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Highly Sensitive and Fast Hydrogen Detection Based on Light-Induced Thermoelastic Spectroscopy

Open access
Ma, Yufei, Liang, Tiantian, Qiao, Shunda, Liu, Xiaonan, Lang, Ziting·2023·Ultrafast Science

Abstract

As a new energy source, hydrogen (H 2 ) detection is a hot topic in recent years. Because of the weak absorption characteristic, laser spectroscopy-based H 2 detection is challenging. In this paper, a highly sensitive H 2 sensor based on light-induced thermoelastic spectroscopy (LITES) technique is demonstrated for the first time. A continuous-wave, distributed feedback diode laser with emission in the 2.1 μm region was adopted as the excitation source to target the strongest H 2 absorption line of 4,712.90 cm −1 . A Herriott multipass cell with an optical length of 10.1 m was chosen to further improve the H 2 absorption. With the feature of processing the raw input data without data preprocessing and extracting the desired features automatically, the robust shallow neural network (SNN) fitting algorithm was brought in to denoise the sensor. For the LITES-based H 2 sensor, the concentration response was tested, and an excellent linear response to H 2 concentration levels was achieved. A minimum detection limit (MDL) of ~80 ppm was obtained. On the basis of implementation of the H 2 -LITES sensor, a heterodyne H 2 -LITES sensor was further constructed to realize a fast measurement of resonance frequency of quartz tuning fork and H 2 concentration simultaneously. The resonance frequency can be retrieved in several hundred milliseconds with the measurement accuracy of ±0.2 Hz, and the result of 30,713.76 Hz is exactly same as the experimentally determined value of 30,713.69 Hz. After the SNN algorithm was applied, an MDL of ~45 ppm was achieved for this heterodyne H 2 -LITES sensor.

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Metadata

Authors
Ma, Y., Liang, T., Qiao, S., Liu, X., & Lang, Z.
Venue
Ultrafast Science
Published
2023-01
Volume
3
ISSN
  • 2097-0331 (ISSN-L)
  • 2765-8791
OpenAlex
W4327951970
Cited by
214
References
2
FWCI
18.78
Type
journal article
Language
en
Open access
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