ArticlePhotoacoustics2022
Highly sensitive HF detection based on absorption enhanced light-induced thermoelastic spectroscopy with a quartz tuning fork of receive and shallow neural network fitting.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 88 citations in OpenAlex.
- Intelligent Algorithm-Assisted Indirect Absorption Spectroscopy for Trace Gas Sensing.Sensors (Basel, Switzerland) · 2026Review
- Enhancing photoacoustic trace gas detection via a CNN-transformer denoising framework.Photoacoustics · 2025Article
- Thermochemical Recycling and Degradation Strategies of Halogenated Polymers (F-, Cl-, Br-): A Holistic Review Coupled with Mechanistic Insights.Chemical record (New York, N.Y.) · 2025Review
- Robust and compact light-induced thermoelastic sensor for atmospheric methane detection based on a vacuum-sealed subminiature tuning fork.Photoacoustics · 2025Article
- High-sensitivity methane detection based on QEPAS and H-QEPAS technologies combined with a self-designed 8.7 kHz quartz tuning fork.Photoacoustics · 2024Article
- Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Gas spectroscopy - Editorial special issue photoacoustics.Photoacoustics · 2023Article
- T-type cell mediated photoacoustic spectroscopy for simultaneous detection of multi-component gases based on triple resonance modality.Photoacoustics · 2023Article
- Highly sensitive light-induced thermoelastic spectroscopy oxygen sensor with co-coupling photoelectric and thermoelastic effect of quartz tuning fork.Photoacoustics · 2023Article
- Quartz-enhanced photoacoustic spectroscopy (QEPAS) and Beat Frequency-QEPAS techniques for air pollutants detection: A comparison in terms of sensitivity and acquisition time.Photoacoustics · 2023Article
- A Miniaturized 3D-Printed Quartz-Enhanced Photoacoustic Spectroscopy Sensor for Methane Detection with a High-Power Diode Laser.Sensors (Basel, Switzerland) · 2023Article
- Highly sensitive photoacoustic acetylene detection based on differential photoacoustic cell with retro-reflection-cavity.Photoacoustics · 2023Article
- Development of Laser Processing Carbon-Fiber-Reinforced Plastic.Sensors (Basel, Switzerland) · 2023Review
- The Development of a Novel Headspace OSensors (Basel, Switzerland) · 2023Article
- Parts-per-billion-level detection of hydrogen sulfide based on doubly resonant photoacoustic spectroscopy with line-locking.Photoacoustics · 2023Article
- Characterization of HPhotoacoustics · 2023Article
- Pre-Shaped Burst-Mode Hybrid MOPA Laser System at 10 kHz Pulse Frequency.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to its advantages of non-contact measurement and high sensitivity, light-induced thermoelastic spectroscopy (LITES) is one of the most promising methods for corrosive gas detection. In this manuscript, a highly sensitive hydrogen fluoride (HF) sensor based on LITES technique is reported for the first time. With simple structure and strong robustness, a shallow neural network (SNN) fitting algorithm is introduced into the field of spectroscopy data processing to achieve denoising. This algorithm provides an end-to-end approach that takes in the raw input data without any pre-processing and extracts features automatically. A continuous wave (CW) distributed feedback diode (DFB) laser with an emission wavelength of 1.27 µm was used as the excitation source. A Herriott multi-pass cell (MPC) with an optical length of 10.1 m was selected to enhance the laser absorption. A quartz tuning fork (QTF) with resonance frequency of 32,767.52 Hz was adopted as the thermoelastic detector. An Allan variance analysis was performed to demonstrate the system stability. When the integration time was 110 s, the minimum detection limit (MDL) was found to be 71 ppb. After the SNN fitting algorithm was used, the signal-to-noise ratio (SNR) of the HF-LITES sensor was improved by a factor of 2.0, which verified the effectiveness of this fitting algorithm for spectroscopy data processing.
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