Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing
文献类型：期刊论文
| 作者 | Xiong, Hao1,2; Shao, Ligang2; Cao, Yuan2; Wang, Guishi2; Wang, Ruifeng2; Mei, Jiaoxu2 ; Liu, Kun2; Gao, Xiaoming1,2
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| 刊名 | ACS SENSORS
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| 出版日期 | 2024-08-16 |
| 关键词 | neural networks midinfrared multicomponent gases sensor aliasing spectral cross-interference wavelength modulationspectroscopy synchronous measurement |
| ISSN号 | 2379-3694 |
| DOI | 10.1021/acssensors.4c01514 |
| 通讯作者 | Wang, Guishi(gswang@aiofm.ac.cn) |
| 英文摘要 | Owing to the overlapping and cross-interference of absorption lines in multicomponent gases, the simultaneous measurement of such gases via laser absorption spectroscopy frequently necessitates the use of supplementary pressure sensors to distinguish the spectral lines. Alternatively, it requires multiple lasers combined with time-division multiplexing to independently scan the absorption peaks of each gas, thereby preventing interference from other gases. This inevitably escalates both the cost of the system and the complexity of the gas pathway. In response to these challenges, a mid-infrared sensor employing a neural network-based decoupling algorithm for aliasing spectral is developed, enabling the simultaneous detection of methane(CH4), water vapor(H2O), and ethane(C2H6). The sensor system underwent evaluation in a controlled laboratory environment. Allan deviation analysis revealed that the minimum detection limits for CH4,H-2O, and C(2)H(6 )were 6.04, 118.44, and 1 ppb, respectively, with an averaging time of 3 s. The performance of the proposed sensor demonstrates that the aliasing spectral decoupling algorithm based on neural network combined with wavelength-modulated spectroscopy technology has the advantages of high sensitivity, low cost and low complexity, showing its potential for simultaneous detection of multicomponent trace gases in various fields. |
| WOS关键词 | METHANE ; QUANTIFICATION ; SENSOR |
| 资助项目 | National Key Research and Development Program of China[2023YFF0714700] ; National Natural Science Foundation of China[42075128] |
| WOS研究方向 | Chemistry ; Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001293311300001 |
| 出版者 | AMER CHEMICAL SOC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136088]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wang, Guishi |
| 作者单位 | 1.Univ Sci & Technol China, Coll Environm Sci & Optoelect Technol, Hefei 230026, Anhui, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiong, Hao,Shao, Ligang,Cao, Yuan,et al. Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing[J]. ACS SENSORS,2024. |
| APA | Xiong, Hao.,Shao, Ligang.,Cao, Yuan.,Wang, Guishi.,Wang, Ruifeng.,...&Gao, Xiaoming.(2024).Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing.ACS SENSORS. |
| MLA | Xiong, Hao,et al."Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing".ACS SENSORS (2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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