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

Martin Barkey; Rebecca Büchner; Alwin Wester; Stefanie D. Pritzl; Maksim Makarenko; Qizhou Wang; Thomas Weber; Dirk Trauner; Stefan A. Maier; Andrea Fratalocchi; Theobald Lohmüller; Andreas Tittl

arXiv · 2308.15644

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

Martin Barkey·Rebecca Büchner·Alwin Wester·Stefanie D. Pritzl·Maksim Makarenko·Qizhou Wang·Thomas Weber·Dirk Trauner·Stefan A. Maier·Andrea Fratalocchi·Theobald Lohmüller·Andreas Tittl

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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.

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Martin Barkey, Rebecca Büchner, Alwin Wester, Stefanie D. Pritzl, Maksim Makarenko, Qizhou Wang, Thomas Weber, Dirk Trauner, Stefan A. Maier, Andrea Fratalocchi, Theobald Lohmüller, Andreas Tittl. 2023-08-29. Pixelated high-Q metasurfaces for in-situ biospectroscopy and AI-enabled classification of lipid membrane photoswitching dynamics. https://arxiv.org/abs/2308.15644

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