ABSTRACT
COVID-19 is a pandemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus is mainly spread by droplets, respiratory secretions, and direct contact. Caused by the huge spread of the COVID-19 epidemic, research is focused on the study of biosensors as it presents a rapid solution for reducing incidents and fatality rates. In this paper, a microchip flow confinement method for the rapid transport of small sample volumes to sensor surfaces is optimized in terms of the confinement coefficient β, the position of the confinement flow X, and its inclination α relative to the main channel. A numerical simulation based on two-dimensional Navier–Stokes equations has been used. Taguchi's L9(33) orthogonal array was adopted to design the numerical assays taking into account the confining flow parameters (α, β, and X) on the response time of microfluidic biosensors. Analyzing the signal-to-noise ratio allowed us to determine the most effective combinations of control parameters for reducing the response time. The contribution of the control factors to the detection time was determined via analysis of variance (ANOVA). Numerical predictive models using multiple linear regression (MLR) and an artificial neural network (ANN) were developed to accurately predict microfluidic biosensor response time. This study concludes that the best combination of control factors is Graphic
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(25) orthogonal table of five critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ), equilibrium dissociation constant (KD), 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, σ = 0.2, KD = 5, and Sc 104 to achieve the minimum response time (0.15). Among the selected key parameters, the relative adsorption capacity (σ) 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.
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(25) orthogonal table of five critical parameters—Reynolds number (Re), Damköhler number (Da), relative adsorption capacity (σ), equilibrium dissociation constant (KD), 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, σ = 0.2, KD = 5, and Sc 104 to achieve the minimum response time (0.15). Among the selected key parameters, the relative adsorption capacity (σ) 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.
ABSTRACT
Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previous studies have shown that the kinetic binding reaction of the target antigen is strongly affected by process parameters. The purpose of this research was to optimize the performance of a microfluidic biosensor using two different approaches: Taguchi optimization and artificial neural network (ANN) optimization. Taguchi L8(25) orthogonal array involving eight groups of experiments for five key parameters, which are microchannel shape, biosensor position, applied alternating current voltage, adsorption constant, and average inlet flow velocity, at two levels each, are performed to minimize the detection time of a biosensor excited by an alternating current electrothermal force. Signal to noise ratio ( S / N ) and analysis of variance were used to reach the optimal levels of process parameters and to demonstrate their percentage contributions, in terms of improved device response time. The principal results of this study showed that the Taguchi method was able to identify that the kinetic adsorption rate is the most influential parameter at 93% contribution, and the reaction surface position is the least influential parameter at 0.07% contribution. Also, the ANN model was able to accurately predict the optimal input values with a very low prediction error. Overall, the major conclusion of this study is both the Taguchi and ANN approaches can be effectively utilized to optimize the performance of a microfluidic biosensor. These advances have the potential to revolutionize the field of biosensing.
ABSTRACT
Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previous studies have shown that the kinetic binding reaction of the target antigen is strongly affected by process parameters. The purpose of this research was to optimize the performance of a microfluidic biosensor using two different approaches: Taguchi optimization and artificial neural network (ANN) optimization. Taguchi L8(25) orthogonal array involving eight groups of experiments for five key parameters, which are microchannel shape, biosensor position, applied alternating current voltage, adsorption constant, and average inlet flow velocity, at two levels each, are performed to minimize the detection time of a biosensor excited by an alternating current electrothermal force. Signal to noise ratio (
ABSTRACT
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 L 8 2 7 orthogonal array of seven critical parameters-Reynolds number (Re), Damköhler number (Da), relative adsorption capacity ( σ ), equilibrium dissociation constant (KD), 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 = 105, 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%).
ABSTRACT
To combat the coronavirus disease 2019 (COVID-19), great efforts have been made by scientists around the world to improve the performance of detection devices so that they can efficiently and quickly detect the virus responsible for this disease. In this context we performed 2D finite element simulation on the kinetics of SARS-CoV-2 S protein binding reaction of a biosensor using the alternating current electrothermal (ACET) effect. The ACET flow can produce vortex patterns, thereby improving the transportation of the target analyte to the binding surface and thus enhancing the performance of the biosensor. Optimization of some design parameters concerning the microchannel height and the reaction surface, such as its length as well as its position on the top wall of the microchannel, in order to improve the biosensor efficiency, was studied. The results revealed that the detection time can be improved by 55% with an applied voltage of 10 V rms and an operating frequency of 150 kHz and that the decrease in the height of the microchannel and in the length of the binding surface can lead to an increase in the rate of the binding reaction and therefore decrease the biosensor response time. Also, moving the sensitive surface from an optimal position, located in front of the electrodes, decreases the performance of the device.