Dataset Information

Demonstration of nanoimprinted hyperlens array for high-throughput sub-diffraction imaging.

ABSTRACT: Overcoming the resolution limit of conventional optics is regarded as the most important issue in optical imaging science and technology. Although hyperlenses, super-resolution imaging devices based on highly anisotropic dispersion relations that allow the access of high-wavevector components, have recently achieved far-field sub-diffraction imaging in real-time, the previously demonstrated devices have suffered from the extreme difficulties of both the fabrication process and the non-artificial objects placement. This results in restrictions on the practical applications of the hyperlens devices. While implementing large-scale hyperlens arrays in conventional microscopy is desirable to solve such issues, it has not been feasible to fabricate such large-scale hyperlens array with the previously used nanofabrication methods. Here, we suggest a scalable and reliable fabrication process of a large-scale hyperlens device based on direct pattern transfer techniques. We fabricate a 5 cm × 5 cm size hyperlenses array and experimentally demonstrate that it can resolve sub-diffraction features down to 160 nm under 410 nm wavelength visible light. The array-based hyperlens device will provide a simple solution for much more practical far-field and real-time super-resolution imaging which can be widely used in optics, biology, medical science, nanotechnology and other closely related interdisciplinary fields.

SUBMITTER: Byun M

PROVIDER: S-EPMC5385565 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Demonstration of nanoimprinted hyperlens array for high-throughput sub-diffraction imaging.

Byun Minseop M Lee Dasol D Kim Minkyung M Kim Yangdoo Y Kim Kwan K Ok Jong G JG Rho Junsuk J Lee Heon H

Scientific reports 20170410

Overcoming the resolution limit of conventional optics is regarded as the most important issue in optical imaging science and technology. Although hyperlenses, super-resolution imaging devices based on highly anisotropic dispersion relations that allow the access of high-wavevector components, have recently achieved far-field sub-diffraction imaging in real-time, the previously demonstrated devices have suffered from the extreme difficulties of both the fabrication process and the non-artificial ...[more]

PMID: 28393906

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Project description:BackgroundThe expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of image data between samples we need to define the normal variation within distinct images of the same sample. Even with tightly controlled experimental conditions, protein expression can vary widely between cells, and because of the difficulty in viewing and comparing large image sets this might not be observed. Here we introduce a novel methodology, iCluster, for visualizing, clustering and comparing large sub-cellular localization image sets. For each member of an image set, iCluster generates statistics that have been found to be useful in distinguishing sub-cellular localization. The statistics are mapped into two or three dimensions such as to preserve distances between the statistics vectors. The complete image set is then visualized in two or three dimensions using the coordinates so determined. The result is images that are statistically similar are spatially close in the visualization allowing for easy comparison of images that are similar and distinguishment of dissimilar images into distinct clusters.ResultsThe methodology was tested on a set of 502 previously published images containing 10 known sub-cellular localizations. The clustering of images of like type was evaluated both by examining the classes of nearest neighbors to each image and by visual inspection. In three dimensions, 3-neighbor classification accuracy was 83.2%. Visually, each class clustered well with the majority of classes localizing to distinct regions of the space. In two dimensions, 3-neighbor classification accuracy was 68.9%, though visually clustering into classes could be readily discerned. Computational expense was found to be relatively low, and sets of up to 1400 images visualized and interacted with in real time.ConclusionThe feasibility of automated spatial layout to allow comparison and discrimination of high throughput sub-cellular imaging has been demonstrated. There are many potential applications such as image database curation, semi-automated interactive classification, outlier detection and reference image comparison. By allowing the observation of the full range of imaging data available using modern microscopes these methods will provide an invaluable tool for cell biologists.

Dataset Information

Demonstration of nanoimprinted hyperlens array for high-throughput sub-diffraction imaging.

Publications

Demonstration of nanoimprinted hyperlens array for high-throughput sub-diffraction imaging.

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