Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air

David Tomeček, Henrik Klein Moberg, Sara Nilsson, Athanasios Theodoridis, Iwan Darmadi, Daniel Midtvedt, Giovanni Volpe, Olof Andersson and Christoph Langhammer ()
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David Tomeček: Chalmers University of Technology
Henrik Klein Moberg: Chalmers University of Technology
Sara Nilsson: Chalmers University of Technology
Athanasios Theodoridis: Chalmers University of Technology
Iwan Darmadi: Chalmers University of Technology
Daniel Midtvedt: University of Gothenburg
Giovanni Volpe: University of Gothenburg
Olof Andersson: Insplorion AB
Christoph Langhammer: Chalmers University of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the

Date: 2024
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DOI: 10.1038/s41467-024-45484-9

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