Bi-Chiral Nanostructures Featuring Dynamic Optical Rotatory Dispersion for Polychromatic Light Multiplexing

Large-scale production of chiral nematic microspheres

The membrane emulsification technique enables the self-assembly of cellulose nanocrystals (CNCs) confined within a spherical geometry for large-scale production. The resulting solid microspheres show long-range ordering with chiral nematic structures, and this fascinating hierarchical architecture can even be transferred to mesoporous carbon or silica microparticles by a sacrificial template method.

Liquid-crystalline circularly polarised fluorescent emitters with a high luminescence dissymmetry factor

Chiral liquid-crystalline emitters based on 9,9-dimethyl-10-(4-(phenylsulfonyl)phenyl)-9,10-dihydroacridine and a functionalised binaphthol show smectic liquid crystal phases and circularly polarised blue fluorescence with a high luminescence dissymmetry factor |glum| of 0.13. Solution-processable organic light-emitting diodes (OLEDs) based on the enantiomers were explored.

Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses. The proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction, and deep-learning diffractive layers as a classifier. The resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding, leading to an accuracy as high as 97.2% for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes, as well as a resistance to eavesdropping in point-to-point free-space transmission. Moreover, through extending the target encoded modes into multiplexed OAM states, we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%. Our work provides a deep insight to the mechanism of machine learning with spatial modes basis, which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

Polychromatic Dual-Mode Imaging with Structured Chiral Photonic Crystals

Optical spatial differentiation is a typical operation of optical analog computing and can single out the edge to accelerate the subsequent image processing, but in some cases, overall information about the object needs to be presented synchronously. Here, we propose a multifunctional optical device based on structured chiral photonic crystals for the simultaneous realization of real-time dual-mode imaging. This optical differentiator is realized by self-organized large-birefringence cholesteric liquid crystals, which are photopatterned to encode with a special integrated geometric phase. Two highly spin-selective modes of second-order spatial differentiation and bright-field imaging are exhibited in the reflected and transmitted directions, respectively. Two-dimensional edges of both amplitude and phase objects have been efficiently enhanced in high contrast and the broadband spectrum. This work extends the ingenious building of hierarchical chiral nanostructures, enriches their applications in the emerging frontiers of optical computing, and boasts considerable potential in machine vision and microscopy.