Issue 46, 2022

Synthesis of honeycomb Ag@CuO nanoparticles and their application as a highly sensitive and electrocatalytically active hydrogen peroxide sensor material

Abstract

Copper acetate/silver nitrate/polyvinylpyrrolidone was first prepared into nano-hybrid silver-doped copper oxide by electrospinning, and then nano-honeycomb particles were produced through heat-treatment. For the first time, honeycomb Ag@CuO nanoparticles were prepared by electrospinning, and a H2O2 sensor was constructed by modifying the carbon paste electrode (CPE) with the honeycomb Ag@CuO nanoparticles. This work performed the structural, morphological, and phase analysis of the Ag@CuO nanoparticles by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The results indicated the synthesis of Ag@CuO hybrid nanoparticles with high purity, and cyclic voltammetry and amperometry show that the Ag@CuO modified electrode has high electrocatalytic performances with fast voltammetric responses and a notably decreased overpotential compared to that of even the CuO modified CPE. In addition, the Ag/CuO-CPE based H2O2 sensor has the highest sensitivity of 1982.14 μA (mmol L−1)−1 cm−2, the lowest detection limit of 0.01 μmol L−1 ((S/N) = 3), and the measured linear response for H2O2 oxidation ranged from 0.05 μmol L−1 to 100 μmol L−1 and 100 μmol L−1 to 1.5 mmol L−1. The proposed method was applied to the determination of H2O2 in coconut fruit samples from canned coconut, and the satisfactory results confirmed the applicability of this sensor in practical analysis.

Graphical abstract: Synthesis of honeycomb Ag@CuO nanoparticles and their application as a highly sensitive and electrocatalytically active hydrogen peroxide sensor material

Article information

Article type
Paper
Submitted
28 Jul 2022
Accepted
25 Oct 2022
First published
26 Oct 2022

Anal. Methods, 2022,14, 4842-4850

Synthesis of honeycomb Ag@CuO nanoparticles and their application as a highly sensitive and electrocatalytically active hydrogen peroxide sensor material

Y. Li, P. Yin, Y. Zhang and R. Zhang, Anal. Methods, 2022, 14, 4842 DOI: 10.1039/D2AY01211A

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