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  5. Copper‐Nitride Nanowires Array: An Efficient Dual‐Functional Catalyst Electrode for Sensitive and Selective Non‐Enzymatic Glucose and Hydrogen Peroxide Sensing

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Article
en
2017

Copper‐Nitride Nanowires Array: An Efficient Dual‐Functional Catalyst Electrode for Sensitive and Selective Non‐Enzymatic Glucose and Hydrogen Peroxide Sensing

0 Datasets

0 Files

en
2017
Vol 23 (21)
Vol. 23
DOI: 10.1002/chem.201700366

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Abdullah Mohamed Asiri
Abdullah Mohamed Asiri

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Zao Wang
Xiaoqin Cao
Danni Liu
+5 more

Abstract

It is highly attractive to develop non-noble-metal nanoarray architecture as a 3D-catalyst electrode for molecular detection due to its large specific surface area and easy accessibility to target molecules. Here, we report the development of a copper-nitride nanowires array on copper foam (Cu3 N NA/CF) as a dual-functional catalyst electrode for efficient glucose oxidation in alkaline solutions and hydrogen peroxide (H2 O2 ) reduction in neutral solutions. Electrochemical tests indicate that such Cu3 N NA/CF possesses superior non-enzymatic sensing ability toward rapid glucose and H2 O2 detection with high selectivity. At 0.40 V, this sensor offers a high sensitivity of 14 180 μA mm cm-2 for glucose detection, with a wide linear range from 1 μm to 2 mm, a low detection limit of 13 nm (S/N=3), and satisfactory stability and reproducibility. Its application in determining glucose in human blood serum is also demonstrated. Amperometric H2 O2 sensing can also been realized with a sensitivity of 7600 μA mm cm-2 , a linear range from 0.1 μm to 10 mm, and a detection limit of 8.9 nm (S/N=3). This 3D-nanoarray architecture holds great promise as an attractive sensing platform toward electrochemical small molecules detection.

How to cite this publication

Zao Wang, Xiaoqin Cao, Danni Liu, Shuai Hao, Rongmei Kong, Gu Du, Abdullah Mohamed Asiri, Xuping Sun (2017). Copper‐Nitride Nanowires Array: An Efficient Dual‐Functional Catalyst Electrode for Sensitive and Selective Non‐Enzymatic Glucose and Hydrogen Peroxide Sensing. , 23(21), DOI: https://doi.org/10.1002/chem.201700366.

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Publication Details

Type

Article

Year

2017

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/chem.201700366

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