Department of Engineering Andrews University 8450 E Campus Circle Drive Berrien Springs MI 49104 USA.
Department of Computing Andrews University 4185 E. Campus Circle Drive Berrien Springs MI 49103 USA.
ChemistryOpen. 2020 Aug 19;9(8):854-863. doi: 10.1002/open.202000165. eCollection 2020 Aug.
The present study introduces a unified framework combining a mechanistic model with a genetic algorithm (GA) for the parameter estimation of electrochemiluminescence (ECL) kinetics of the Ru(bpy)/TPrA system occurring in a smartphone-based sensor. The framework allows a straightforward solution for simultaneous estimation of multiple parameters which can be, otherwise, time-consuming and lead to non-convergence. Model parameters are estimated by achieving a high correlation between the model prediction and the measured ECL intensity from the ECL sensor. The developed model is used to perform a sensitivity analysis (SA), which provides quantitative effects of the model parameters on the concentrations of chemical species involved in the system. The results demonstrate that the GA-based parameter estimation and the SA approaches are effective in analyzing the kinetics of the ECL mechanism. Therefore, these approaches can be incorporated as analysis tools in the ECL kinetics study with practical application in the calibration of mechanistic models for any required sensing condition.
本研究提出了一个统一的框架，将一个机理模型与遗传算法（GA）结合，用于在基于智能手机的传感器中发生的 Ru(bpy)/TPrA 体系的电化学发光（ECL）动力学的参数估计。该框架允许直接对多个参数进行同时估计，否则这些参数会很耗时且导致不收敛。通过使模型预测与从 ECL 传感器测量的 ECL 强度之间达到高度相关，来估计模型参数。开发的模型用于进行灵敏度分析（SA），该分析提供了模型参数对系统中涉及的化学物质浓度的定量影响。结果表明，基于 GA 的参数估计和 SA 方法在分析 ECL 机制的动力学方面非常有效。因此，这些方法可以作为分析 ECL 动力学的工具，在任何所需的传感条件下，在机械模型的校准中具有实际应用。
