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Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics.
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.
Affiliation
  • Barkey M; Chair in Hybrid Nanosystems, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Büchner R; Chair in Hybrid Nanosystems, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Wester A; Nanophotonic Systems Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
  • Pritzl SD; Chair in Hybrid Nanosystems, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Makarenko M; Chair for Photonics and Optoelectronics, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Wang Q; Department of Physics and Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands.
  • Weber T; PRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
  • Trauner D; PRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
  • Maier SA; Chair in Hybrid Nanosystems, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Fratalocchi A; Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6323, United States.
  • Lohmüller T; Chair in Hybrid Nanosystems, Nano-Institute Munich, Faculty of Physics, Ludwig-Maximilians-Universtität München, Königinstraße 10, 80539 München, Germany.
  • Tittl A; School of Physics and Astronomy, Monash University, Wellington Road, Clayton, VIC 3800, Australia.
ACS Nano ; 18(18): 11644-11654, 2024 May 07.
Article in En | MEDLINE | ID: mdl-38653474
ABSTRACT
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence Language: En Journal: ACS Nano Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence Language: En Journal: ACS Nano Year: 2024 Document type: Article Affiliation country: Country of publication: