Numerical optimization of microfluidic biosensor detection time for the SARS-CoV-2 using the Taguchi method

Abstract

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.

Optimizing PCF-SPR sensor design through Taguchi approach, machine learning, and genetic algorithms

“…The Taguchi method is a robust optimization technique that has gained widespread recognition in various fields for its ability to systematically optimize multiple parameters and their respective levels while minimizing the need for extensive experimentation 28 , 30 , 42 , 43 . When applied to the task of optimizing the structural parameters of the PCF sensor, which include the diameters of the air holes (d 1 , d 2 , d 3 ), the pitch (Λ), and the thickness of the silver (t Ag ), the Taguchi method provides an efficient approach.…”

“…Factors are the variables or parameters that can affect the outcome of a process or product, and levels represent the different settings or values that each factor can take. The choice of factors and their levels is crucial for conducting efficient experiments while capturing the effects of interest 28 , 30 , 42 , 43 . By utilizing the L 8 (2 5 ) orthogonal array, researchers can systematically explore the effects of multiple factors on a process or product with a relatively small number of experiments.…”