ArticleACS sensors2025
Neural Network-Enhanced FMCW Gas Spectroscopy.
Article in ACS sensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Detecting multicomponent gases over extensive concentration ranges with laser spectroscopy faces challenges of complex configurations, intricate spectral analysis, and reduced accuracy. Neural networks offer transformative potential for advancing laser spectroscopy by facilitating real-time optimization and automation of experimental processes. Here, we report a frequency-modulated continuous-wave (FMCW) spectroscopic system enhanced by a feedforward neural network (FNN) algorithm. FMCW encodes gas absorption spectra into optical signals, enabling both spatial localization and spectroscopic analysis. By training the FNN for the target gas components, our approach can analyze broadband superposed spectra to determine gas concentrations in the mixture. In the proof-of-concept demonstration for the measurement of C
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