Machine learning-assisted array from fluorescent conjugated microporous polymers for multiple explosives recognition

The fluorescent properties of conjugated microporous polyphenylene (CMPs) were tuned through a wide range by inclusion of small amount of comonomer as chromophore in the network. The multi-color CMPs were used for explosives sensing and demonstrated broad sensitivity (ranging from -0.01888 mu M-1 to -0.00467 mu M-1) and LODs (ranging from 31.0 nM to 125.3 nM) against thirteen explosive compounds including nitroaromatics (NACs), nitramines (NAMs) and nitrogen-rich heterocycles (NRHCs). The CMPs were also developed as a sensor array for discrimination of thirteen explosives, specifically including NT, p-DNB, DNT, TNT, TNP, TNR, RDX, HMX, CL-20, FOX-7, NTO, DABT and DHT. By using classical statistical method "Linear Discriminant Analysis (LDA)", the thirteen explosives at a fixed concentration were completely discriminated and unknown test samples were indentied with 88% classification accuracy. Moreover, explosives in different concentrations and the mixtures of explosives were also successfully classified. Compared with LDA, Machine Learning algorithms have significant advantages in analyzing the array-based sensing data. Different Machine Learning models for pattern recognition have also been implemented and discussed here and much higher accuracy (96% for "neural network") can be achieved in predicting unknown test samples after training. (C) 2021 Elsevier B.V. All rights reserved.