We present surface plasmon enhanced fluorescence microscopy with random spatial sampling using patterned block of silver nanoislands. Rigorous coupled wave analysis was performed to confirm near-field localization on nanoislands. Random nanoislands were fabricated in silver by temperature annealing. By analyzing random near-field distribution, average size of localized fields was found to be on the order of 135 nm. Randomly localized near-fields were used to spatially sample F-actin of J774 cells (mouse macrophage cell-line). Image deconvolution algorithm based on linear imaging theory was established for stochastic estimation of fluorescent molecular distribution. The alignment between near-field distribution and raw image was performed by the patterned block. The achieved resolution is dependent upon factors including the size of localized fields and estimated to be 100-150 nm.
We have considered linear nanoaperture arrays for super-resolved live cell imaging. The nanoaperture arrays consist of nanoholes of varying diameter. Each nanohole localizes near-field distribution and produces extraordinary optical transmission (EOT) by surface plasmon localization. Much deeper light penetration was achieved in EOT than under total internal reflection. The results can be used to implement subdiffraction-limited axial resolution when applied to microscopy.
KEYWORDS: Target detection, Molecules, Near field, Biosensors, Near field optics, Surface plasmons, 3D modeling, Silver, Dielectrics, Imaging spectroscopy
We analyze sensing performances of localized surface plasmon resonance biosensors based on the overlap between target distribution and local field intensity produced by silver nanoislands in three detection models of non-specific, non-colocalized, and colocalized detection. The behavior of biomolecules was modeled to follow a probabilistic model using Poisson distribution. The results have found that the colocalized detection achieves the highest overlap signature with the smallest uncertainty and can enhance the limit of detection by more than 10000 times compared to conventional non-specific detection.
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