Computational design and multivariate statistical analysis for electrochemical sensing platform of iron oxide nanoparticles in sensitive detection of anti-inflammatory drug diclofenac in biological fluids

Abstract In this study, a multivariate statistical analysis is developed to predict and optimized all effective parameters on determination of diclofenac (DCF). For achieving the purpose, a sensitive electrochemical detector for DCF is designed. The sensor is fabricated by iron oxide nanoparticles (IONPs). Some spectroscopic and microscopic techniques are applied to characterize of IONPs. For designing the modified electrode, IONPs is incorporate with CPE. The electrochemical characterizations are performed using different voltammetric techniques. The proposed nano-structured electrode is used for determination of DCF by differential pulse voltammetry. A multivariate optimization is used for achieving the best electrochemical response of DCF. By the optimization strategy, the nano-structured sensor has the advantages of low detection limit (2.45 nM), one linear range (0.01–100.0 μM), good sensitivity, long-term stability and repeatability for DCF determination. All results indicate that IONPs is a good candidate for DCF detection in biological fluids.