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

Ruru Gao¹, Xiu-Shen Wei², Wei Zhao¹

¹School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Analytica Chimica Acta

|January 21, 2022

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Conjugated microporous polyphenylene (CMPs) were engineered for multi-color explosives sensing, achieving high sensitivity and low detection limits for thirteen explosive types. Machine learning models further enhanced discrimination accuracy for unknown samples and mixtures.

Area of Science:

Materials Science
Analytical Chemistry
Chemical Engineering

Background:

Conjugated microporous polyphenylene (CMPs) offer tunable fluorescent properties.
Developing sensitive and selective sensors for explosives detection remains a critical challenge.

Purpose of the Study:

To tune the fluorescent properties of CMPs for explosives sensing.
To develop a sensor array for discriminating various explosive compounds.
To evaluate the performance of statistical and machine learning methods for analyzing sensor data.

Main Methods:

Incorporation of comonomer chromophores into CMP networks to tune fluorescence.
Fabrication of multi-color CMPs for explosives sensing.
Utilizing Linear Discriminant Analysis (LDA) and Machine Learning (ML) algorithms for data analysis.

Keywords:

Conjugated microporous polymers Explosives Fluorescence Machine learning Sensor array

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Main Results:

CMPs exhibited broad sensitivity and low limits of detection (LODs) for thirteen explosive compounds.
Complete discrimination of thirteen explosives at fixed concentrations using LDA with 88% accuracy for unknown samples.
Machine learning models, particularly neural networks, achieved 96% accuracy in classifying unknown samples and mixtures.

Conclusions:

Tunable CMPs are effective materials for sensitive and selective explosives detection.
Sensor arrays combined with LDA and ML provide powerful tools for identifying and classifying explosives.
Machine learning significantly enhances the accuracy and applicability of CMP-based sensor arrays.