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 (α_loss). 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.

Microfluidic biosensors have played an important and challenging role for the rapid detection of the new severe acute respiratory syndrome coronavirus-2 (S

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 (ω), biosensor position (X _S ), angular alignment (θ), and radial displacement (R _D ). Our research also involves a comprehensive performance evaluation of the PSO-ANN model, comparing it to an alternative multilayer perceptron 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.

Recent developments in surface plasmon resonance (SPR) sensors based on photonic crystal fiber (PCF) technology have greatly enhanced sensitivity. Nevertheless, existing configurations often face challenges related to design complexity and real-time application optimization. In this study, we present a straightforward and highly sensitive circular PCF-SPR biosensor tailored for the detection of early-stage cancer cells, with efficient performance across both visible and near-infrared spectrums. Using the Taguchi L₂₇(3⁵) orthogonal array approach, we optimized five critical structural parameters: air hole diameters, pitch, and the thickness of the gold (Au) and titanium dioxide (TiO₂) layers. The resulting sensor achieved remarkable spectral sensitivity of 18,000 nm/RIU and an amplitude sensitivity of 681.655 RIU^-1. Designed to function across a wide refractive index range (1.29-1.40), the biosensor is particularly effective for identifying cervical, blood, and skin cancer cells, reaching peak sensitivity of 7,500 nm/RIU for cervical cancer detection. Additionally, performance prediction utilized machine learning techniques, specifically Multiple Linear Regression (MLR) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) models. The MLP-ANN model demonstrated superior predictive accuracy, highlighting the potential of artificial intelligence in optimizing biosensor configurations for advanced cancer diagnostics.

In this research, Taguchi's method was employed to optimize the performance of a microfluidic biosensor with an integrated flow confinement for rapid d