Numerical simulation and optimization of AC electrothermal microfluidic biosensor for COVID-19 detection through
Sameh Kaziz1,2, Imed Ben Romdhane3, Fraj Echouchene3,4
1Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia.
European Physical Journal Plus
|February 6, 2023
Summary
Optimizing microfluidic biosensors for rapid SARS-CoV-2 detection is crucial. Both Taguchi and artificial neural network (ANN) methods effectively improved biosensor performance by identifying key parameters like kinetic adsorption rate.
Area of Science:
- Biomedical Engineering
- Biosensor Technology
- Nanotechnology
Background:
- Microfluidic biosensors are vital for rapid detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
- The kinetic binding reaction of target antigens is significantly influenced by various process parameters.
- Optimizing these parameters is essential for enhancing biosensor efficiency and response time.
Purpose of the Study:
- To optimize the performance of a microfluidic biosensor for rapid SARS-CoV-2 detection.
- To compare the effectiveness of Taguchi optimization and artificial neural network (ANN) optimization approaches.
- To identify key process parameters influencing biosensor response time.
Main Methods:
- Utilized Taguchi L8(2^5) orthogonal array for five parameters: microchannel shape, biosensor position, AC voltage, adsorption constant, and flow velocity.
- Employed signal-to-noise ratio and analysis of variance to determine optimal parameter levels and contributions.
- Developed an artificial neural network (ANN) model for predicting optimal input values and validating results.
Main Results:
- Taguchi optimization identified kinetic adsorption rate as the most influential parameter (93% contribution).
- Reaction surface position was found to be the least influential parameter (0.07% contribution).
- The ANN model accurately predicted optimal values with minimal error, confirming Taguchi's findings.
Conclusions:
- Both Taguchi and ANN optimization methods are effective for enhancing microfluidic biosensor performance.
- This optimization significantly reduces detection time for biosensors, crucial for rapid diagnostics.
- These advancements hold potential for revolutionizing biosensing applications, particularly in infectious disease detection.
