Molecularly imprinted polymer coated Mn-doped ZnS quantum dots embedded in a metal-organic framework as a probe for selective room temperature phosphorescence detection of chlorpyrifos.
RSC Adv
; 11(45): 27845-27854, 2021 Aug 16.
Article
em En
| MEDLINE
| ID: mdl-35480778
As one of the most widely used organophosphorus pesticides, chlorpyrifos (CPF) is toxic to humans. However, the rapid, effective and sensitive detection of CPF is still a challenge. In this paper, a novel molecularly imprinted phosphorescent sensor with a core-shell structure (Mn:ZnS QDs@ZIF-8@MIP) using Mn:ZnS quantum dots (QDs) as phosphorescent emitters was prepared for the highly sensitive and selective detection of CPF, and a simple and rapid room-temperature phosphorescence (RTP) detection method for CPF was proposed. For the prepared Mn:ZnS QDs@ZIF-8@MIP, Mn:ZnS QDs had good phosphorescence emission characteristics, ZIF-8 as support materials was used to improve the dispersibility of Mn:ZnS QDs, and molecularly imprinted polymer (MIP) on the surface of ZIF-8 was used to improve the selectivity of Mn:ZnS QDs for CPF. Under the optimal response conditions, the RTP intensity of Mn:ZnS QDs@ZIF-8@MIP showed a rapid response to CPF (less than 5 min), the RTP intensity ratio of P 0/P had a good linear relationship with the concentration of CPF in the range of 0-80 µM, and the detection limit of this method was 0.89 µM with the correlation coefficient of 0.99. Moreover, this simple and rapid method has been successfully used to detect CPF in real water samples with satisfactory results, and the recoveries ranged from 92% to 105% with a relative standard deviation of less than 1%. This method combines the advantages of phosphorescence emission and molecular imprinting, and greatly reduces the potential interferences of competitive substances, background fluorescence and scattered light, which opens up a broad prospect for the highly sensitive and selective detection of pollutants in water based on molecularly imprinted phosphorescent sensors.
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article