Intelligent photonic crystal-based optical sensor for accurate glycerol-water mixture measurement using artificial neural networks

This paper proposes an intelligent photonic crystal-based optical sensor designed for the first time to accurately measure glycerol-water concentration and temperature. The proposed sensor features a novel two-dimensional (2D) photonic crystal structure with an optimized waveguide configuration to enhance refractive index sensitivity. The sensor structure does not include defect rods, which simplifies fabrication and enhances stability. By using the unique optical properties of photonic crystals and the artificial neural network (ANN), the proposed design ensures high precision and stability in detecting changes in the glycerol concentration. The performance of the sensor was evaluated based on sensitivity, detection limit (DL), figure of merit (FOM), and quality factor (Q-F) across different temperatures and glycerol concentrations. The optical response of the sensor was numerically analyzed and simulated using the finite-difference time-domain (FDTD) method. Then, a feedforward ANN model was developed and trained to predict glycerol concentration and temperature from the output spectral data, enabling intelligent and real-time analysis. The results demonstrate that the proposed sensor achieves high sensitivity (up to 89.9 nm/RIU), a low detection limit (0.0003–0.0010 RIU −1 ), and an excellent Q-factor (5233), making it a highly effective solution for refractive index sensing. Overall, the findings confirm that the proposed photonic crystal sensor, enhanced with ANN-based intelligent analysis, offers high accuracy, stability, and fast response, making it suitable for biomedical, pharmaceutical, and industrial applications where precise glycerol concentration measurements are required.