Real-time ppm-level multigas monitoring and ppb-level fingerprint detection with transient photoacoustics

Detection and simultaneous monitoring of multiple trace gases are essential tasks in many scientific and industrial processes. Here, we use an in-situ learning method for rapid fingerprinting of trace gases to identify and monitor arbitrary gases at extremely low concentrations without prior knowledge of the gas composition. We validate our method on various gas mixtures, including CH4, C2H2, C2H4, C2H6, NO2 and NH3. To this end, we demonstrate real-time analysis of a mixture containing four trace gases at ppm-level with the ability to monitor changes of gas composition on the seconds-timescale. Our approach leverages linear regression analysis to determine gas concentrations within these mixtures. The scalability of simultaneously distinguishable gases is straightforward. Furthermore, we expand the fingerprinting limit down to 5 ppm with a detection confidence of 90 ppb. Consequently, our method offers significant potential for field-deployable sensing applications where speed, accuracy, and ease of use are critical. Reference: S. Angstenberger, M. Floess, L. Schmid, P. Ruchka, T. Steinle and H. Giessen, Optica 12, 1 (2025).