Ppb-level NH3 photoacoustic sensor combining a hammer-shaped tuning fork and a 9.55 µm quantum cascade laser

All-Fiber LITES Sensor Based on Hollow-Core Anti-Resonant Fiber and Self-Designed Low-Frequency Quartz Tuning Fork

In this paper, an all-fiber light-induced thermoelastic spectroscopy (LITES) sensor based on hollow-core anti-resonant fiber (HC-ARF) and self-designed low-frequency quartz tuning fork (QTF) is reported for the first time. By utilizing HC-ARF as both the transmission medium and gas chamber, the laser tail fiber was spatially coupled with the HC-ARF, and the end of the HC-ARF was directly guided onto the QTF surface, resulting in an all-fiber structure. This design eliminated the need for lens combinations, thereby enhancing system stability and reducing cost and size. Additionally, a self-designed rectangular-tip QTF with a low resonant frequency of 8.69 kHz was employed to improve the sensor's detection performance. Acetylene (C2H2), with an absorption line at 6534.37 cm-1 (1.53 μm), was chosen as the target gas. Experimental results clearly demonstrated that the detection performance of the rectangular-tip QTF system was 2.9-fold higher than that of a standard commercial QTF system. Moreover, it exhibited an outstanding linear response to varying C2H2 concentrations, indicating its high sensitivity and reliability in detecting C2H2. The Allan deviation analysis was used to assess the system's stability, and the results indicated that the system exhibits excellent long-term stability.

Multivariate-coupled-enhanced photoacoustic spectroscopy with Chebyshev rational fractional-order filtering algorithm for trace CH4 detection

An innovative and miniature photoacoustic spectroscopy (PAS) gas sensor based on a multivariate-coupled amplification photoacoustic cell (MVCA-PAC) with a total length of 100 mm was developed to achieve ultra-sensitive trace CH4 detection. Acoustic pressure distribution simulations reveal that at the first-order resonance frequency, the MVCA-PAC achieves a maximum acoustic pressure approximately 3.9 times higher than that of a conventional photoacoustic cell. The absorption optical path of the MVCA-PAC reached 2068 mm through 22 reflections, resulting in a 2-fold increase in the amplitude of photoacoustic signals compared to the traditional photoacoustic cell with an equivalent absorption optical path. Furthermore, compared to a single-pass photoacoustic cell, the 2-f signal intensity of the MVCA-PAC increased by a factor of 4.5. Allan variance analysis indicated a detection limit of 0.572 ppm for CH4 detection with an averaging time of approximately 300 s. To further improve the measurement precision of the designed sensor, the Chebyshev rational fractional-order filtering (CRFOF) algorithm was introduced for PAS signal processing for the first time. Post-processing results demonstrated a 15.4-fold improvement in measurement precision, achieving a precision of 0.578 ppm. Finally, continuous monitoring of atmospheric CH4 over a 48-hour period validated the reliability and feasibility of the sensor.

LITES-Based Sensitive CO2 Detection Using 2 μm Diode Laser and Self-Designed 9.5 kHz Quartz Tuning Fork

A carbon dioxide (CO2) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant frequency of 9464.18 Hz and a high quality factor (Q) of 12,133.56 can significantly increase the accumulation time and signal level of the CO2-LITES sensor. A continuous-wave (CW) distributed-feedback (DFB) diode laser is used as the light source, and the strongest absorption line of CO2 located at 2004.01 nm is chosen. A comparison between the standard commercial QTF with the resonant frequency of 32.768 kHz and the self-designed trapezoidal-head QTF is performed. The experimental results show that the CO2-LITES sensor with the self-designed trapezoidal-head QTF has an excellent linear response to CO2 concentration, and its minimum detection limit (MDL) can reach 46.08 ppm (parts per million). When the average time is increased to 100 s based on the Allan variance analysis, the MDL of the sensor can be improved to 3.59 ppm. Compared with the 16.85 ppm of the CO2-LITES sensor with the commercial QTF, the performance is improved by 4.7 times, demonstrating the superiority of the self-designed trapezoidal-head QTF.

Optomechanical energy enhanced BF-QEPAS for fast and sensitive gas sensing

Traditional beat frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) are limited by short energy accumulation times and the necessity of a decay period, leading to weaker signals and longer measurement cycles. Herein, we present a novel optomechanical energy-enhanced (OEE-) BF-QEPAS technique for fast and sensitive gas sensing. Our approach employs periodic pulse-width modulation (PWM) of the laser signal with an optimized duty cycle, maintaining the quartz tuning fork's (QTF) output at a stable steady-state level by applying stimulus signals at each half-period and allowing free vibration in alternate half-periods to minimize energy dissipation. This method enhances optomechanical energy accumulation in the QTF, resulting in an approximate 33-fold increase in response speed and a threefold increase in signal intensity compared to conventional BF-QEPAS. We introduce an energy efficiency coefficient K to quantify the relationship between transient signal amplitude and measurement duration, exploring its dependence on the modulation signal's period and duty cycle. Theoretical analyses and numerical simulations demonstrate that the maximum K occurs at a duty cycle of 50 % and an optimized beat frequency Δf of 30 Hz. Experimental results using methane reveal a detection limit of 2.17 ppm with a rapid response time of 33 ms. The OEE-BF-QEPAS technique exhibits a wide dynamic range with exceptional linearity over five orders of magnitude and a record noise-equivalent normalized absorption (NNEA) coefficient of 9.46 × 10-10 W cm-1 Hz-1/2. Additionally, a self-calibration method is proposed for correcting resonant frequency shifts. The proposed method holds immense potential for applications requiring fast and precise gas detection.

Sub-ppb level HCN photoacoustic sensor employing dual-tube resonator enhanced clamp-type tuning fork and U-net neural network noise filter

Hydrogen cyanide (HCN) is a toxic industrial chemical, necessitating low-level detection capabilities for safety and environmental monitoring. This study introduces a novel approach for detecting hydrogen cyanide (HCN) using a clamp-type custom quartz tuning fork (QTF) integrated with a dual-tube acoustic micro-resonator (AmR) for enhanced photoacoustic gas sensing. The design and optimization of the AmR geometry were guided by theoretical simulation and experimental validation, resulting in a robust on-beam QEPAS (Quartz-Enhanced Photoacoustic Spectroscopy) configuration. To boost the QEPAS sensitivity, an Erbium-Doped Fiber Amplifier (EDFA) was incorporated, amplifying the laser power by approximately 286 times. Additionally, a transformer-based U-shaped neural network, a machine learning filter, was employed to refine the photoacoustic signal and reduce background noise effectively. This combination yielded a significantly low detection limit for HCN at 0.89 parts per billion (ppb) with a rapid response time of 1 second, marking a substantial advancement in optical gas sensing technologies. Key modifications to the QTF and innovative use of AmR lengths were validated under various experimental conditions, affirming the system's capabilities for real-time, high-sensitivity environmental monitoring and industrial safety applications. This work not only demonstrates significant enhancements in QEPAS but also highlights the potential for further technological advancements in portable gas detection systems.

End-to-end methane gas detection algorithm based on transformer and multi-layer perceptron

In this paper, an end-to-end methane gas detection algorithm based on transformer and multi-layer perceptron (MLP) for tunable diode laser absorption spectroscopy (TDLAS) is presented. It consists of a Transformer-based U-shaped Neural Network (TUNN) filtering algorithm and a concentration prediction network (CPN) based on MLP. This algorithm employs an end-to-end architectural design to extract information from noisy transmission spectra of methane and derive the CH4 concentrations from denoised spectra, without intermediate steps. The results demonstrate the superiority of the proposed TUNN filtering algorithm over other typically employed digital filters. For concentration prediction, the determination coefficient (R2) reached 99.7%. Even at low concentrations, R2 remained notably high, reaching up to 89%. The proposed algorithm results in a more efficient, convenient, and accurate spectral data processing for TDLAS-based gas sensors.