Multiphysics-guided design of ZIF-67/MWCNT-modified electrodes for highly selective electrochemical detection of sunset yellow in complex food matrices

Abstract

The development of reliable sensing platforms for synthetic food additives remains a critical challenge due to severe matrix interferences that limit selectivity and analytical accuracy. In this work, a multiphysics-guided framework is employed to design a ZIF-67/MWCNT-modified glassy carbon electrode (GCE) for the highly selective electrochemical detection of sunset yellow (SY) in complex food matrices. By integrating experimental electrochemical analysis with COMSOL-based modeling of mass transport, adsorption dynamics, charge transfer, and thermal effects, this study provides a mechanistic basis for material–analyte interactions that govern sensor performance. The ZIF-67/MWCNT hybrid exhibits synergistic surface chemistry, where π–π stacking between the azo-aromatic structure of SY and the graphitic domains of MWCNTs, together with electrostatic interactions with Co²⁺ centers in ZIF-67, yields a high adsorption constant (K_ads = 5.41 × 10⁴ m³ mol⁻¹) and a dominant surface flux (3.47 × 10⁻⁷ mol m⁻² s⁻¹), surpassing those of common interferents. The optimized electrode delivers a steady-state current density of 5.22 µA m⁻² at pH 7 and a 5 µm composite layer, while maintaining negligible faradaic contributions from ascorbic acid, citric acid, aspartame, and acesulfame potassium. Parametric simulations reveal robust performance under thermal variations (298–328 K), minimal sensitivity to electrolyte disturbances, and a direct correlation between surface heterogeneity and current attenuation. Model validation against experimental electrochemical impedance spectroscopy yields a low RMSE (0.0621), confirming predictive accuracy. These findings demonstrate how multiphysics analysis can rationally guide electrode engineering, offering a powerful design strategy for next-generation electrochemical sensors. The proposed platform provides a selective, sensitive, and scalable solution for trace-level SY detection, underscoring its relevance for food safety monitoring and real-sample analysis.