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Electrochemiluminescence Mechanisms Investigated with Smartphone-Based Sensor Data Modeling, Parameter Estimation and

Elmer Ccopa Rivera¹, Rodney L Summerscales², Padma P Tadi Uppala³

¹Department of Engineering Andrews University 8450 E Campus Circle Drive Berrien Springs MI 49104 USA.

|August 25, 2020

Summary

This study presents a unified framework using a mechanistic model and genetic algorithm (GA) for electrochemiluminescence (ECL) kinetics parameter estimation in smartphone sensors. The approach efficiently estimates multiple parameters and analyzes their impact on chemical species concentrations.

Keywords:

electrochemiluminescence parameter estimation sensitivity analysis sensors smartphones

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Area of Science:

Analytical Chemistry
Chemical Kinetics
Sensor Technology

Background:

Electrochemiluminescence (ECL) is a sensitive analytical technique.
Accurate kinetic parameter estimation is crucial for ECL sensor calibration and performance.
Existing methods for parameter estimation can be time-consuming and prone to convergence issues.

Purpose of the Study:

To develop a unified framework for parameter estimation of ECL kinetics.
To combine a mechanistic model with a genetic algorithm (GA) for efficient and simultaneous estimation of multiple parameters.
To perform sensitivity analysis (SA) to understand parameter influence on chemical species concentrations.

Main Methods:

Developed a unified framework integrating a mechanistic model with a genetic algorithm (GA).
Estimated model parameters by maximizing the correlation between model predictions and measured ECL intensity.
Employed sensitivity analysis (SA) to quantify the impact of parameters on system dynamics.

Main Results:

The GA-based framework enabled straightforward, simultaneous estimation of multiple ECL kinetic parameters.
High correlation was achieved between model predictions and experimental ECL intensity data.
Sensitivity analysis provided quantitative insights into parameter effects on chemical species concentrations.

Conclusions:

The GA-based parameter estimation and SA are effective tools for ECL kinetics analysis.
This framework offers a practical approach for calibrating mechanistic models under various sensing conditions.
The developed methodology enhances the reliability and applicability of ECL sensing systems.