Issue 26, 2022

An efficient 3D adsorbent foam based on graphene oxide/AgO nanoparticles for rapid vortex-assisted floating solid phase extraction of bisphenol A in canned food products

Abstract

In this study, a three-dimensional adsorbent was developed based on graphene oxide/AgO nanoparticles over interconnected nickel foam (GO/AgO@Ni foam) for rapid and efficient vortex assisted floating solid phase extraction of bisphenol A in canned food products prior to high performance liquid chromatography with a fluorescence detector. The analytical techniques scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and Fourier transform infrared (FT-IR) were used for characterization of the synthetized GO/AgO@Ni foam. The effect of proficiency factors including pH, foam size, vortexing time, salt addition, sample volume, desorption type and volume, and desorption time on the extraction efficiency of bisphenol A were explored through the matrix match method. Under the above experimental conditions, the figures of merit of the method were acquired as LODs (S/N = 3) of 0.18–0.84 μg kg−1, LOQs of 0.61–2.81 μg kg−1 (S/N = 10), linear ranges of 0.5–500 μg kg−1, and enrichment factors of 235.5–244.9. The inter-day precision values (RSD%, n = 7) of 2.5–3.6 and the intra-day precision (%) of (5 days and seven replicates for each day) 2.8–3.8 were achieved for bisphenol A at a concentration of 50 μg kg−1. The relative recoveries of 94.0% to 99.6% were obtained for the canned food samples.

Graphical abstract: An efficient 3D adsorbent foam based on graphene oxide/AgO nanoparticles for rapid vortex-assisted floating solid phase extraction of bisphenol A in canned food products

Article information

Article type
Paper
Submitted
11 Mar 2022
Accepted
24 May 2022
First published
23 Jun 2022

Anal. Methods, 2022,14, 2623-2630

An efficient 3D adsorbent foam based on graphene oxide/AgO nanoparticles for rapid vortex-assisted floating solid phase extraction of bisphenol A in canned food products

M. Shirani, A. Aslani, S. Sepahi, E. Parandi, A. Motamedi, E. Jahanmard, H. R. Nodeh and B. Akbari-adergani, Anal. Methods, 2022, 14, 2623 DOI: 10.1039/D2AY00426G

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