The precise detection of volatile organic compounds plays a pivotal role in addressing environmental concerns, industrial safety, and medical diagnostics. The accurate identification and quantification of these compounds because of their ubiquity and potential health hazards has fueled the development of advanced sensing technologies. This work presents a sensing system in the realm of long-wavelength infrared spectroscopy for achieving enhanced selectivity and sensitivity of benzene, toluene, and propane detection through quartz-enhanced photoacoustic spectroscopy. High-resolution gas spectroscopy is made possible by the use of specially designed InAs/AlSb-based quantum cascade lasers, emitting in the wavelength range 13–15 μm, and quartz tuning forks. The sensor system, characterized by its robustness and precision, demonstrates exceptional capabilities in benzene, toluene, and propane detection. The system's capacity for practical applications in environmental monitoring and medical diagnostics is demonstrated by its ability to distinguish these volatile organic compounds with a minimum detection limit of 113 ppb, 3 ppb, and 3 ppm for toluene, benzene, and propane at an integration time of 10 s, even in complex gas matrices. This work advances gas sensing technology while also offering insightful information on spectral interferences, a persistent problem in the field. The results usher in a new era of sophisticated and reliable gas sensing techniques meeting the growing demand for precise volatile organic compounds detectors for environmental monitoring purposes.

A dual-gas sensor based on the combination of a quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor and an electronic hygrometer was realized for the simultaneous detection of methane (CH₄) and water vapor (H₂O) in air. The QEPAS sensor employed an interband cascade laser operating at 3.34 μm capable of targeting a CH₄ absorption line at 2988.8 cm⁻¹ and a water line at 2988.6 cm⁻¹. Water vapor was measured with both the electronic hygrometer and the QEPAS sensor for comparison. The measurement accuracy provided by the hygrometer enabled the adjustment of methane QEPAS signal with respect to the water vapor concentration to retrieve the actual CH₄ concentration. The sensor was tested by performing prolonged measurements of CH₄ and H₂O over 60 h to demonstrate the effectiveness of this approach for environmental monitoring applications.

A theoretical model describing the acoustic coupling between two resonator tubes in spectrophones exploiting custom-made quartz tuning forks (QTFs) is proposed. The model is based on an open-end correction to predict the optimal tube length. A calculation of the sound field distribution from one tube exit allowed for the estimation of the optimal radius as a function of the QTF prong spacing and the sound wavelength. The theoretical predictions have been confirmed using experimental studies employing a custom QTF with a fundamental flexural mode resonance frequency of 15.8 kHz and a quality factor of 15,000 at atmospheric pressure. The spacing between the two prongs was 1.5 mm. Spectrophones mounting this QTF were implemented for the quartz-enhanced photoacoustic detection of water vapor in air in the mid-infrared spectral range.

We report on a fiber-coupled, quartz-enhanced photoacoustic spectroscopy (QEPAS) near-IR sensor for sequential detection of methane (CH₄ or C1) and ethane (C₂H₆ or C2) in air. With the aim of developing a lightweight, compact, low-power-consumption sensor suitable for unmanned aerial vehicles (UAVs)-empowered environmental monitoring, an all-fiber configuration was designed and realized. Two laser diodes emitting at 1653.7 nm and 1684 nm for CH₄ and C₂H₆ detection, respectively, were fiber-combined and fiber-coupled to the collimator port of the acoustic detection module. No cross talk between methane and ethane QEPAS signal was observed, and the related peak signals were well resolved. The QEPAS sensor was calibrated using gas samples generated from certified concentrations of 1% CH₄ in N₂ and 1% C₂H₆ in N₂. At a lock-in integration time of 100 ms, minimum detection limits of 0.76 ppm and 34 ppm for methane and ethane were achieved, respectively. The relaxation rate of CH₄ in standard air has been investigated considering the effects of H₂O, N₂ and O₂ molecules. No influence on the CH₄ QEPAS signal is expected when the water vapor concentration level present in air varies in the range 0.6–3%.

In this work, we report on the novel employment of lithium niobate tuning forks as acoustic transducers in photoacoustic spectroscopy for gas sensing. The lithium niobate tuning fork (LiNTF) exhibits a fundamental resonance frequency of 39196.6 Hz and a quality factor Q = 5900 at atmospheric pressure. The possibility to operate the LiNTF as a photoacoustic wave detector was demonstrated targeting a water vapor absorption line falling at 7181.14 cm⁻¹ (1.39 µm). A noise equivalent concentration of 2 ppm was reached with a signal integration time of 20 s. These preliminary results open the path towards integrated photonic devices for gas sensing with LiNTF-based detectors on lithium niobate platforms.

A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively. With respect to previously reported QEPAS sensors for natural gas analysis, the high prediction accuracy as well as the capability to discriminate and detect C3 within natural gas-like complex mixtures provided by the employment of partial least-squares regression mark significant improvements. Furthermore, these results enable an improved performance of the sensor for in situ, real-time, and online natural gas composition analysis.