Published September 2019 | Version v1
Journal article

Sensitive detection of glyphosate based on a Cu-BTC MOF/g-C3N4 nanosheet photoelectrochemical sensor

  • 1. The College of Chemistry and Chemical Engineering, Jiangsu Key Laboratory of Environmental Engineering and Monitoring, Yangzhou University, 180 Si-Wang-Ting Road, Yangzhou, 225002 (China)
  • 2. State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, 130012 (China)
  • 3. School of Mathematical and Physical Sciences, University of Technology, Sydney, City Campus, Broadway, Sydney, NSW, 2007 (Australia)

Description

A photoelectrochemical (PEC) sensor based on hierarchically porous Cu-BTC/g-C3N4 nanosheet (Cu-BTC/CN-NS, BTC = benzene-1,3,5-tricarboxylic acid) material was constructed for the first time. As a composite material, the hierarchically porous Cu-BTC can help to efficiently capture suitable pesticide molecules and accelerate signal transmission, while CN-NS possesses good optical performance. Under the irradiation of visible light, the Cu metal center can coordinate with the added glyphosate to form Cu-glyphosate complexes, leading to the increased steric hindrance of electron transfer and consequently resulted in an obvious decrease in photocurrent. Through this strategy, the constructed sensor can realize the detection of glyphosate from non-electroactive to electroactive. The results indicate that this photoelectrochemical sensor has a low detection limit of 1.3 × 10−13 mol L−1 and a wide detection range (1.0 × 10−12–1.0 × 10−8 mol L−1 and 1.0 × 10−8 ∼ 1.0 × 10−3 mol L−1). Furthermore, this Cu-BTC/CN-NS based sensor has the characteristics of short detection time and easy operation. It has great potential applications in photoelectrochemical analysis.

Additional details

Identifiers

DOI
10.1016/j.electacta.2019.06.004;
PII
S0013468619311338;

Publishing Information

Journal Title
Electrochimica Acta
Journal Volume
317
Journal Page Range
p. 341-347
ISSN
0013-4686
CODEN
ELCAAV

Optional Information

Copyright
Copyright (c) 2019 Elsevier Ltd. All rights reserved.