Smartphone enabled machine learning approach assisted copper (II) quantification and opto-electrochemical explosive recognition by Aldazine-functionalized chemobiosensor

Abstract

An Aldazine-based optoelectrochemical sensor, BMH (1-(quinolin-4-ylmethylene)hydrazono)methyl)naphthalen-2-ol) has been introduced herein for selective detection of aqueous copper (Cu2+) and 2, 4, 6-Trinitrophenol (TNP) at an ultra-low level detection limit (0.09 ppm for Cu2+ and 0.019 ppm for TNP). Multichannel recognition aptitude of the chemosensor (BMH) towards both Cu2+ and TNP along with bountiful practical applications ascertained it as an innovative one in the environmental and biomedical domains. BMH can detect Cu2+ in water, fetal bovine serum, and human urine samples, while explosive TNP can be identified in water, soil, and matches powder. The intracellular Cu2+ and TNP recognition efficiencies of BMH have been investigated in human lung cancer cell lines (A459). The hassle-free smartphone ensemble machine learning approach for Cu2+quantification has been introduced which would certainly be a significant addition in the domain of water quality analysis. Moreover, the ethylenediaminetetraacetic acid (EDTA) mediated reversibility of the probe could serve as a logic gate imitating electrical circuitry.

Keywords

• Aldazine based chemobiosensor
• Optoelectrochemical sensing
• Copper detection and explosive detection
• Machine learning
• Bio-imaging