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Kaziz S, Echouchene F, Gazzah MH. Optimizing PCF-SPR sensor design through Taguchi approach, machine learning, and genetic algorithms. Sci Rep 2024; 14:7837. [PMID: 38570590 PMCID: PMC10991260 DOI: 10.1038/s41598-024-55817-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/28/2024] [Indexed: 04/05/2024] Open
Abstract
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.
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Affiliation(s)
- Sameh Kaziz
- NANOMISENE Laboratory, LR16CRMN01, Centre for Research on Microelectronics and Nanotechnology (CRMN) of Sousse Technopole, Sahloul, B.P.334, 4054, Sousse, Tunisia.
| | - Fraj Echouchene
- Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019, Monastir, Tunisia
| | - Mohamed Hichem Gazzah
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019, Monastir, Tunisia
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Ben Mariem I, Kaziz S, Belkhiria M, Echouchene F, Belmabrouk H. Numerical optimization of microfluidic biosensor detection time for the SARS-CoV-2 using the Taguchi method. INDIAN JOURNAL OF PHYSICS AND PROCEEDINGS OF THE INDIAN ASSOCIATION FOR THE CULTIVATION OF SCIENCE (2004) 2023; 97:1-8. [PMID: 37361718 PMCID: PMC10008012 DOI: 10.1007/s12648-023-02632-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/06/2023] [Indexed: 05/20/2023]
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.
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Affiliation(s)
- Ibrahim Ben Mariem
- Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Sameh Kaziz
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
- Higher National Engineering School of Tunis, Taha Hussein Montfleury Boulevard, University of Tunis, 1008 Tunis, Tunisia
| | - Maissa Belkhiria
- Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Fraj Echouchene
- Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Higher Institute of Applied Sciences and Technology of Soussse, University of Sousse, Sousse, Tunisia
| | - Hafedh Belmabrouk
- Electronic and Microelectronics Lab, Department of Physics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Department of Physics, College of Science at Zulfi, Majmaah University, Al Majma’ah, 11952 Saudi Arabia
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Kaziz S, Ben Romdhane I, Echouchene F, Gazzah MH. Numerical simulation and optimization of AC electrothermal microfluidic biosensor for COVID-19 detection through Taguchi method and artificial network. EUROPEAN PHYSICAL JOURNAL PLUS 2023; 138:96. [PMID: 36741917 PMCID: PMC9884486 DOI: 10.1140/epjp/s13360-023-03712-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/17/2023] [Indexed: 05/20/2023]
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.
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Affiliation(s)
- Sameh Kaziz
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
- Higher National Engineering School of Tunis, Taha Hussein Montfleury Boulevard, University of Tunis, 1008 Tunis, Tunisia
| | - Imed Ben Romdhane
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
| | - Fraj Echouchene
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
- Higher Institute of Applied Sciences and Technology of Soussse, University of Sousse, Sousse, Tunisia
| | - Mohamed Hichem Gazzah
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
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Ben Romdhane I, Jemmali A, Kaziz S, Echouchene F, Alshahrani T, Belmabrouk H. Taguchi method: artificial neural network approach for the optimization of high-efficiency microfluidic biosensor for COVID-19. EUROPEAN PHYSICAL JOURNAL PLUS 2023; 138:359. [PMID: 37131342 PMCID: PMC10132959 DOI: 10.1140/epjp/s13360-023-03988-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/12/2023] [Indexed: 05/04/2023]
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 α 3 β 3 X 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% contribution) is the factor most responsible for the reduction in response time. Based on the correlation coefficient (R 2), and value adjustment factor (VAF), the ANN model performed better than the MLR model in terms of prediction accuracy. Graphic abstract
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Affiliation(s)
- Imed Ben Romdhane
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Asma Jemmali
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
| | - Sameh Kaziz
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Higher National Engineering School of Tunis, Taha Hussein Montfleury Boulevard, University of Tunis, 1008 Tunis, Tunisia
| | - Fraj Echouchene
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Higher Institute of Applied Sciences and Technology of Sousse, Sousse, Tunisia
| | - Thamraa Alshahrani
- Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Hafedh Belmabrouk
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019 Monastir, Tunisia
- Department of Physics, College of Science, Majmaah University, Al Majma’ah, 11952 Saudi Arabia
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