Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis
Abstract 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 s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2020-08-01
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| Series: | ChemistryOpen |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/open.202000165 |
| _version_ | 1818966369894400000 |
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| author | Dr. Elmer Ccopa Rivera Dr. Rodney L. Summerscales Dr. Padma P. Tadi Uppala Dr. Hyun J. Kwon |
| author_facet | Dr. Elmer Ccopa Rivera Dr. Rodney L. Summerscales Dr. Padma P. Tadi Uppala Dr. Hyun J. Kwon |
| author_sort | Dr. Elmer Ccopa Rivera |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-20T13:31:49Z |
| format | Article |
| id | doaj.art-ce97380228f249148727859852d458ef |
| institution | Directory Open Access Journal |
| issn | 2191-1363 |
| language | English |
| last_indexed | 2024-12-20T13:31:49Z |
| publishDate | 2020-08-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | ChemistryOpen |
| spelling | doaj.art-ce97380228f249148727859852d458ef2022-12-21T19:39:04ZengWiley-VCHChemistryOpen2191-13632020-08-019885486310.1002/open.202000165Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity AnalysisDr. Elmer Ccopa Rivera0Dr. Rodney L. Summerscales1Dr. Padma P. Tadi Uppala2Dr. Hyun J. Kwon3Department of Engineering Andrews University 8450 E Campus Circle Drive Berrien Springs MI 49104 USADepartment of Computing Andrews University 4185 E. Campus Circle Drive Berrien Springs MI 49103 USASchool of Population Health, Nutrition & Wellness Andrews University 8475 University Boulevard Berrien Springs MI 49104 USADepartment of Engineering Andrews University 8450 E Campus Circle Drive Berrien Springs MI 49104 USAAbstract 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.https://doi.org/10.1002/open.202000165sensorssmartphoneselectrochemiluminescenceparameter estimationsensitivity analysis |
| spellingShingle | Dr. Elmer Ccopa Rivera Dr. Rodney L. Summerscales Dr. Padma P. Tadi Uppala Dr. Hyun J. Kwon Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis ChemistryOpen sensors smartphones electrochemiluminescence parameter estimation sensitivity analysis |
| title | Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis |
| title_full | Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis |
| title_fullStr | Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis |
| title_full_unstemmed | Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis |
| title_short | Electrochemiluminescence Mechanisms Investigated with Smartphone‐Based Sensor Data Modeling, Parameter Estimation and Sensitivity Analysis |
| title_sort | electrochemiluminescence mechanisms investigated with smartphone based sensor data modeling parameter estimation and sensitivity analysis |
| topic | sensors smartphones electrochemiluminescence parameter estimation sensitivity analysis |
| url | https://doi.org/10.1002/open.202000165 |
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