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Nanophotonic devices excel at confining light into intense hot spots of theelectromagnetic near fields, creating unprecedented opportunities forlight-matter coupling and surface-enhanced sensing. Recently, all-dielectricmetasurfaces with ultrasharp resonances enabled by photonic bound states in thecontinuum have unlocked new functionalities for surface-enhancedbiospectroscopy by precisely targeting and reading out molecular absorptionsignatures of diverse molecular systems. However, BIC-driven molecularspectroscopy has so far focused on endpoint measurements in dry conditions,neglecting the crucial interaction dynamics of biological systems. Here, wecombine the advantages of pixelated all-dielectric metasurfaces with deeplearning-enabled feature extraction and prediction to realize an integratedoptofluidic platform for time-resolved in-situ biospectroscopy. Our approachharnesses high-Q metasurfaces specifically designed for operation in a lossyaqueous environment together with advanced spectral sampling techniques totemporally resolve the dynamic behavior of photoswitchable lipid membranes.Enabled by a software convolutional neural network, we further demonstrate thereal-time classification of the characteristic cis and trans membraneconformations with 98% accuracy. Our synergistic sensing platform incorporatingmetasurfaces, optofluidics, and deep learning opens exciting possibilities forstudying multi-molecular biological systems, ranging from the behavior oftransmembrane proteins to the dynamic processes associated with cellularcommunication.

Pixelated high-Q metasurfaces for in-situ biospectroscopy and AI-enabled classification of lipid membrane photoswitching dynamics