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Electrochemical Determination of Catechol Using a Disposable Printed Electrode with Conductive Ink Based on Graphite and Carbon Black
Catechol (CT) is a phenolic compound widely used in various industrial sectors, but it is toxic; thus, there is a need for methods that aim to identify and quantify the existence of residues of this material in the environment. In this study a disposable printed electrochemical sensor was developed...
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Published in: | Analytica 2024-06, Vol.5 (2), p.250-262 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Catechol (CT) is a phenolic compound widely used in various industrial sectors, but it is toxic; thus, there is a need for methods that aim to identify and quantify the existence of residues of this material in the environment. In this study a disposable printed electrochemical sensor was developed as an effective alternative for determining CT in water samples. The electrode, called SPEC, was manufactured using the screen-printing method using polyethylene terephthalate (PET) as a support, in which a conductive ink based on carbonaceous materials was used to print the working and auxiliary electrodes and a silver/silver chloride of ink on the reference electrode. The optimal ratio for the conductive ink was 6.25% carbon black, 35.42% graphite, and 58.33% nail polish. The ink obtained was characterized by scanning electron microscopy (SEM). The assessment of the effect of pH on the redox process showed Nernstian behavior (0.057 V pH−1), indicating that the process involves the same number of protons and electrons. Under optimized conditions, with 0.2 mol L−1 acetate buffer at pH 5.0, and by square wave voltammetry, the sensor presented sensitivity values of 0.31 μA L μmol−1, a detection limit of 5.96 μmol L−1, and a quantification limit of 19.87 μmol L−1. The sensor was applied to determine CT in tap water samples, and the results showed recoveries between 97.95 and 100.17%. |
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ISSN: | 2673-4532 2673-4532 |
DOI: | 10.3390/analytica5020016 |