Abstract
Luminescence-based sensors for measuring oxygen concentration are widely used in both industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most cases is non-linear, is parameterized through device-specific constants. Therefore, to determine these parameters, every sensor needs to be precisely calibrated at one or more known concentrations. This study explored an entirely new artificial intelligence approach and demonstrated the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5% air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.
Highlights
The determination of oxygen partial pressure is of great interest in numerous areas including medicine, biotechnology, and chemistry
This study explored an entirely new artificial intelligence approach and demonstrated the feasibility of oxygen sensing through machine learning
The values of all three mean absolute error (MAE) are larger for neural networks with lower effective complexity, on the left of the plot, than for ones with higher complexity, on the right of the plot, regardless of the dataset to which the network is applied
Summary
Since the purpose of this work is to generate synthetic data to perform the training of the neural network, the model of Equation (4) is chosen to describe the data, being as simple as possible, and keeping in mind the limited physical meaning
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