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Machine learning-assisted array from fluorescent conjugated microporous polymers for multiple explosives recognition
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DOI：10.1016/j.aca.2021.339343.png)
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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.
Keyword:
Sensor array
Explosives
Machine learning
Conjugated microporous polymers
Fluorescence
期刊
IF:
6
论文数:
3.3W
被引数:
6.1W
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暂无机构信息
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