Cost-Effective Nanophotonic Metasurfaces with Spatially Gradient Structures for Ultrasensitive Imaging-Based Refractometric Sensing

Nanophotonic metasurfaces are widely utilized in various domains, such as biomedical, healthcare, and environmental monitoring, benefiting from their unique advantages of label-free, noninvasive, and real-time response. However, nanophotonic metasurfaces usually rely on sophisticated instruments, and expensive and time-consuming fabrication processes, which severely restricts their practical applications. Herein, a spatially gradient metasurface is integrated with an imaging-based sensing scheme, waiving the requirement of spectrometers and achieving an ultrahigh imaging-based sensitivity of 3321 pixels/refractive index unit superior to that characterized using conventional compact spectrometers. The metasurface is fabricated by nanoimprint lithography using a reusable cyclic olefin copolymer template featuring millions of unique nanostructures. Under the illumination of monochromatic light, the transmittance of different nanostructures on the metasurface differs, resulting in grayscale images with varied intensity distributions. Analyzing the intensity change of the metasurface's recorded image can obtain the covering medium's refractive index. Furthermore, through theory and experimentation, the high reliability of the proposed reusable and flexible template has been verified for nanophotonic metasurface fabrication which further reduces the fabrication cost of core sensing elements. Finally, with proper optimization of the metasurface structure and imaging system, this setup is expected to be applied to many emerging areas of point-of-care, real-time, and on-site biosensing. A gradient metasurface fabricated through a low-cost, reusable flexible nanoimprint template for ultrasensitive imaging-based refractometric sensing is reported. A high sensitivity of 3321 pixels/refractive index unit (RIU) and a low level of detection of 0.0003 RIU were achieved. This imaging-based sensing system is promising for future real-time on-site detection scenarios. image