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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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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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Wang H, Xu T, Fu Y, Wang Z, Leeson MS, Jiang J, Liu T. Liquid Crystal Biosensors: Principles, Structure and Applications. BIOSENSORS 2022; 12:bios12080639. [PMID: 36005035 PMCID: PMC9406233 DOI: 10.3390/bios12080639] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/06/2022] [Accepted: 08/12/2022] [Indexed: 12/31/2022]
Abstract
Liquid crystals (LCs) have been widely used as sensitive elements to construct LC biosensors based on the principle that specific bonding events between biomolecules can affect the orientation of LC molecules. On the basis of the sensing interface of LC molecules, LC biosensors can be classified into three types: LC–solid interface sensing platforms, LC–aqueous interface sensing platforms, and LC–droplet interface sensing platforms. In addition, as a signal amplification method, the combination of LCs and whispering gallery mode (WGM) optical microcavities can provide higher detection sensitivity due to the extremely high quality factor and the small mode volume of the WGM optical microcavity, which enhances the interaction between the light field and biotargets. In this review, we present an overview of the basic principles, the structure, and the applications of LC biosensors. We discuss the important properties of LC and the principle of LC biosensors. The different geometries of LCs in the biosensing systems as well as their applications in the biological detection are then described. The fabrication and the application of the LC-based WGM microcavity optofluidic sensor in the biological detection are also introduced. Finally, challenges and potential research opportunities in the development of LC-based biosensors are discussed.
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Affiliation(s)
- Haonan Wang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Tianhua Xu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
- Correspondence: (T.X.); (J.J.)
| | - Yaoxin Fu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Ziyihui Wang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
- School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Mark S. Leeson
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Junfeng Jiang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
- Correspondence: (T.X.); (J.J.)
| | - Tiegen Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
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Ram D, Bhandari DS, Tripathi D, Sharma K. Propagation of H1N1 virus through saliva movement in oesophagus: a mathematical model. EUROPEAN PHYSICAL JOURNAL PLUS 2022; 137:866. [PMID: 35912042 PMCID: PMC9326416 DOI: 10.1140/epjp/s13360-022-03070-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
H1N1 (Swine flu) is caused by the influenza A virus which belongs to the Orthomyxoviridae family. Influenza A is very harmful to the elderly, and people with chronic respiratory disease and cardiovascular disease. Therefore, it is essential to analyse the behaviour of virus transmission through the saliva movement in oesophagus. A mathematical paradigm is developed to study the saliva movement under the applications of transverse magnetic field. Jeffrey fluid model is considered for saliva to show the viscoelastic nature. The flow nature is considered creeping and assumptions of long wavelength and low Reynolds number are adopted for analytical solutions. The Basset-Boussinesq-Oseen equation is employed to understand the propagation of H1N1 virus through saliva under the effect of applicable forces such as gravity, virtual mass, basset force, and drag forces. The suitable data for saliva, oesophagus and H1N1 virus are taken from the existing literature for simulation of the results using MATLAB software. From the graphical results, it is observed that the susceptibility to viral infections is less because the magnetic field reduces the motion of the virus particle. Further, the chances of infections in males are more as compared to females and children due to variation in viscosity of saliva. Such findings provide an understanding of the mechanics of the virus floating through the saliva (viscoelastic fluids) in the oesophagus.
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Affiliation(s)
- Daya Ram
- Department of Mathematics, Malaviya National Institute of Technology Jaipur, Rajasthan, 302017 India
| | - D. S. Bhandari
- Department of Mathematics, National Institute of Technology, Uttarakhand, Srinagar, 246174 India
| | - Dharmendra Tripathi
- Department of Mathematics, National Institute of Technology, Uttarakhand, Srinagar, 246174 India
| | - Kushal Sharma
- Department of Mathematics, Malaviya National Institute of Technology Jaipur, Rajasthan, 302017 India
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Kaziz S, Ben Mariem I, Echouchene F, Belkhiria M, Belmabrouk H. Taguchi optimization of integrated flow microfluidic biosensor for COVID-19 detection. EUROPEAN PHYSICAL JOURNAL PLUS 2022; 137:1235. [PMID: 36405040 PMCID: PMC9660129 DOI: 10.1140/epjp/s13360-022-03457-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 10/17/2022] [Indexed: 05/20/2023]
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%).
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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
| | - Ibrahim Ben Mariem
- Quantum and Statistical Physics Laboratory, Faculty of Sciences 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 Sousse, University of Sousse, Sousse, Tunisia
| | - Maissa Belkhiria
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
| | - Hafedh Belmabrouk
- Department of Physics, College of Science at Zulfi, Majmaah University, Al Majma’ah, Saudi Arabia
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Kaziz S, Ben Mariem I, Echouchene F, Gazzah MH, Belmabrouk H. Design parameters optimization of an electrothermal flow biosensor for the SARS-CoV-2 S protein immunoassay. INDIAN JOURNAL OF PHYSICS AND PROCEEDINGS OF THE INDIAN ASSOCIATION FOR THE CULTIVATION OF SCIENCE (2004) 2022; 96:4091-4101. [PMID: 35463477 PMCID: PMC9013635 DOI: 10.1007/s12648-022-02360-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/25/2022] [Indexed: 05/20/2023]
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.
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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
| | - Ibrahim Ben Mariem
- Quantum and Statistical Physics Laboratory, Faculty of Sciences 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
| | - Mohamed Hichem Gazzah
- Quantum and Statistical Physics Laboratory, Faculty of Sciences of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
| | - Hafedh Belmabrouk
- Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, Environment Boulevard, 5019 Monastir, Tunisia
- Department of Physics, College of Science at Al Zulfi, Majmaah University, Al Majma’ah, 11952 Saudi Arabia
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