Treebupachatsakul Treesukon, Boosamalee Apivitch, Chaithatwanitch Kamejira, Pechprasarn Suejit
Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology, Ladkrabang, Bangkok 10520, Thailand.
College of Biomedical Engineering, Rangsit University, Pathum Thani 12000, Thailand.
Biomed Opt Express. 2022 Mar 2;13(4):1784-1800. doi: 10.1364/BOE.451023. eCollection 2022 Apr 1.
We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.
我们提出了一个理论框架，用于分析定量传感性能参数，包括灵敏度、半高宽、等离子体激元凹陷位置以及用于Kretschmann配置不同表面等离子体操作条件的品质因数。文献中已经报道了品质因数的几种定义和表达式。此外，每种品质因数的最佳操作条件实际上是不同的。此外，不同表达式和定义之间仍然没有直接的品质因数比较，以确定哪种定义能提供更准确的性能预测。这里，散粒噪声模型和模拟表面等离子体共振（SPR）实验中噪声行为的蒙特卡罗模拟已被用于量化SPR等离子体激元凹陷测量中的标准偏差，以评估品质因数的性能响应。在此，我们提出并制定了一个广义的品质因数定义，为检测限提供了良好的性能估计。在品质因数公式中使用的测量参数通过主成分分析和机器学习来确定。我们还表明，所提出的品质因数可以为包括金和铝在内的等离子体材料的表面等离子体共振性能提供良好的估计，而无需进行资源需求大的计算。
