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Chen K, Ma C, Chen G, Yang T, Gao H, Li L, Yang Z, Cao J, Zheng C, Ma L. SERS substrate based on COF@Ag for detecting amoxicillin in honey and lake water. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 313:124165. [PMID: 38493514 DOI: 10.1016/j.saa.2024.124165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/04/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
This study presents the design of a Surface-enhanced Raman scattering (SERS) substrate, COF@Ag, for the sensitive detection of Amoxicillin (AMX) in lake water and honey. Furthermore, the study investigates the role of covalent organic frameworks (COFs) in SERS detection. The characterization results demonstrate the capability of COFs to efficiently enrich Ag nanoparticles (AgNPs), resulting in a more concentrated distribution of hotspots and an enhanced electromagnetic field on the substrate. By employing density functional theory (DFT) simulation, the frontier electronic orbitals of COFs and AMX were analyzed, and the chemical bonds and weak interactions in the system were examined using the Interaction Region Indicator (IRI) method to propose potential enhancement mechanisms. In aqueous solutions, the linear range is 1 μg/L-30 μg/L, with a limit of detection (LOD) 0.279 μg/L. In lake water, the linear range span from 100 μg/L to 500 μg/L, with a detection limit of 8.244 μg/L. For honey, the linear range extend from 20 ng/g to 100 ng/g, with a detection limit of 2.917 ng/g. This method holds key significance in facilitating the rapid detection of amoxicillin and advancing the application of COFs in SERS.
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Affiliation(s)
- Kun Chen
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Chaoqun Ma
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China.
| | - Guoqing Chen
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Taiqun Yang
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Hui Gao
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Lei Li
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Zichen Yang
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China; School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jun Cao
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Chenkai Zheng
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
| | - Longyao Ma
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, China; Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology, Wuxi, Jiangsu 214122, China
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