In recent decades, new nonlinear optical materials have been actively developed to create coherent tunable light sources in the mid-infrared (mid-IR) part of the spectrum used in a variety of scientific fields. In the present review, the main attention is focused on barium chalcogenide crystals, including their linear and nonlinear optical properties, laser-induced damage threshold (LIDT), and frequency down-conversion.

The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, they have limitations such as dependence on a high-power light source, a requirement for a high-quality acoustic signal detector, and sensitivity to environmental factors, affecting their accuracy and reliability. Machine learning has great potential in the analysis and interpretation of sensor data as it can identify complex patterns and make accurate predictions based on the available data. We propose a novel approach that utilizes wavelet analysis and neural networks with enhanced architectures to improve the accuracy and sensitivity of photoacoustic gas sensors. Our proposed approach was experimentally tested for methane concentration measurements, showcasing its potential to significantly advance the field of gas detection and analysis, providing more accurate and reliable results.

In this study, we address the challenge of estimating the resonance frequency of a photoacoustic detector (PAD) gas cell under varying temperature conditions, which is crucial for improving the accuracy of gas concentration measurements. We introduce a novel approach that uses a long short-term memory network and a self-attention mechanism to model resonance frequency shifts based on temperature data. To investigate the impact of the gas mixture temperature on the resonance frequency, we modified the PAD to include an internal temperature sensor. Our experiments involved multiple heating and cooling cycles with varying methane concentrations, resulting in a comprehensive dataset of temperature and resonance frequency measurements. The proposed models were trained and validated on this dataset, and the results demonstrate real-time prediction capabilities with a mean absolute error of less than 1 Hz for frequency shifts exceeding 30 Hz over four-hour periods. This approach allows continuous, real-time tracking of the resonance frequency without interrupting the laser operation, significantly enhancing gas concentration measurements and contributing to the long-term stabilization of the sensor. The results suggest that the proposed approach is effective in managing temperature-induced frequency shifts, making it a valuable tool for improving the accuracy and stability of gas sensors in practical applications.