SERS nose arrays based on a signal differentiation approach for TNT gas detection
Peitao Dong, Haiyang Yang, T. H. Wang, Siyue Xiong, Li Kuang, Weihong Qi, Xiaohua Chen, Lixia Yang, Qiuyun Fan, Dingbang Xiao, Xuezhong Wu
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.