Optoelectronic Dataset for Slot-Waveguide-Based Detection of Water Contaminants

Optoelectronic Dataset for Slot-Waveguide-Based Detection of Water Contaminants This repository contains a physics-based simulation dataset describing the electromagnetic and optoelectronic response of an integrated slot-waveguide sensor for water contaminant detection. The dataset was generated using an analytical electromagnetic model based on a symmetric five-layer slot-waveguide operating under transverse magnetic (TM) polarization. The sensing mechanism relies on concentration-dependent refractive-index variations in aqueous solutions derived from molar refractivity and the Lorentz–Lorenz relation. Four environmentally relevant compounds regulated under drinking-water quality standards were considered: Arsenic Acid (H₃AsO₄) Fluoridric Acid (HF) Potassium Nitrate (KNO₃) Sodium Chloride(NaCl) For each compound, concentration and operating wavelength were systematically swept to generate a multidimensional feature space describing the optical and electrical response of the sensing structure. The dataset includes simulated descriptors such as: Refractive index of the sensing region (n_slot) Propagation constant (β) Optical confinement factor Group velocity (v_g) Electromagnetic interaction factor (η) Spectral sensitivity (S_λ) Effective electric-field related quantities Electrical power dissipation Electromagnetic interaction energy Additional derived optoelectronic metrics The final dataset contains 48,000 simulation instances covering a broad range of wavelengths and analyte concentrations, providing a benchmark resource for: Integrated photonic sensing Water-quality monitoring Electromagnetic waveguide modeling Physics-informed machine learning Optical sensor design Multivariate classification of chemical contaminants All simulations were generated using analytical electromagnetic formulations and numerical post-processing workflows implemented in Python.