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تسجيل مستخدم جديدAll-optical reconfigurable chiral meta-molecules
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Chirality is a ubiquitous phenomenon in the natural world. Many biomolecules without inversion symmetry such as amino acids and sugars are chiral molecules. Measuring and controlling molecular chirality at a high precision down to the atomic scale are highly desired in physics, chemistry, biology, and medicine, however, have remained challenging. Herein, we achieve all-optical reconfigurable chiral meta-molecules experimentally using metallic and dielectric colloidal particles as artificial atoms or building blocks to serve at least two purposes. One is that the on-demand meta-molecules with strongly enhanced optical chirality are well-suited as substrates for surface-enhanced chiroptical spectroscopy of chiral molecules and as active components in optofluidic and nanophotonic devices. The other is that the bottom-up-assembled colloidal meta-molecules provide microscopic models to better understand the origin of chirality in the actual atomic and molecular systems. Keywords: opto-thermoelectric tweezers; optical chirality; metamolecules; bottom-up assembly
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Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, meta-molecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of meta
-molecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multi-element systems impede an effective strategy for the design and optimization of meta-molecules. Here, we propose a hybrid artificial intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of meta-molecules in metasurfaces. The framework breaks the design of the meta-molecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. We leverage the proposed framework to design metallic meta-molecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner.
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Y.P.Ruan (National Laboratory of Solid State Microstructures
, Keyn Laboratory of Intelligent Optical Sensing
, Manipulation
2021
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