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

Ibrahim Ben Mariem1, Sameh Kaziz2,3, Maissa Belkhiria1

  • 1Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia.

Indian Journal of Physics and Proceedings of the Indian Association for the Cultivation of Science (2004)
|June 26, 2023
PubMed
Summary

This study optimized microfluidic biosensors for SARS-CoV-2 detection using the Taguchi method. Relative adsorption capacity significantly reduces response time, improving biosensor design.

Keywords:
ANOVABiosensorDetection timeSARS-CoV-2Taguchi method

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Area of Science:

  • Biomedical Engineering
  • Chemical Engineering

Background:

  • Microfluidic biosensors are crucial for rapid pathogen detection, including SARS-CoV-2.
  • Optimizing sensor performance, particularly response time, is essential for effective diagnostics.

Purpose of the Study:

  • To numerically analyze and optimize the performance of microfluidic biosensors for SARS-CoV-2 detection.
  • To identify critical parameters influencing sensor response time and determine optimal settings.

Main Methods:

  • Finite element method (FEM) for numerical analysis of biosensor performance.
  • Taguchi method with an L8(2^5) orthogonal array to optimize five key parameters: Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ), equilibrium dissociation constant (Kd), and Schmidt number (Sc).
  • Analysis of Variance (ANOVA) to determine the significance of each parameter.

Main Results:

  • The optimal parameter combination (Re=10-2, Da=1000, σ=0.2, Kd=5, Sc=104) achieved a minimum response time of 0.15.
  • Relative adsorption capacity (σ) was the most significant parameter, contributing 42.17% to response time reduction.
  • Schmidt number (Sc) had the least impact, contributing 5.19%.

Conclusions:

  • The study successfully optimized microfluidic biosensor design for faster SARS-CoV-2 detection.
  • Findings provide valuable insights for engineering faster and more efficient microfluidic biosensing devices.