Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/48229
Title: COF-derived multicore-shell FeCo nanoalloys@N-doped carbon for sensitive electrochemical detection of caffeic acid
Authors: Zhang , Shu
Zhang , Yao
Chen, Feiyang
Yang , Juan
Huang, Lijin
Zhang , Yuanyuan
YANG, Nianjun 
Issue Date: 2025
Publisher: ELSEVIER SCI LTD
Source: Food chemistry, 494 (Art N° 146180)
Abstract: Caffeic acid is a key indicator of wine quality, but its sensitive and accurate detection remains challenging due to the lack of high-performance sensing materials. Metal/N-doped porous carbon (M/NPC) electrocatalysts with abundant catalytic sites are promising to address this issue. Herein, a FeCo nanoalloy encapsulated in NPC (FeCo@NPC) was designed and synthesized via a "covalent organic framework (COF) adsorption-pyrolysis" strategy. The COF template enabled homogeneous dispersion and stabilization of FeCo. The resulting FeCo@NPC exhibited hierarchical porosity, abundant metal-Nx sites, and strong bimetallic synergetic effects, leading to exceptional electrocatalytic activity towards caffeic acid oxidation. The constructed sensor achieved high sensitivity (5.195 and 0.895 mu A mu M- 1) and selectivity. It was successfully applied for caffeic acid detection in commercial tablets and wines, and further tracked changes in caffeic acid concentration in red wine over postopening time. This work provides a general design strategy for designing high-performance electrocatalysts for food quality control.
Notes: Yang, J (corresponding author), Wuhan Inst Technol, Sch Chem & Environm Engn, Hubei Key Lab Novel Reactor & Green Chem Technol, Key Lab Green Chem Proc,Minist Educ, Wuhan 430205, Peoples R China.
jyangchem@wit.edu.cn
Keywords: Electrochemical sensor;Metal - nitrogen coordination sites;Pyrolysis;Wine analysis
Document URI: http://hdl.handle.net/1942/48229
ISSN: 0308-8146
e-ISSN: 1873-7072
DOI: 10.1016/j.foodchem.2025.146180
ISI #: 001653959900001
Rights: 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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