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Updated: Aug 19, 2026
Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes
Published on: March 2, 2020
Deep Learning-Assisted RTP Sensor Array Based on a Melt-Injection Reaction for Visual Discrimination of
Xiaozhen Wang1, Yutong Xuan1, Peng Yun2
1Hebei Technology Innovation Center for Energy Conversion Materials and Devices, Hebei Key Laboratory of Inorganic Nanomaterials, College of Chemistry and Material Science, Hebei Normal University, Shijiazhuang050024, China.
None:
The widespread residue and structural similarity of fluoroquinolone antibiotics (FQs) pose a critical requirement for analytical methods capable of both sensitive detection and accurate discrimination of FQ subtypes. Herein, we report a deep learning-assisted room-temperature phosphorescence (RTP) sensor array for FQs constructed via a simple melt-injection reaction. By incorporation of FQ subtypes into a urea-formaldehyde (UF) matrix, composites (UF@FQs) exhibiting tunable, ultralong RTP emission were produced. Photophysical studies reveal that the UF matrix provides a rigid protective environment that suppresses nonradiative decay and activates the weak phosphorescence of FQs through confinement and hydrogen-bonding interactions. This matrix not only amplifies the afterglow intensity but also prolongs the emission lifetime, generating fingerprint-like optical responses for the FQ subtypes. Leveraging these cross-reactive signals, we fabricated a sensor array to discriminate four different FQs and their mixtures. The array was further integrated with a deep learning model capable of visually discriminating FQs directly from afterglow images. The platform demonstrates excellent selectivity against common interferents and achieves satisfactory recovery in meat samples. This work presents a straightforward strategy for transforming nonemissive analytes into bright afterglow signatures, offering a powerful tool for on-site, high-throughput, and intelligent discrimination of antibiotics.
