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基于成像的等离子体彩虹芯片上的智能光谱仪。

Imaging-based intelligent spectrometer on a plasmonic rainbow chip.

机构信息

Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA.

Material Science Engineering, Physical Science Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia.

出版信息

Nat Commun. 2023 Apr 5;14(1):1902. doi: 10.1038/s41467-023-37628-0.

Abstract

Compact, lightweight, and on-chip spectrometers are required to develop portable and handheld sensing and analysis applications. However, the performance of these miniaturized systems is usually much lower than their benchtop laboratory counterparts due to oversimplified optical architectures. Here, we develop a compact plasmonic "rainbow" chip for rapid, accurate dual-functional spectroscopic sensing that can surpass conventional portable spectrometers under selected conditions. The nanostructure consists of one-dimensional or two-dimensional graded metallic gratings. By using a single image obtained by an ordinary camera, this compact system can accurately and precisely determine the spectroscopic and polarimetric information of the illumination spectrum. Assisted by suitably trained deep learning algorithms, we demonstrate the characterization of optical rotatory dispersion of glucose solutions at two-peak and three-peak narrowband illumination across the visible spectrum using just a single image. This system holds the potential for integration with smartphones and lab-on-a-chip systems to develop applications for in situ analysis.

摘要

为了开发便携式和手持式传感和分析应用,需要紧凑、轻便、片上的光谱仪。然而,由于过于简化的光学架构,这些小型化系统的性能通常比其台式实验室对应物要低得多。在这里,我们开发了一种紧凑的等离子体“彩虹”芯片,用于快速、准确的双功能光谱传感,在选定条件下可以超越传统的便携式光谱仪。该纳米结构由一维或二维渐变金属光栅组成。通过使用普通相机获得的单个图像,这个紧凑的系统可以准确、精确地确定照明光谱的光谱和偏振信息。在经过适当训练的深度学习算法的辅助下,我们仅使用单个图像演示了对可见光范围内双峰和三峰窄带照明的葡萄糖溶液旋光色散的特征化。该系统有可能与智能手机和片上实验室系统集成,以开发用于原位分析的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/257f/10076426/ccd34c8b56ba/41467_2023_37628_Fig1_HTML.jpg

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