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)₃²⁺/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.

中文翻译：

---

基于智能手机的传感器数据建模，参数估计和灵敏度分析研究的电化学发光机理。

本研究介绍了一个统一的框架，将机理模型与遗传算法（GA）相结合，用于Ru（bpy）₃ ²⁺的电化学发光（ECL）动力学参数估计/ TPrA系统出现在基于智能手机的传感器中。该框架提供了一个直接解决方案，用于同时估计多个参数，否则可能会很耗时并导致不收敛。通过在模型预测与从ECL传感器测得的ECL强度之间实现高度相关性来估算模型参数。开发的模型用于执行灵敏度分析（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，基于遗传算法的参数估计和SA方法可有效地分析ECL机制的动力学。因此，