Issue 23, 2021

High performance benzene vapor sensor based on three-dimensional photonic crystals of zeolitic imidazolate framework-8@graphene quantum dots

Abstract

Superior sensitive, selective, and repeatable real-time detection of low concentrations of benzene vapor is vitally important for environmental protection and human health. A benzene vapor sensor using three-dimensional photonic crystals (3-D PCs) based on zeolitic imidazolate framework-8@graphene quantum dots (ZIF-8@GQDs) was proposed. The 3-D PCs were acquired by centrifuging ZIF-8@GQDs pseudo-solutions, which were prepared via hydrothermal methods. The application of the ZIF-8@GQDs 3-D PCs sensor for optical benzene vapor detection via the strong π–π stacking interactions and large specific surface area and abundant open-framework structure of the ZIF-8@GQDs was investigated. The ZIF-8@GQDs 3-D PCs sensor exhibits a more sensitive response to benzene vapor compared with the ZIF-8 3-D PCs sensor. The relationship between the wavelength shift and the benzene vapor concentration was demonstrated to be linear. Additionally, the ZIF-8@GQDs 3-D PCs sensor presents a fast optical response and recovery times of 1 s and 7 s for 200 ppm benzene vapor detection, the benzene vapor detection limit can reach 1 ppm, and the deviation of the reflected wavelength varied within 2 nm after 10 cycles. Moreover, the fabricated ZIF-8@GQDs 3-D PCs sensor exhibited reliability and exceptional thermal and long-time storage stability, demonstrating great potential for practical benzene vapor sensing applications.

Graphical abstract: High performance benzene vapor sensor based on three-dimensional photonic crystals of zeolitic imidazolate framework-8@graphene quantum dots

Supplementary files

Article information

Article type
Paper
Submitted
18 Aug 2021
Accepted
20 Oct 2021
First published
26 Oct 2021

Analyst, 2021,146, 7240-7249

High performance benzene vapor sensor based on three-dimensional photonic crystals of zeolitic imidazolate framework-8@graphene quantum dots

Z. Wang, K. Zhan, Y. Zhu, J. Yan, B. Liu and Y. Chen, Analyst, 2021, 146, 7240 DOI: 10.1039/D1AN01502H

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