Xie Yingchao, Xiong Hao, Feng Shiling, Pan Ning, Li Chuan, Liu Yixuan, Zhang Ye, Shao Ligang, Lu Gaopeng, Liu Kun, Wang Guishi
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
College of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, Anhui 230026, China.
Photoacoustics. 2025 Feb 27;43:100707. doi: 10.1016/j.pacs.2025.100707. eCollection 2025 Jun.
Quartz-enhanced photoacoustic spectroscopy (QEPAS) is a promising technique for trace gas sensing, offering advantages such as compact size and high sensitivity. However, noise remains a critical factor limiting detection sensitivity. In this study, a novel approach was proposed to leverage noise for the enhancement of weak QEPAS signals. The method employs stochastic resonance (SR), which counterintuitively utilizes noise to amplify weak spectral signals, thereby significantly improving the signal-to-noise ratio of the QEPAS sensor. The effectiveness of this approach was demonstrated through methane (CH₄) detection using QEPAS. Experimental results indicate that the SR algorithm enhances the output signal by a factor of 3 and reduces the minimum detection limit (MDL) from 329 ppb to 85 ppb compared to conventional QEPAS. The proposed SR-enhanced algorithm presents a promising strategy for further improving QEPAS sensor performance in trace gas detection.
石英增强光声光谱技术（QEPAS）是一种很有前景的痕量气体传感技术，具有体积紧凑和灵敏度高的优点。然而，噪声仍然是限制检测灵敏度的关键因素。在本研究中，提出了一种利用噪声增强微弱QEPAS信号的新方法。该方法采用随机共振（SR），它反直觉地利用噪声来放大微弱的光谱信号，从而显著提高QEPAS传感器的信噪比。通过使用QEPAS检测甲烷（CH₄）证明了该方法的有效性。实验结果表明，与传统的QEPAS相比，SR算法将输出信号增强了3倍，并将最低检测限（MDL）从329 ppb降低到85 ppb。所提出的SR增强算法为进一步提高QEPAS传感器在痕量气体检测中的性能提供了一种很有前景的策略。
