Tailored xerogel-based sensor arrays and artificial neural networks yield improved O2 detection accuracy and precision

Abstract

The objective of this research is to develop arrays of tuned chemical sensors wherein each sensor element responds to a particular target analyte in a unique manner. By creating sol–gel-derived xerogels that are co-doped with two luminophores at a range of molar ratios, we can form suites of sensor elements that can exhibit a continuum of response profiles. We trained an artificial neural network (ANN) to “learn” to identify the optical outputs from these xerogel-based sensor arrays. By using the ANN in concert with our tailored sensor arrays we obtained a 5–10 fold improvement in accuracy and precision for quantifying O₂ in unknown samples. We also explored the response characteristics of these types of sensor elements after they had been contacted with rat plasma/blood. Contact with plasma/blood caused ∼15% of the luminophore molecules within the xerogels to become non-responsive to O₂. This behavior is consistent with rat albumin blocking certain pore sub-populations within the mesoporous xerogel matrix thereby limiting O₂ access to the luminophores.