Deep Learning-Assisted Sensor Array Based on Host-Guest Chemistry for Accurate Fluorescent Visual Identification of Multiple Explosives.
Journal:
Analytical chemistry
Published Date:
May 17, 2025
Abstract
Accurate and rapid discrimination of multiple explosives with high precision is of paramount importance for national security, ecological protection, and human health yet remains a significant challenge with conventional analytical techniques. Herein, we present an innovative deep learning-assisted artificial vision platform based on cyclodextrin-protected multicolor fluorescent gold nanoclusters (CD-AuNCs) with four distinct emission wavelengths, enabling the highly accurate discrimination of seven explosives. The sensor array leverages the host-guest interactions between the cyclodextrin ligands on the AuNCs' surface and the target explosives, generating unique fluorescence fingerprint patterns. Mechanistic studies reveal that the fluorescence enhancement of CD-AuNCs is attributed to ligand rigidification, while fluorescence quenching is primarily caused by photoinduced electron transfer between CD-AuNCs and explosives. The multicolor fluorescence responses are captured by using a smartphone, and the corresponding RGB values are simultaneously extracted. To enhance the recognition accuracy, a dense convolutional network (DenseNet) algorithm with advanced image recognition capability is integrated with the fluorescence sensor array. This platform achieves remarkable 100% recognition accuracy at a concentration of 200 μM, enabling the rapid and precise visual classification of explosives. The proposed strategy not only provides a powerful tool for on-site explosive monitoring but also offers a versatile platform for the intelligent detection of diverse analytes, demonstrating significant potential for real-world applications in environmental and security monitoring.
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