The adsorption effect on chemical kinetics at the reaction surface in a microfluidic channel of a biosensor for the SARS-Cov-2 detection

Abstract

The global impact of the Severe Acute Respiratory Syndrome coronavirus has brought about significant changes in the lives of people worldwide, affecting societies and economies. Consequently, there is a growing need to explore the field of research focused on developing wearable sensors capable of continuously monitoring the presence of viruses in the environment. In this particular study, we simulated the binding reaction of the SARS-CoV-2 protein within a microchannel biosensor. One challenge encountered in this setup is the transportation of the analyte to the biosensor's reaction surface. The limited mass transport leads to the creation of a diffusion boundary layer, impeding the overall kinetic reaction. To improve the biosensor performance by enhancing transport, we thoroughly examined the impact of adsorption phenomena on the chemical kinetics. We compared the kinetic response of the biosensor in both cases, i.e. taking into account the adsorption and neglecting it in the numerical model. A parametric study concerning the trapping coefficient was carried out such as the quantity adsorbed at the saturation of a layer, the concentration at half saturation, and the relaxation time. In addition, we investigated the effect of the length of the reaction surface and the inlet velocity of the fluid. The main novelty in this work is to highlight the importance of adsorption on the kinetics of the binding reaction of the SARS-CoV-2-S protein which results in obtaining a complete simulation of the entire process of SARS-Cov-2 binding reaction. Therefore, the adsorption mechanism was described qualitatively and quantitatively using a kinetic model containing a trapping or source term. The obtained results demonstrate the successful integration of adsorption into the kinetic reaction process. The best biosensor performance is achieved in the first case. This theoretical analysis shows that the studied parameters significantly enhance the biosensor performance, especially the sensitivity as well as the response time.