Rapid Detection of Trace Nitro-Explosives under UV Irradiation by Electronic Nose with Neural Networks

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 SnO₂/WO₃ 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.