Simultaneous Electrochemical and Emission Monitoring of Electrogenerated Chemiluminescence through Instrument Hyphenation

A redox-mediator pathway for enhanced multi-colour electrochemiluminescence in aqueous solution

The classic and most widely used co-reactant electrochemiluminescence (ECL) reaction of tris(2,2'-bipyridine)ruthenium(ii) ([Ru(bpy)3]2+) and tri-n-propylamine is enhanced by an order of magnitude by fac-[Ir(sppy)3]3- (where sppy = 5'-sulfo-2-phenylpyridinato-C 2,N), through a novel 'redox mediator' pathway. Moreover, the concomitant green emission of [Ir(sppy)3]3-* enables internal standardisation of the co-reactant ECL of [Ru(bpy)3]2+. This can be applied using a digital camera as the photodetector by exploiting the ratio of R and B values of the RGB colour data, providing superior sensitivity and precision for the development of low-cost, portable ECL-based analytical devices.

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

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.

Data-Driven Modeling of Smartphone-Based Electrochemiluminescence Sensor Data Using Artificial Intelligence

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru(bpy)32+ luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru(bpy)32+/TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru(bpy)32+ concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru(bpy)32+ using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

‘Cathodic’ electrochemiluminescence of [Ru(bpy)3]2+ and tri-n-propylamine confirmed as emission at the counter electrode

Monitoring emission and potentials at both the working and counter electrodes provides new insight into a proposed cathodic electrochemiluminescence system.