Haiyue Sun,Shunda Qiao,Ying He,Yuanzhi Wang,Jinfeng Hou,Chu Zhang,Yongkang Dong,Yufei Ma
{"title":"High-speed and high-sensitivity multi-gas detection based on parallel heterodyne LITES sensor.","authors":"Haiyue Sun,Shunda Qiao,Ying He,Yuanzhi Wang,Jinfeng Hou,Chu Zhang,Yongkang Dong,Yufei Ma","doi":"10.1038/s41377-026-02385-4","DOIUrl":null,"url":null,"abstract":"A parallel heterodyne light-induced thermoelastic spectroscopy (PH-LITES) sensor is proposed for high-speed and high-sensitivity multi-gas detection for the first time. Within the collaborative signal enhancement architecture (CSEA), high sensitivity and high-speed detection are achieved at the physical layer. A self-designed cylindrical multi-pass cell (MPC) with a recorded high optical path length to volume ratio (OPL/V = 37.4 cm-2) and a four‑tine quartz tuning fork (QTF) with a low resonant frequency (f0 = ~7.9 kHz) work synergistically to enhance detection responsivity, establishing a robust foundation for highly sensitive detection of low-concentration gas mixtures. High‑speed capability is enabled by parallel heterodyne modulation, where a single QTF is excited to generate a composite transient response signal, allowing for the rapid, simultaneous acquisition of spectral information from multiple gases. At the information layer, intelligent processing is implemented via a collaborative intelligent processing architecture (CIPA) integrating convolutional neural networks (CNN), a hybrid attention mechanism (HAM), and bidirectional long short-term memory (BiLSTM). The CNN-HAM-BiLSTM architecture performs feature extraction, attention-based enhancement, and temporal modeling to enable accurate concentration retrieval from the parallel spectra derived from a single QTF output. Experimental validation using methane (CH4) and acetylene (C2H2) achieved minimum detection limits (MDLs) of 378 ppb and 285 ppb, respectively, within a 4 s scanning time. The proposed system offers an efficient solution for applications requiring rapid and sensitive multi-gas detection.","PeriodicalId":18069,"journal":{"name":"Light-Science & Applications","volume":"40 1","pages":""},"PeriodicalIF":24.0000,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Light-Science & Applications","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1038/s41377-026-02385-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
A parallel heterodyne light-induced thermoelastic spectroscopy (PH-LITES) sensor is proposed for high-speed and high-sensitivity multi-gas detection for the first time. Within the collaborative signal enhancement architecture (CSEA), high sensitivity and high-speed detection are achieved at the physical layer. A self-designed cylindrical multi-pass cell (MPC) with a recorded high optical path length to volume ratio (OPL/V = 37.4 cm-2) and a four‑tine quartz tuning fork (QTF) with a low resonant frequency (f0 = ~7.9 kHz) work synergistically to enhance detection responsivity, establishing a robust foundation for highly sensitive detection of low-concentration gas mixtures. High‑speed capability is enabled by parallel heterodyne modulation, where a single QTF is excited to generate a composite transient response signal, allowing for the rapid, simultaneous acquisition of spectral information from multiple gases. At the information layer, intelligent processing is implemented via a collaborative intelligent processing architecture (CIPA) integrating convolutional neural networks (CNN), a hybrid attention mechanism (HAM), and bidirectional long short-term memory (BiLSTM). The CNN-HAM-BiLSTM architecture performs feature extraction, attention-based enhancement, and temporal modeling to enable accurate concentration retrieval from the parallel spectra derived from a single QTF output. Experimental validation using methane (CH4) and acetylene (C2H2) achieved minimum detection limits (MDLs) of 378 ppb and 285 ppb, respectively, within a 4 s scanning time. The proposed system offers an efficient solution for applications requiring rapid and sensitive multi-gas detection.