Designing Photonic Crystal Fibers incorporating the Surface Plasmon Resonance Phenomenon (PCF-SPR) has led to numerous interesting applications. This investigation presents an exceptionally responsive surface plasmon resonance sensor, seamlessly integrated into a dual-core photonic crystal fiber, specifically designed for low refractive index (RI) detection. The integration of a plasmonic material, namely silver (Ag), externally deposited on the fiber structure, facilitates real-time monitoring of variations in the refractive index of the surrounding medium. To ensure long-term functionality and prevent oxidation, a thin layer of titanium dioxide (TiO2) covers the silver coating. To optimize the sensor, five key design parameters, including pitch, air hole diameter, and silver thickness, are fine-tuned using the Taguchi L8(25) orthogonal array. The optimal results obtained present spectral and amplitude sensitivities that reach remarkable values of 10,000 nm/RIU and 235,882 RIU-1, respectively. In addition, Artificial Neural Network (ANN) optimization techniques, specifically Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), are used to predict a critical optical property of the sensor confinement loss (αloss). These predictions are derived from the same input structure parameters that are present in the full L32(25) design experiment. A genetic algorithm (GA) is then applied for optimization with the goal of maximizing the confinement loss. Our results highlight the effectiveness of training PSO artificial neural networks and demonstrate their ability to quickly and accurately predict results for unknown geometric dimensions, demonstrating their significant potential in this innovative context. The proposed sensor design can be used for various applications including pharmaceutical inspection and detection of low refractive index analytes.

The performance of the microfluidic biosensor with integrated flow confinement for the detection of SARS-CoV-2 was analyzed numerically by the finite element method. First, the numerical model was validated by comparison with experimental data reported in the literature. Then, the influence of some parameters on the binding reaction was investigated, such as the flux confinement rate and the amount of analyte supplied to the microchannel inlet. Results showed that flow confinement enhances the convection and diffusion transport of target analytes to the reaction surface and significantly reduces device detection time as well as target sample consumption.

This research presents a surface plasmon resonance (SPR) biosensor that incorporates a dual-side polished photonic crystal fiber (PCF). The biosensor uses an external gold (Au) coating as the plasmonic layer to identify changes in the refractive index (RI) of various analytes. Five critical design parameters, including the diameters of the air holes and the thicknesses of both the analyte and gold layers, were optimized using the Taguchi L8(25) orthogonal array method. The optimization resulted in outstanding spectral and amplitude sensitivities, achieving 1000 nm/RIU and 98.422 RIU−1, respectively. Additionally, Multiple Linear Regression (MLR) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) models were employed to predict the sensor’s confinement loss. The findings demonstrate the efficacy of artificial neural networks in providing quick and accurate predictions for various geometric configurations, showcasing their potential in this advanced application. The designed sensor can detect a wide range of analytes (RI range of 1.28–1.44), making it suitable for applications in organic chemical detection, pharmaceutical analysis, and biosensing.

In order to ensure the optimal functionality of biosensor devices across a diverse range of applications, it is crucial to accurately predict their detection times. This study delves into an in- depth exploration of the centrifugal and Coriolis effects that emerge due to the angular alignment and radial displacement of a rotating microfluidic biosensor specifically designed for detecting complex reactive proteins (CRPs). To address this challenge, we introduce an innovative hybrid model known as PSO-ANN, which combines the power of an artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm. This pioneering model is aimed at predicting the response time of a lab-on-a-CD device by utilizing critical input variables, such as rotational velocity ( omega ), biosensor position ( XS ), angular alignment ( theta ), and radial displacement ( RD ). Our research also involves a comprehensive performance evaluation of the PSO-ANN model, comparing it to an alternative multilayer perceptron (MLP) model, ANN. This evaluation seeks to assess the impact of these models on improving the accuracy and reliability of predictions related to biosensor detection times, with potential applications spanning a wide spectrum of practical fields. Key metrics utilized in our evaluation include mean absolute error (MAE), root mean square error (RMSE), variance accounted for (VAF), and coefficient of determination ( R-2 ). The results highlight the remarkable predictive capabilities of the hybrid PSO-ANN model. This research carries significant implications for enhancing the performance of biosensor devices and advancing their utility in various domains, promising advancements in the field of biosensor technology.

Optimizing the performance parameters, including the detection time, of microfluidic biosensors is crucial for enhancing their efficiency and accuracy. In this study, the detection time (T_R) was optimized by considering control parameters, such as the width of the annular reaction surface ( δ R ), the applied voltage ( V_rms ), the inlet concentration ( C_0 ), the inlet average velocity ( U_ave ), and the presence of an obstacle. Taguchi’s method was employed to design an L8(25) orthogonal network, enabling optimal parameter settings. The performance parameters were optimized using the signal-to-noise (S/N) ratio curve, and numerical predictive models were developed using multiple linear regression (MLR), quadratic regression, and multi-layer perceptron artificial neural network (MLP-ANN). The theoretical analysis resulted in optimized design parameters, with δ R=5μ m , V_rms=5V , C_0=10pmol/m^3 , U_ave=0.5mm/s , and the presence of an obstacle, leading to a minimum response time of 10325 s (2.87 h). Among the key parameters, U_ave had the highest contribution (62 U_ave and the minimal contribution of C0 in reducing the response time. Additionally, the MLP-ANN model demonstrates outstanding prediction accuracy for the response time of the new microfluidic biosensor design.

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

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 α_3β_3X_2 that corresponds to α =90^∘ , β =25 and X = 40 µm. Analysis of variance (ANOVA) shows that the position of the confinement channel (62

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.

In this study, Taguchi's approach was used to optimize the performance of an electrothermal microfluidic biosensor with a new shape of the reaction surface used for the rapid detection of novel severe acute respiratory syndrome coronavirus-2. An orthogonal table L9 of four critical parameters at three levels each, namely the fluid inlet velocity, the voltage applied between the electrodes, the analyte concentration at the inlet and the constant adsorption, was designed. Signal to noise ratio (S/N) combined with analysis of variance were used to reach the optimal levels of process parameters and to demonstrate the percentage contributions of each of the four controllable parameters, in terms of improved device response time

To contribute to the fight versus the coronavirus disease 2019, 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 a two-dimensional finite element simulation on the binding kinetics of SARS-CoV-2 S protein 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. The results showed that the detection time can be improved under the electrothermal effect. The effect of certain design parameters concerning the reaction surface, such as its length as well as its position on the top wall of the microchannel, on the biosensor efficiency were also presented. Results showed that the decrease 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, which is opposite the electrodes, decreases the performance of the biosensor.

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 L8(2^7) orthogonal array of seven critical parameters—Reynolds number (Re), Damköhler number (Da), relative adsorption capacity ( σ ), equilibrium dissociation constant (K D ), 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 = 10 5 , 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 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.