Issue 33, 2024

An O-vanillin scaffold as a selective chemosensor of PO43− and the application of neural network based soft computing to predict machine learning outcomes

Abstract

O-Vanillin derived Schiff base 1-[(E)-(2-hydroxy-3-methoxybenzylidene) amino]-4-methylthiosemicarbazone (VCOH) has been synthesized for colorimetric and fluorescence chemosensors towards PO43− ions. A fluorescence ‘turn-on’ sensing mechanism of VCOH towards PO43− ions has been explained due to emission from the VCO ion formed upon transfer of the phenolic proton of VCOH to a PO43− ion. The 1 : 1 stoichiometry between the VCOH probe and PO43− ion is confirmed by Job's plot based on UV-vis titration. The limit of detection (LOD) of VCOH towards PO43− ions is found to be 0.49 nM. The PO43− ion sensing property of probe VCOH has been applied to prepare portable paper strips and for the analysis of real water samples. Fluorescence ‘turn-on’ and ‘turn-off’ responses of VCOH towards PO43−and H+ respectively have been used to construct a molecular logic gate. Fluorescence based sensing studies in which the concentration of analytes is adjusted over a broad range can be both laborious and expensive. In order to address these challenges, we have utilized various soft computing methods, including artificial neural networks (ANN), fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS), to appropriately model the ‘turn-on’ and ‘turn-off’ behaviors of the VCOH probe upon addition of PO43− and H+ respectively as well as to predict the experimental sensing data.

Graphical abstract: An O-vanillin scaffold as a selective chemosensor of PO43− and the application of neural network based soft computing to predict machine learning outcomes

Supplementary files

Article information

Article type
Paper
Submitted
28 May 2024
Accepted
19 Jul 2024
First published
24 Jul 2024

New J. Chem., 2024,48, 14642-14654

An O-vanillin scaffold as a selective chemosensor of PO43− and the application of neural network based soft computing to predict machine learning outcomes

N. Mudi, S. S. Samanta, S. Mandal, S. Barman, H. Beg and A. Misra, New J. Chem., 2024, 48, 14642 DOI: 10.1039/D4NJ02462A

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