Pixelated high-Q metasurfaces for in-situ biospectroscopy and AI-enabled classification of lipid membrane photoswitching dynamics
arXiv (Cornell University)(2023)
摘要
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.
更多查看译文
关键词
lipid membrane,in-situ,ai-enabled
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要