Abstract
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 luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with /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 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 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.
Highlights
Electrochemiluminescence (ECL) is being explored in research ranging from fundamental studies to its application as a platform of light-emitting sensors and an analytical detection method
This modeling approach is suitable for a certain class of problems that are susceptible to a mathematical description such as the Ru(bpy)23+ /TPrA system charge, momentum, and mass transfer, as well as the reaction rates involved
CV means that Feedforward Neural Network (FNN) with different numbers of hidden neurons, that is, different architectures, are trained with the training set, and the performances are assessed on the ability to make accurate predictions of the validation set in terms of R2 and mean square error (MSE)
Summary
Electrochemiluminescence (ECL) is being explored in research ranging from fundamental studies to its application as a platform of light-emitting sensors and an analytical detection method. Quantitative studies to explore the complex mechanism of ECL typically use applied mathematical methods, partial differential equations (PDEs) that constitute mechanistic or first-principle models. This modeling approach is suitable for a certain class of problems that are susceptible to a mathematical description such as the Ru(bpy)23+ /TPrA system charge, momentum, and mass transfer, as well as the reaction rates involved. In other work [12], model simulations coupled to microscopy imaging provided light emission mechanism insight to obtain high sensitivity in bead-based ECL assays These studies required strong expertise in electrochemical theory for the mechanistic model set-up.
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