SERS nose arrays based on a signal differentiation approach for TNT gas detection
Abstract TNT, a well-known explosive, is highly toxic and difficult to decompose, making the detection of trace amounts of residual TNT in the environment a topic of significant research importance. Label-free surface-enhanced Raman spectroscopy (SERS) has been demonstrated to be capable of capturin...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-025-01656-2 |
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| _version_ | 1849226617731153920 |
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| author | Peitao Dong Haiyang Yang Tianran Wang Siyue Xiong Li Kuang Weihong Qi Xiaohua Chen Lixia Yang Qiuyun Fan Dingbang Xiao Xuezhong Wu |
| author_facet | Peitao Dong Haiyang Yang Tianran Wang Siyue Xiong Li Kuang Weihong Qi Xiaohua Chen Lixia Yang Qiuyun Fan Dingbang Xiao Xuezhong Wu |
| author_sort | Peitao Dong |
| collection | DOAJ |
| description | Abstract TNT, a well-known explosive, is highly toxic and difficult to decompose, making the detection of trace amounts of residual TNT in the environment a topic of significant research importance. Label-free surface-enhanced Raman spectroscopy (SERS) has been demonstrated to be capable of capturing rich compositional information from the sample being tested. Here we show a SERS nose array that contains six individual SERS substrates composed of different components based on a signal differentiation approach (SD-SERS arrays). In this strategy, the SD-SERS arrays integrate differentiated signal structures, physically enhanced structures, and structures with varied adsorption capabilities. Through the differentiated information obtained from SD-SERS arrays, further integration with machine learning algorithms demonstrates the high accuracy of SD-SERS arrays in classifying TNT and structurally similar 2,4-DNPA, as well as in distinguishing between gases at different concentrations. The SERS nose based on SD-SERS arrays presents a convenient and broadly applicable technology with great potential for substance classification and concentration categorization. |
| format | Article |
| id | doaj-art-fb4fb5408f504c13b2f1262f9af55255 |
| institution | Kabale University |
| issn | 2399-3669 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Chemistry |
| spelling | doaj-art-fb4fb5408f504c13b2f1262f9af552552025-08-24T11:12:22ZengNature PortfolioCommunications Chemistry2399-36692025-08-018111410.1038/s42004-025-01656-2SERS nose arrays based on a signal differentiation approach for TNT gas detectionPeitao Dong0Haiyang Yang1Tianran Wang2Siyue Xiong3Li Kuang4Weihong Qi5Xiaohua Chen6Lixia Yang7Qiuyun Fan8Dingbang Xiao9Xuezhong Wu10College of Intelligence Science and Technology, National University of Defense TechnologySchool of Computer Science and Engineering, Central South UniversityCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologySchool of Computer Science and Engineering, Central South UniversityState Key Laboratory of Solidification Processing and Center of Advanced Lubrication and Seal Materials, Northwestern Polytechnical UniversityDepartment of Laboratory Medicine, General Hospital of Central Theater CommandChangsha Institute for Food and Drug ControlHunan Changsha Ecological and Environmental Monitoring CenterCollege of Intelligence Science and Technology, National University of Defense TechnologyCollege of Intelligence Science and Technology, National University of Defense TechnologyAbstract TNT, a well-known explosive, is highly toxic and difficult to decompose, making the detection of trace amounts of residual TNT in the environment a topic of significant research importance. Label-free surface-enhanced Raman spectroscopy (SERS) has been demonstrated to be capable of capturing rich compositional information from the sample being tested. Here we show a SERS nose array that contains six individual SERS substrates composed of different components based on a signal differentiation approach (SD-SERS arrays). In this strategy, the SD-SERS arrays integrate differentiated signal structures, physically enhanced structures, and structures with varied adsorption capabilities. Through the differentiated information obtained from SD-SERS arrays, further integration with machine learning algorithms demonstrates the high accuracy of SD-SERS arrays in classifying TNT and structurally similar 2,4-DNPA, as well as in distinguishing between gases at different concentrations. The SERS nose based on SD-SERS arrays presents a convenient and broadly applicable technology with great potential for substance classification and concentration categorization.https://doi.org/10.1038/s42004-025-01656-2 |
| spellingShingle | Peitao Dong Haiyang Yang Tianran Wang Siyue Xiong Li Kuang Weihong Qi Xiaohua Chen Lixia Yang Qiuyun Fan Dingbang Xiao Xuezhong Wu SERS nose arrays based on a signal differentiation approach for TNT gas detection Communications Chemistry |
| title | SERS nose arrays based on a signal differentiation approach for TNT gas detection |
| title_full | SERS nose arrays based on a signal differentiation approach for TNT gas detection |
| title_fullStr | SERS nose arrays based on a signal differentiation approach for TNT gas detection |
| title_full_unstemmed | SERS nose arrays based on a signal differentiation approach for TNT gas detection |
| title_short | SERS nose arrays based on a signal differentiation approach for TNT gas detection |
| title_sort | sers nose arrays based on a signal differentiation approach for tnt gas detection |
| url | https://doi.org/10.1038/s42004-025-01656-2 |
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