Non-enzymatic electrochemical sensors using Polyaniline:Metal orotate nanocomposites for selective dopamine and glucose detection: Predicting sensor performance with machine learning algorithms

Abstract

In this study, we developed novel non-enzymatic electrochemical biosensors, capitalizing on the capabilities of orotate metal complexes encompassing Co(II), Cu(II), Ni(II), and Zn(II) cations coordinated within polyaniline (PANI) nanocomposites (NCs). Our approach involved the meticulous crystallization of metal cation complexes from solution, utilizing orotic acid as a ligand. Through the utilization of a cost-efficient and uncomplicated sonication technique, we synthesized PANI:Co(II), PANI:Ni(II), PANI:Cu(II), and PANI:Zn(II) orotate NCs. Our results demonstrated that the PANI:Zn(II) orotate NCs-based sensor exhibited the highest sensitivity, recording 99.25 μA cm⁻² μM⁻¹ for dopamine detection, with a remarkable limit of detection (LOD) of 0.74 μM. Furthermore, the sensor effectively detected glucose with a sensitivity of 24.39 μAcm⁻²mM⁻¹ and a LOD of 1.89 mM. Importantly, the sensor displayed exceptional selectivity towards dopamine. To further enhance the sensor performance, machine learning algorithms were applied to analyze and predict the sensor's output. Models such as Linear Regression and Artificial Neural Networks (ANN) were employed to interpret the electrochemical results and predict performance metrics like sensitivity and selectivity. The outcomes of our sensor assessments underscore the potential of the PANI:Zn(II) orotate NCs as a robust platform for dopamine detection applications. Our findings provide insights for improving PANI: Zn(II) orotate NCs in biosensing. PANI:metal orotate NCs function as semiconductor materials, with sensor sensitivity closely tied to their conductance, conductivity, and impedance.