This research utilises Artificial Intelligence (AI) to enhance electrochemical peak resolution and lower detection limits in voltammetric analysis, focusing on complex, multiplex real matrices analyses. The study investigated the quinone family, hydroquinone, benzoquinone, and catechol analysed individually and in mixtures using cyclic and square wave voltammetry. The ferrocyanide/ferricyanide redox couple was included as a standard redox probe to provide a reference for method validation.

Cancelliere, R., Molinara, M., Licheri, A., Maffucci, A., Micheli, L. (2025). Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses. DIGITAL DISCOVERY, 2025(4), 338-342 [10.1039/D4DD00318G].

Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses

Rocco Cancelliere
;
Antonio Licheri;Laura Micheli
2025-01-01

Abstract

This research utilises Artificial Intelligence (AI) to enhance electrochemical peak resolution and lower detection limits in voltammetric analysis, focusing on complex, multiplex real matrices analyses. The study investigated the quinone family, hydroquinone, benzoquinone, and catechol analysed individually and in mixtures using cyclic and square wave voltammetry. The ferrocyanide/ferricyanide redox couple was included as a standard redox probe to provide a reference for method validation.
2025
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore CHIM/01
Settore ING-IND/01
Settore CHEM-01/A - Chimica analitica
English
Con Impact Factor ISI
The research leading to these results has received funding from the Project “SENS-AI, Environmental Sensing with Articial Intelligence” CUP H53D23000520006, funded by EU in NextGenerationEU plan through the Italian “Bando Prin 2022– D.D. 104 del 02-02-202200 by MUR (2023–2025).
Cancelliere, R., Molinara, M., Licheri, A., Maffucci, A., Micheli, L. (2025). Artificial intelligence-assisted electrochemical sensors for qualitative and semi-quantitative multiplexed analyses. DIGITAL DISCOVERY, 2025(4), 338-342 [10.1039/D4DD00318G].
Cancelliere, R; Molinara, M; Licheri, A; Maffucci, A; Micheli, L
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/420919
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