Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering.

Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location.

Defocused imaging-based quantification of plasmon-induced distortion of single emitter emission.

Optical properties of single emitters can be significantly improved through the interaction with plasmonic structures, leading to enhanced sensing and imaging capabilities. In turn, single emitters can act as sensitive probes of the local electromagnetic field surrounding plasmonic structures, furnishing fundamental insights into their physics and guiding the design of novel plasmonic devices. However, the interaction of emitters in the proximity to a plasmonic nanostructure causes distortion, which hinders precise estimation of position and polarization state and is one of the reasons why detection and quantification of molecular processes yet remain fundamentally challenging in this era of super-resolution.

Super-resolved three-dimensional near-field mapping by defocused imaging and tracking of fluorescent emitters.

Near-field optics is essential in many nanotechnology applications, such as implementing sensitive biosensing and imaging systems with extreme precision. Understanding optical near-fields at the nanoscale has so attracted the considerable research interest, which use a variety of analytical approaches, most notably near-field scanning microscopy. Here, we show defocused point localization mapped accumulation (DePLOMA), which can overcome many weaknesses of conventional analytical methods.

Super-resolved Raman microscopy using random structured light illumination: Concept and feasibility.

In this article, we report the use of randomly structured light illumination for chemical imaging of molecular distribution based on Raman microscopy with improved image resolution. Random structured basis images generated from temporal and spectral characteristics of the measured Raman signatures were superposed to perform structured illumination microscopy (SIM) with the blind-SIM algorithm. For experimental validation, Raman signatures corresponding to Rhodamine 6G (R6G) in the waveband of 730-760 nm and Raman shift in the range of 1096-1634 cm were extracted and reconstructed to build images of R6G.

Plasmon-enhanced fluorescence correlation spectroscopy for super-localized detection of nanoscale subcellular dynamics.

In this report, we investigate plasmon-enhanced imaging fluorescence correlation spectroscopy (p-FCS). p-FCS takes advantage of extreme light confinement by localization at nanogap-based plasmonic nanodimer arrays (PNAs) for enhanced signal-to-noise ratio (SNR) and improved precision by registration with surface plasmon microscopy images. Theoretical results corroborate the enhancement by PNAs in the far-field.

Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials.

In this work, we explore the performance of plasmonic biosensor designs that integrate metamaterials based on machine learning algorithms. The meta-plasmonic biosensors were designed for optimized detection of DNA with a layer of double negative metamaterial modeled by an effective medium. An iterative transfer matrix approach was employed to generate training and test sets of resonance characteristics in the parameter space for machine learning.

Dispersive effects in imaging polarimetry based on a wire-grid polarizer.

We explore effects of light dispersion by a wire-grid polarizer (WGP) in imaging polarimetry. The dispersive characteristics of a WGP, combined with off-axis scene incidence, cause significant non-uniformity. The normalized performance measure of contrast due to dispersion of WGP exceeded 0.

Enhanced surface plasmon microscopy based on multi-channel spatial light switching for label-free neuronal imaging.

In this paper, we have investigated multi-channel switching of light incidence in multiple directions to improve image clarity in surface plasmon microscopy (SPM) for robust and consistent imaging performance regardless of the pattern geometry and shape. Multi-channel light switching in SPM allows significant reduction of adverse scattering effects by surface plasmon (SP). For proof of concept, an eight-channel spatially switched SPM (ssSPM) system has been set up.

Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering.

A deep learning approach has been taken to improve detection characteristics of surface plasmon microscopy (SPM) of light scattering. Deep learning based on the convolutional neural network algorithm was used to estimate the effect of scattering parameters, mainly the number of scatterers. The improvement was assessed on a quantitative basis by applying the approach to SPM images formed by coherent interference of scatterers.

Superlocalized Three-Dimensional Live Imaging of Mitochondrial Dynamics in Neurons Using Plasmonic Nanohole Arrays.

We investigated the transport of neuronal mitochondria using superlocalized near-fields with plasmonic nanohole arrays (PNAs). Compared to traditional imaging techniques, PNAs create a massive array of superlocalized light beams and allow 3D mitochondrial dynamics to be sampled and extracted almost in real time. In this work, mitochondrial fluorescence excited by the PNAs was captured by an optical microscope using dual objective lenses, which produced superlocalized dynamics while minimizing light scattering by the plasmonic substrate.