Plasmonic‐Fluorescent Au Hybrids with Silver‐Regulated Optical Switching Synergized with Deep Learning Enabling Intelligent Biosensing

Abstract The synergistic enhancement of signal transduction and data processing significantly improves the performance of biosensors. Herein, a deep learning assisted artificial biosensing platform is developed, featuring a dual‐mode signal response derived from silver‐modulated gold nanorods and gold nanoclusters, for detecting 2,4‐dichlorophenoxyacetic acid (2,4‐D). The platform integrates Ag‐regulated fluorescence intensity variations and localized surface plasmon resonance shifts, enabling simultaneous fluorescent and colorimetric readouts. Leveraging the reduction of silver ions by ascorbic acid (AA) and the inhibitory effect of 2,4‐D on AA production, this platform achieves high‐precision detection of 2,4‐D through complementary signal outputs. The colorimetric assay has a detection limit of 0.24 µg mL −1 in the range of 0.25–9 µg mL −1 , whereas the fluorescence assay has a detection limit of 1.29 ng mL −1 in the range of 20–1600 ng mL −1 , demonstrating their evident complementary effect. To facilitate point‐of‐care testing applications, convolutional neural network (CNN) algorithms are integrated to process dual‐mode signal images. Data analysis reveals a robust correlation (R 2 ≥ 0.97) within 5 s and exhibits excellent performance in the detection of real samples. This work bridges nanomaterial engineering with CNN‐augmented analytics, offering a versatile framework for next‐generation biosensors.