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<p>Photonic crystals are being increasingly utilized in a variety of advanced and innovative applications. This study aims to enhance the theoretical design of photonic sensors by employing a statistical optimization method, examining thickness tolerances, and investigating the impact of surface roughness. These factors are crucial for understanding the limitations and challenges associated with the sensor fabrication process. The proposed sensor features a one-dimensional (1D) mirror-symmetric defect cavity photonic crystal (PC) structure specifically designed for the detection of iron ions in water. The sensing medium is modeled using Fe<sub>3</sub>O<sub>4</sub> nanoparticle suspensions, which exhibit refractive index variations as a function of concentration. The sensor architecture consists of a defect cavity layer situated between ordinary photonic crystal (OPC) and mirror photonic crystal (MPC) structures. To identify the optimal thickness for each layer, statistical optimization was performed using response surface methodology (RSM). The optimized OPC/C/MPC sensor demonstrated a sensitivity of 2.16&#xa0;nm/% across concentrations from 0.0 to 100% of Fe<sub>3</sub>O<sub>4</sub> solution under normal incident light. Importantly, the sensor also exhibited a high figure of merit (FoM), with values ranging from 10.33 to 5.03. In addition, the effects of temperature and angle of incidence were addressed. The calculated electric field |E| distribution across the proposed sensor demonstrates significant improvement after the optimization process. The analysis of thickness tolerances indicated that the sensor maintained stable performance with a thickness variation of ± 10%. Moreover, the incorporation of a rough interlayer into the sensor design had a minimal effect on the resonance shift, suggesting that surface roughness does not significantly impact the performance of the proposed sensor. These findings suggest that the proposed sensor holds promise for practical iron detection with both accuracy and reliability. Furthermore, the study lays the groundwork for addressing the instability issues frequently encountered in theoretical PC sensors during experimental validation.</p>

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Theoretical analysis of statistical optimization, thickness tolerance, and surface roughness effects on a mirror-symmetric photonic crystal sensor for iron ion detection in water

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Ashour M. Ahmed

摘要

Photonic crystals are being increasingly utilized in a variety of advanced and innovative applications. This study aims to enhance the theoretical design of photonic sensors by employing a statistical optimization method, examining thickness tolerances, and investigating the impact of surface roughness. These factors are crucial for understanding the limitations and challenges associated with the sensor fabrication process. The proposed sensor features a one-dimensional (1D) mirror-symmetric defect cavity photonic crystal (PC) structure specifically designed for the detection of iron ions in water. The sensing medium is modeled using Fe3O4 nanoparticle suspensions, which exhibit refractive index variations as a function of concentration. The sensor architecture consists of a defect cavity layer situated between ordinary photonic crystal (OPC) and mirror photonic crystal (MPC) structures. To identify the optimal thickness for each layer, statistical optimization was performed using response surface methodology (RSM). The optimized OPC/C/MPC sensor demonstrated a sensitivity of 2.16 nm/% across concentrations from 0.0 to 100% of Fe3O4 solution under normal incident light. Importantly, the sensor also exhibited a high figure of merit (FoM), with values ranging from 10.33 to 5.03. In addition, the effects of temperature and angle of incidence were addressed. The calculated electric field |E| distribution across the proposed sensor demonstrates significant improvement after the optimization process. The analysis of thickness tolerances indicated that the sensor maintained stable performance with a thickness variation of ± 10%. Moreover, the incorporation of a rough interlayer into the sensor design had a minimal effect on the resonance shift, suggesting that surface roughness does not significantly impact the performance of the proposed sensor. These findings suggest that the proposed sensor holds promise for practical iron detection with both accuracy and reliability. Furthermore, the study lays the groundwork for addressing the instability issues frequently encountered in theoretical PC sensors during experimental validation.