vix.ing · top · new · best · stats · spec

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

2023/08/29 by Martin Barkey, Rebecca Büchner, Barkey, Martin +21
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · #Applied Physics (physics.app-ph) #FOS: Physical sciences #Lipid Membrane Structure and Behavior #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Molecular Communication and Nanonetworks #Optics (physics.optics) #Photoreceptor and optogenetics research

paper · pdf · doi:10.48550/arxiv.2308.15644

openalex publication_date 2023/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Nanophotonic devices excel at confining light into intense hot spots of the electromagnetic near fields, creating unprecedented opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum have unlocked new functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on endpoint measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in-situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning opens exciting possibilities for studying multi-molecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.

Related