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
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.
ABSTRACT
The coronavirus (COVID-19) pandemic has put the entire world at risk and caused an economic downturn in most countries. This work provided theoretical insight into a novel fiber optic-based plasmonic biosensor that can be used for sensitive detection of SARS-CoV-2. The aim was always to achieve reliable, sensitive, and reproducible detection. The proposed configuration is based on Ag-Au alloy nanoparticle films covered with a layer of graphene which promotes the molecular adsorption and a thiol-tethered DNA layer as a ligand. Here, the combination of two recent approaches in a single configuration is very promising and can only lead to considerable improvement. We have theoretically analyzed the sensor performance in terms of sensitivity and resolution. To highlight the importance of the new configuration, a comparison was made with two other sensors. One is based on gold nanoparticles incorporated into a host medium; the other is composed of a bimetallic Ag-Au layer in the massive state. The numerical results obtained have been validated and show that the proposed configuration offers better sensitivity (7100 nm\RIU) and good resolution (figure of merit; FOM = 38.88 RIU - 1 and signal-to-noise ratio; SNR = 0.388). In addition, a parametric study was performed such as the graphene layers' number and the size of the nanoparticles.
ABSTRACT
In this study, we performed 3D finite element simulations on the binding reaction kinetics of SARS-CoV-2 S protein (target analyte) and its corresponding immobilized antibody (ligand) in a heterogeneous microfluidic immunoassay. Two types of biosensors with two different shapes and geometries of the reaction surface and electrodes were studied. Alternating current electrothermal (ACET) force was applied to improve the binding efficiency of the biomolecular pairs by accelerating the transport of analytes to the binding surface. The ACET force stirs the flow field, thereby reducing the thickness of the diffusion boundary layer, often developed on the reaction surface due to the slow flow velocity, low analyte diffusion coefficient, and surface reaction high rate. The results showed that the detection time of one of the biosensors can be improved by 69% under an applied voltage of 10 Vrms and an operating frequency of 100 kHz. Certain control factors such as the thermal boundary conditions as well as the electrical conductivity of the buffer solution were analyzed in order to find the appropriate values to improve the efficiency of the biosensor.
ABSTRACT
The rapid spread and quick transmission of the new ongoing pandemic coronavirus disease 2019 (COVID-19) has urged the scientific community to looking for strong technology to understand its pathogenicity, transmission, and infectivity, which helps in the development of effective vaccines and therapies. Furthermore, there was a great effort to improve the performance of biosensors so that they can detect the pathogenic virus quickly, in reliable and precise way. In this context, we propose a numerical simulation to highlight the important role of the design parameters that can significantly improve the performance of the biosensor, in particular the sensitivity as well as the detection limit. Applied alternating current electrothermal (ACET) force can generate swirling patterns in the fluid within the microfluidic channel, which improve the transport of target molecule toward the reaction surface and, thus, enhance the response time of the biosensor. In this work, the ACET effect on the SARS-CoV-2 S protein binding reaction kinetics and on the detection time of the biosensor was analyzed. Appropriate choice of electrodes location on the walls of the microchannel and suitable values of the dissociation and association rates of the binding reaction, while maintaining the same affinity, with and without ACET effect, are also, discussed to enhance the total performance of the biosensor and reduce its response time. The two-dimensional equations system is solved by the finite element approach. The best performance of the biosensor is obtained in the case where the response time decreased by 61% with AC applying voltage.