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  4. Pixelated high-Q metasurfaces for in situ biospectroscopy and artificial intelligence-enabled classification of lipid membrane photoswitching dynamics
 
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Pixelated high-Q metasurfaces for in situ biospectroscopy and artificial intelligence-enabled classification of lipid membrane photoswitching dynamics

Publikationstyp
Journal Article
Date Issued
2024-04-23
Sprache
English
Author(s)
Barkey, Martin
Büchner, Rebecca
Wester, Alwin
Pritzl, Stefanie D.
Makarenko, Maksim
Wang, Qizhou
Weber, Thomas  
Trauner, Dirk
Maier, Stefan A.  
Fratalocchi, Andrea
Lohmüller, Theobald
Tittl, Andreas  
TORE-URI
https://hdl.handle.net/11420/62088
Journal
ACS nano  
Volume
18
Issue
18
Start Page
11644
End Page
11654
Citation
ACS Nano 18 (18): 11644-11654 (2024)
Publisher DOI
10.1021/acsnano.3c09798
Scopus ID
2-s2.0-85191942937
Publisher
American Chemical Society
Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point 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 reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.
Subjects
biosensing
bound states in the continuum
deep learning
dielectric metasurfaces
surface-enhanced spectroscopy
DDC Class
600: Technology
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