Deep-learning-assisted wedge fiber optic surface plasmon resonance sensor for accuracy detection of trace mercury ions

作者：Lixia Li, Jiabin Zhao, Feng Ning, Yufang Liu · 发表于：Photonics Research · 年份：2025 · DOI：10.1364/prj.570225 · 被引用次数：7 · 研究领域：Analytical Chemistry and Sensors

The increasing levels of heavy metal pollution pose significant health and environmental challenges, particularly the existence of mercury ions ( Hg 2+ ). Even trace concentrations of Hg 2+ can cause serious harm to the human body, making highly sensitive and accurate detection of tiny concentrations of Hg 2+ particularly important. This study demonstrated a novel deep-learning-assisted fiber optic surface plasmon resonance (SPR) sensor for Hg 2+ detection. The fiber optic sensor utilized a wedge probe with an incidence angle of 73° and self-assembly of gold nanospheres (AuNPs) on the surface of the sensing region by DL-dithiothreitol (DTT). Experimental results demonstrated that the sensor exhibited a refractive index (RI) sensitivity of up to 30,400 nm/RIU, which is significantly higher than many previously reported values, and enabled the detection of Hg 2+ at concentrations as low as 1 pmol/L. Furthermore, the fiber optic sensor was tested in 12 (1 pmol/L to 500 nmol/L) solutions with different Hg 2+ concentrations, and the corresponding SPR spectra were recorded. A residual neural network (ResNet)-based deep learning model was developed to classify and recognize the SPR spectra of 12 different Hg 2+ concentrations with a training accuracy of 99.76%. In a blind test on 120 samples, the ResNet model had a prediction accuracy of 99.17%. Therefore, the combination of the sensor and deep learning algorithm proposed in this study holds significant potential for a wide range of...