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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. [ABSTRACT FROM AUTHOR]

Classifying and quantifying small molecules on‐site with a rapid and cost‐effective operation are essential for pharmaceutical manufacturing, healthcare, hazardous risk control, and other applications. However, traditional molecular detection and identification methods like chromatography often involve expensive and bulky equipment and are required to be operated by trained professionals, which severely hinders the further development of small molecule‐based applications. Herein, a novel molecular detection platform is introduced by imaging a spatial gradient metasurface consisting of millions of different unique atoms and following with deep learning modeling to classify and quantify small molecules from mixed solutions accurately. The metasurface has a circular gradient geometry, which changes its transmittance intensity pattern based on the surrounding molecules under narrow‐band illumination. A convolutional neural network trained on the monochromatic images of the metasurface is employed. The results demonstrate a recognition rate of 96.88% for classification and a mean absolute error of 16.23% for quantification. This novel platform enables label‐free, sensitive, and rapid molecular classification and quantification, which opens a new avenue for small molecule classification and quantification and enables possibilities for real‐time, on‐site, and label‐free applications, including environmental monitoring, drug screening, and early diagnosis. [ABSTRACT FROM AUTHOR]