Laser absorption spectroscopy as a powerful tool for detecting trace gases has been widely used in the monitoring of atmospheric greenhouse gases, pollutions, and respiration processes, including human breath analysis. The detection is based on the light absorption when it propagates through a medium. Most inorganic and organic molecules have characteristic absorption lines in the mid-infrared (mid-IR), which correspond to fundamental vibrational modes and in the near-IR (first overtones) presenting their absorption fingerprints. Here, we summarize the recent developments of the three techniques, namely, wavelength modulation spectroscopy (WMS), cavity ringdown spectroscopy (CRDS), and frequency comb spectroscopy (FCS), and describe their main features as well as possible applications, illustrated by recent experimental results. Emphasis is made on methane detection as applied to breath analysis and atmospheric monitoring. For the WMS technique, we consider local measurements with a multipass cell and also kilometer long open-path configurations for the near-IR and mid-IR spectral regions. The results of measurements of methane in exhaled breath with the CRDS technique in the near-IR are presented for a group of subjects of different ages. We consider various schemes of the FCS that enable fast broadband detection, including direct spectroscopy, dual FCS, and Vernier FCS, and review numerous applications of this approach that revolutionized the field of absorption spectroscopy. The current trends and possible future developments and applications are also discussed.

In absorption spectroscopy, analysis of multicomponent gas mixtures becomes challenging when absorption features overlap (blended spectra). Here we propose a gas sensor which can accurately identify the species and retrieve the concentrations of components in a gaseous mixture in a broad spectrum. The sensor integrates a mid-infrared dual-frequency comb laser source for spectrum acquisition and a deep learning algorithm for spectral analysis. The sensor was tested on gas phase mixtures of methane, acetone and water vapor. A prototype sensor was assessed in realistic scenarios in real time. We also systematically analyzed and presented explicit visualizations to explain the underlying working mechanism of the algorithms.

Broadband mid‐infrared spectroscopy not only offers supreme sensitivity for the massively parallel detection of trace gases but also presents many challenges. Herein, a new platform combining the advantages of a mid‐infrared dual‐comb spectrometer based on two difference‐frequency generation combs pumped by femtosecond Er‐doped fiber comb oscillators and an unsupervised deep learning neural network consisting of information extraction and information mapping blocks is presented. The scarce data problem, the uncertainties of apparatus, and manual operations intrinsic to multicomponent gas mixture analysis are overcome by coupling an unsupervised leaning approach with a model‐agnostic, physics‐informed data augmentation strategy using simulated data from spectral databases. The system provides reliable simultaneous identification of gas species, concentration retrieval, as well as ambient pressure prediction and eliminates the negative impacts on the measurement, such as model error, baseline fluctuation, and unknown absorbers. Parallel optical detection of 31 different mixtures of 5 gas species over a 2900–3100 cm⁻¹ spectral range with a sub part‐per‐billion sensitivity is demonstrated showing the potential in various applications such as atmospheric monitoring, diagnostics with breath biomarkers, and capturing rapid chemical reaction kinetics.

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil.

Silicon carbide (SiC) is widely used in power electronic devices and other fields, the defects of which can significantly impact its performance in device fabrication. Laser ultrasonic non-destructive testing (NDT) as a novel and effective approach can detect these defects in real time. This study introduces a numerical model for the SiC NDT that elucidates the dynamic interactions between laser-induced ultrasonic waves and surface defects, and internal defects in SiC, respectively. Results show NDT is an effective way to locate the SiC defect and the ultrasonic waves’ vibration amplitude of detection points at defect edges increases by at least 16% compared to adjacent points, with a maximum of 43%. A comparative assessment between surface and internal defect vibration responses for acoustic is also made. For internal defects, the oscillation time of the acoustic wave at the detection point on the surface away from the edge of the defect at the excitation point exceeds that of surface defects by 100 ns, and the amplitude near the excitation point is more pronounced, reaching 1.44 nm, which is 4.2 times that of corresponding surface defects. Additionally, a linear relationship is observed between the arrival time of transmitted Rayleigh Waves (RSR) and internal defect length, with a correlation coefficient of 0.9878. Similarly, a linear relationship is established between the amplitude of reflected Rayleigh Waves (rR) and defect width, with a correlation coefficient of 0.9976, providing an effective way to quantify the inner defect. Furthermore, transient temperature profiles at distinct positions and transient acoustic fields and the relationship of acoustic vibration amplitude increasing with laser spot size under a fixed laser power density are also analyzed. This model provides a theoretical foundation for laser ultrasonic NDT setup and choice of micro-vibration detection device.