This article investigates the design of a surface plasmon resonance (SPR) sensor that utilizes carbon nanotubes (CNT) and graphene to detect formalin concentration in water. The proposed sensor's design optimization and performance evaluation are achieved by implementing Gradient Boosting Regression (GBR), a machine learning (ML) algorithm, and the artificial hummingbird algorithm. An iterative transfer matrix technique is employed to create training and test sets for machine learning analysis, and a dataset of 8505 × 8 is obtained. The optimized thickness of Ag, CNT, and graphene 51.71 nm, 0.489 nm, and 4.32 nm were obtained using the artificial hummingbird algorithm. The results demonstrate that the SPR sensor achieves excellent reflectance curves, leading to a significant increase in detection sensitivity of 340.44 deg./RIU. Other characteristic parameters such as detection accuracy (DA), full width at half maximum (FWHM), and figure of merit (FoM) have also been evaluated.

本文研究了表面等离子共振 (SPR) 传感器的设计，该传感器利用碳纳米管 (CNT) 和石墨烯来检测水中的福尔马林浓度。所提出的传感器的设计优化和性能评估是通过实施梯度提升回归（GBR）、机器学习（ML）算法和人工蜂鸟算法来实现的。采用迭代转移矩阵技术创建用于机器学习分析的训练集和测试集，获得8505×8的数据集。使用人工蜂鸟算法获得Ag、CNT和石墨烯的优化厚度51.71 nm、0.489 nm和4.32 nm。结果表明，SPR传感器实现了优异的反射率曲线，使检测灵敏度显着提高至340.44 deg./RIU。还评估了其他特征参数，例如检测精度 (DA)、半峰全宽 (FWHM) 和品质因数 (FoM)。