Liu Peilin, Guo Xuezheng, Liang Chengyao, Du Bingsheng, Tan Yiling, Zheng Hao, Min Chengzong, Guo Yuanjun, Yang Xi
Institute of Chemical materials, China Academy of Engineering Physics, Mianyang, Sichuan 621900, P. R. China.
School of Physics, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China.
ACS Appl Mater Interfaces. 2023 Aug 2;15(30):36539-36549. doi: 10.1021/acsami.3c06498. Epub 2023 Jul 19.
The development of an electronic nose (E-nose) for rapid explosive trace detection (ETD) has been extensively studied. However, the extremely low saturated vapor pressure of explosives becomes the major obstacle for E-nose to be applied in practical environments. In this work, we innovatively combine the decomposition characteristics of nitro explosives when exposed to ultraviolet light into gas sensors for detecting explosives, and an E-nose consisting of a SnO/WO nanocomposite-based chemiresistive sensor array with an artificial neural network is utilized to identify trace nitro-explosives by detecting their photolysis gas products, rather than the explosive molecules themselves or their saturated vapor. The ultralow detection limits for nitro-explosives can be achieved, and the detection limits toward three representative nitro-explosives of trinitrotoluene, pentaerythritol tetranitrate, and cyclotetramethylene tetranitroamine are as low as 500, 100, and 50 ng, respectively. Moreover, by extracting the features of sensor responses within 15 s, a classification system based on convolutional neural network (CNN) and long short-term memory network (LSTM) is introduced to realize fast and accurate classification. The 5-fold cross-validation results demonstrate that the CNN-LSTM model exhibits the highest classification accuracy of 97.7% compared with those of common classification models. This work realizes the detection of explosives photolysis gases using sensor technology, which provides a unique insight for the classification of trace explosives.
用于快速爆炸物痕量检测（ETD）的电子鼻（E-nose）的开发已得到广泛研究。然而，爆炸物极低的饱和蒸气压成为电子鼻在实际环境中应用的主要障碍。在这项工作中，我们创新性地将硝基爆炸物在紫外光照射下的分解特性整合到用于检测爆炸物的气体传感器中，并利用由基于SnO/WO纳米复合材料的化学电阻传感器阵列与人工神经网络组成的电子鼻，通过检测其光解气体产物来识别痕量硝基爆炸物，而非爆炸物分子本身或其饱和蒸气。可以实现对硝基爆炸物的超低检测限，对三种代表性硝基爆炸物三硝基甲苯、季戊四醇四硝酸酯和环四亚甲基四硝胺的检测限分别低至500、100和50纳克。此外，通过提取15秒内传感器响应的特征，引入了基于卷积神经网络（CNN）和长短期记忆网络（LSTM）的分类系统以实现快速准确的分类。五折交叉验证结果表明，与常见分类模型相比，CNN-LSTM模型表现出最高的分类准确率，为97.7%。这项工作利用传感器技术实现了对爆炸物光解气体的检测，为痕量爆炸物的分类提供了独特的见解。
