Analysis of fluid flow and reaction kinetics in a flow injection analysis biosensor

• Published in: Sensors and actuators. B, Chemical Chemical, 2006-04, Vol.114 (2), p.728-736
• Main Authors: Lammertyn, Jeroen, Verboven, Pieter, Veraverbeke, Els A., Vermeir, Steven, Irudayaraj, Joseph, Nicolaï, Bart M.
• Format: Article
• Language: English
• Subjects: Biosensor; Computational fluid dynamics; Electrochemistry; Microfluidics
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2005.06.050

This paper presents the development of a convection–diffusion-reaction model to simulate the behavior of a flow injection analysis (FIA) biosensor with respect to its design and operational parameters, such as flow rate, fluidic channel dimensions, flow cell geometry and volume of substrate and enzyme injected. The problem is described by the steady-state incompressible Navier–Stokes equations for fluid flow, and transient convection–diffusion-reaction equations for the different dissolved species. The enzyme–substrate interaction is described by Michaelis–Menten kinetics. The model was successfully validated on a glucose FIA-biosensor by comparing measured and simulated H 2O 2 concentration versus time profiles. Both peaks matched well, however, a slightly longer tailing was observed in the measurements due to simplifications in the geometrical model of the flow cell. The simulated and measured calibration curves for glucose closely matched in the range between 0 and 2.5 mM. The model was also applied to simulate the substrate, enzyme and product concentration profiles in the fluidic channels and the flow cell as a function of time. A parabolic velocity profile obtained from a pressure driven flow resulted in more peak broadening in the flow cell than a uniform velocity profile that occurs in packed column flow. Given the specified set of parameters, the concentration profiles of substrate, enzyme and product illustrated that not all substrate was converted into product when the flow reached the inlet of the flow cell, indicating a suboptimal sensitivity of the designed biosensor. This modeling approach has a high potential in the design and development of high accuracy FIA-biosensors, regardless of the chosen enzyme substrate system.