled self-verifying hydrogen peroxide detection. Overall, this work investigates the role of CAT in CAT-inherent POD nanozymes both theoretically and experimentally, and confirms POD/CAT nanozyme's priority in developing high-performance sensors.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.