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Optical Oxygen Sensing with Artificial Intelligence.

Umberto Michelucci1, Michael Baumgartner2, Francesca Venturini3,4

  • 1TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland. umberto.michelucci@toelt.ai.

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Summary
This summary is machine-generated.

This study introduces a novel artificial intelligence approach for oxygen sensing, utilizing machine learning to accurately determine oxygen concentration. This method bypasses traditional calibration, offering a new generation of intelligent sensors.

Keywords:
artificial intelligenceluminescenceluminescence quenchingmachine learningneural networkoptical sensoroxygen sensorphase fluorimetry

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Area of Science:

  • Analytical Chemistry
  • Sensor Technology
  • Artificial Intelligence

Background:

  • Luminescence-based sensors are vital for oxygen concentration measurement, relying on luminescence quenching.
  • Traditional methods use the Stern-Volmer equation, requiring complex, device-specific calibration.
  • Non-linear Stern-Volmer relationships necessitate precise calibration at known oxygen concentrations.

Purpose of the Study:

  • To explore a novel artificial intelligence (AI) approach for oxygen sensing.
  • To demonstrate the feasibility of using machine learning (ML) for oxygen concentration determination.
  • To develop an AI-driven sensor that bypasses traditional calibration methods.

Main Methods:

  • Development of a specialized neural network for oxygen sensing.
  • Training the neural network using synthetically generated data.
  • Relating input sensor quantities to oxygen concentration via machine learning.

Main Results:

  • The AI approach demonstrated feasibility in oxygen sensing.
  • The neural network efficiently learned to correlate sensor inputs with oxygen concentration.
  • Achieved a mean deviation of 0.5% air, comparable to commercial sensors.

Conclusions:

  • Artificial intelligence is applicable to sensing technology.
  • The ML approach offers a pathway for next-generation oxygen sensors.
  • Future improvements are possible with extensive experimental data for training.