Flexible and wearable sensor for in situ monitoring of gallic acid in plant leaves.

Electrochemical biosensor Flexible sensor Gallic acid In situ Molybdenum disulfide Mxene

Journal

Food chemistry
ISSN: 1873-7072
Titre abrégé: Food Chem
Pays: England
ID NLM: 7702639

Informations de publication

Date de publication:
03 Aug 2024
Historique:
received: 27 03 2024
revised: 12 07 2024
accepted: 31 07 2024
medline: 11 8 2024
pubmed: 11 8 2024
entrez: 10 8 2024
Statut: aheadofprint

Résumé

Gallic acid (GA) is one of the main phenolic components naturally occurring in many plants and foods and has been a subject of increasing interest owing to its antioxidant and anti-mutagenic properties. This study introduces a novel flexible sensor designed for in situ detecting GA in plant leaves. The sensor employs a laser-induced graphene (LIG) flexible electrode, enhanced with MXene and molybdenum disulfide (MoS

Identifiants

DOI: 10.1016/j.foodchem.2024.140740 PMID: 39126955
pubmed: 39126955
pii: S0308-8146(24)02390-2
doi: 10.1016/j.foodchem.2024.140740
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

140740

Informations de copyright

Copyright © 2024 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.

Auteurs

Ke Liu (K)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China.

Bin Luo (B)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Le Zhang (L)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Peichen Hou (P)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Dayu Pan (D)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Tianyang Liu (T)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Chunjiang Zhao (C)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China. Electronic address: zhaocj@nercita.org.cn.

Aixue Li (A)

Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. Electronic address: liax@nercita.org.cn.

Classifications MeSH