CSA/PANI and MWCNT/PANI Composites as Electrode Platforms for Reliable Creatinine Biosensing

Abstract

Creatinine is a crucial biomarker for monitoring kidney function, as abnormal levels can indicate severe renal disorders like kidney failure. Therefore, there is a significant need for developing sensitive and reliable biosensors for the early detection of creatinine level fluctuations. This work presents the development and comparative analysis of two electrochemical biosensors for creatinine detection, one based on a camphorsulfonic acid (CSA)-doped polyaniline (PANI) composite and the other on a multi-walled carbon nanotube (MWCNT)/PANI composite. Both platforms were successfully modified and characterized using scanning electron microscope, cyclic voltammetry, and electrochemical impedance spectroscopy, confirming the immobilization of the enzymes and enhanced electrochemical properties. Creatinine detection was performed using differential pulse voltammetry. The CSA/PANI/Enzyme biosensor demonstrated a linear range of 2-140 & micro;M with a low limit of detection (LOD) of 1.76 & micro;M. In contrast, the MWCNT/PANI/Enzyme sensor showed a linear range of 25-180 & micro;M and a higher LOD of 13.1 & micro;M. Repeatability tests demonstrated that the CSA/PANI system exhibited a lower relative standard deviation (2.47%) compared to the MWCNT/PANI sensor (RSD = 21.6%), indicating more stable analytical performance. In addition, both biosensor platforms demonstrated acceptable inter-electrode reproducibility using independently fabricated electrodes. Both biosensors exhibited good resistance to common interfering molecules in selectivity studies. Furthermore, both biosensors demonstrated good stability, retaining 80% (CSA/PANI) and 75% (MWCNT/PANI) of their initial current responses after 15 days. The practicality of the sensors was confirmed by testing real human serum samples. Overall, the CSA/PANI/Enzyme platform proved superior in sensitivity, repeatability, stability, and applicability in real biological samples, making it a promising and scalable platform for clinical diagnostics.