<p>Aptamer-based biosensors, commonly referred to as aptasensors, represent a promising class of diagnostic tools that have garnered significant attention over the past decade. A novel approach in the design of these biosensors involves the incorporation of nanozymes as enzyme-like mimetics, with iron oxide nanoparticles (IONPs) emerging as particularly effective peroxidase mimics due to their ability to facilitate Fenton-like reactions. Aptamers serve dual roles in this context: they act as recognition elements in Fenton-like activity-based sensors and are crucial for the surface functionalization of nanoparticles. The integration of aptamers enhances the performance and selectivity of biosensors by minimizing unwanted background signals associated with colorimetric and fluorescent measurements, thus improving detection limits. This study investigates the impact of buffer optimization and various oligo-aptamer dependent variables on the design of Fenton-like reaction-based aptasensors, utilizing zeta potential measurements as a diagnostic tool. Key factors explored include buffer types and concentrations, aptamer length and sequence, and incubation times. Optimizing these parameters is expected to significantly influence the efficacy of Fenton-like aptasensors. The findings suggest that zeta potential measurement is a valuable technique for real-time monitoring of the surface coating conditions of nanozymes with aptamers, facilitating the optimization of multiple parameters critical for developing effective Fenton-like reaction-based aptasensors.</p>