In this research, Taguchi's method was employed to optimize the performance of a microfluidic biosensor with an integrated flow confinement for rapid detection of the SARS-CoV-2. The finite element method was used to solve the physical model which has been first validated by comparison with experimental results. The novelty of this study is the use of the Taguchi approach in the optimization analysis. An L8 2 7 orthogonal array of seven critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ ), equilibrium dissociation constant (K D ), Schmidt number (Sc), confinement coefficient (α) and dimensionless confinement position (X), with two levels was designed. Analysis of variance (ANOVA) methods are also used to calculate the contribution of each parameter. The optimal combination of these key parameters was Re 10 -2 , Da 1000, σ 0.5, K D 5, Sc 10 5 , α 2 and X 2 to achieve the lowest dimensionless response time (0.11). Among the all-optimization factors, the relative adsorption capacity (σ ) has the highest contribution (37%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (7%).

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The performance of microfluidic biosensor of the SARS-Cov-2 was numerically analyzed through finite element method. The calculation results have been validated with comparison with experimental data reported in the literature. The novelty of this study is the use of the Taguchi method in the optimization analysis, and an L8(2 5 ) orthogonal table of five critical parameters-Reynolds number (Re), Damko ¨hler number (Da), relative adsorption capacity (r), equilibrium dissociation constant (K D ), and Schmidt number (Sc), with two levels was designed. ANOVA methods are used to obtain the significance of key parameters. The optimal combination of the key parameters is Re = 10 -2 , Da = 1000, r = 0.2, K D = 5, and Sc 10 4 to achieve the minimum response time (0.15). Among the selected key parameters, the relative adsorption capacity (r) has the highest contribution (42.17%) to the reduction of the response time, while the Schmidt number (Sc) has the lowest contribution (5.19%). The presented simulation results are useful in designing microfluidic biosensors in order to reduce their response time.

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