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Register a new userUnidirectional light propagation through two-layer nanostructures based on optical near-field interactions
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We theoretically demonstrate direction-dependent polarization conversion efficiency, yielding unidirectional light transmission, through a two-layer nanostructure by using the angular spectrum representation of optical near-fields. The theory provides results that are consistent with electromagnetic numerical simulations. This study reveals that optical near-field interactions among nanostructured matter can provide unique optical properties, such as the unidirectionality observed here, and offers fundamental guiding principles for understanding and engineering nanostructures for realizing novel functionalities.
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We experimentally demonstrate unidirectional light transmission through two-layer nanostructured materials considering that the horizontal-to-vertical-polarization conversion efficiency in the forward direction and the vertical-to-horizontal efficiency in the backward direction, which are usually equivalent due to optical reciprocity, are different. The different ways of transferring light momentum in the forward and backward directions via optical near-fields between the layers are responsible for the unidirectionality of light, which was theoretically investigated in our recent work [J. Opt. Soc. Am. B 31, 2404-2413]. With two-layer metal nanostructures experimentally fabricated via a repeated lift-off technique, consistent optical characteristics are observed, verifying the utilization of the large momentum of optical near-fields.
Optical near-field interactions between nanostructured matter, such as quantum dots, result in unidirectional optical excitation transfer when energy dissipation is induced. This results in versatile spatiotemporal dynamics of the optical excitation, which can be controlled by engineering the dissipation processes and exploited to realize intelligent capabilities such as solution searching and decision making. Here we experimentally demonstrate the ability to solve a decision making problem on the basis of optical excitation transfer via near-field interactions by using colloidal quantum dots of different sizes, formed on a geometry-controlled substrate. We characterize the energy transfer behavior due to multiple control light patterns and experimentally demonstrate the ability to solve the multi-armed bandit problem. Our work makes a decisive step towards the practical design of nanophotonic systems capable of efficient decision making, one of the most important intellectual attributes of the human brain.
Scattering-type scanning near-field optical microscopy (s-SNOM) is instrumental in exploring polaritonic behaviors of two-dimensional (2D) materials at the nanoscale. A sharp s-SNOM tip couples momenta into 2D materials through phase matching to excite phonon polaritons, which manifest as nanoscale interference fringes in raster images. However, s-SNOM lacks the ability to detect the progression of near-field property along the perpendicular axis to the surface. Here, we perform near-field analysis of a micro-disk and a reflective edge made of isotopically pure hexagonal boron nitride (h-11BN), by using three-dimensional near-field response cubes obtained by peak force scattering-type near-field optical microscopy (PF-SNOM). Momentum quantization of polaritons from the confinement of the circular structure is revealed in situ. Moreover, tip-sample distance is found to be capable of fine-tuning the momentum of polaritons and modifying the superposition of quantized polaritonic modes. The PF-SNOM-based three-dimensional near-field analysis provides detailed characterization capability with a high spatial resolution to fully map three-dimensional near-fields of nano-photonics and polaritonic structures.
Cellulose is the most abundant bio-polymer on earth. Cellulose fibres, such as the one extracted form cotton or woodpulp, have been used by humankind for hundreds of years to make textiles and paper. Here we show how, by engineering light matter-interaction, we can optimise light scattering using exclusively cellulose nanocrystals. The produced material is sustainable, biocompatible and, when compared to ordinary microfibre-based paper, it shows enhanced scattering strength (x4) yielding a transport mean free path as low as 3.5 um in the visible light range. The experimental results are in a good agreement with the theoretical predictions obtained with a diffusive model for light propagation.
A key concept underlying the specific functionalities of metasurfaces, i.e. arrays of subwavelength nanoparticles, is the use of constituent components to shape the wavefront of the light, on-demand. Metasurfaces are versatile and novel platforms to manipulate the scattering, colour, phase or the intensity of the light. Currently, one of the typical approaches for designing a metasurface is to optimize one or two variables, among a vast number of fixed parameters, such as various materials properties and coupling effects, as well as the geometrical parameters. Ideally, it would require a multi-dimensional space optimization through direct numerical simulations. Recently, an alternative approach became quite popular allowing to reduce the computational cost significantly based on a deep-learning-assisted method. In this paper, we utilize a deep-learning approach for obtaining high-quality factor (high-Q) resonances with desired characteristics, such as linewidth, amplitude and spectral position. We exploit such high-Q resonances for the enhanced light-matter interaction in nonlinear optical metasurfaces and optomechanical vibrations, simultaneously. We demonstrate that optimized metasurfaces lead up to 400+ folds enhancement of the third harmonic generation (THG); at the same time, they also contribute to 100+ folds enhancement in optomechanical vibrations. This approach can be further used to realize structures with unconventional scattering responses.
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