RESUMEN

Data-driven design approaches based on deep learning have been introduced into nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report a convolutional neural network (CNN) used to perform the prediction of surface plasmon polariton (SPP) grating output spectra, which is not limited by predefined shapes. For a random given structure, the network can output spectra with effective prediction, so that the simulation results are in excellent agreement with the network prediction results. Compared with the traditional finite-difference time-domain (FDTD) method, the CNN model proposed in this Letter has absolute advantages in speed. Previous studies often used a regular device structure to modify its parameters for prediction; the random structure design method adopted in this Letter also provides a new, to the best of knowledge, idea for device design.

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RESUMEN

Aminooligosaccharides possess various biological activities and can exploit wide applications in food, pharmaceutical and cosmetic industries. Commercial aminooligosaccharides are often prepared by the hydrolysis of chitin and chitosan. In this study, a novel GH family 20 ß-N-acetylhexosaminidases gene named AoNagase was cloned from Aspergillus oryzae and expressed in Pichia pastoris. The purified AoNagase had maximal activity at pH 5.5 and 65°C. It exhibited good pH stability in the range of pH 6.0-7.5 and at temperatures below 50°C. AoNagase was capable of hydrolyzing not only colloidal chitosan (508.26 U/mg) but also chitin (29.78 U/mg). The kinetic parameters (K m and V max ) of AoNagase were 1.51 mM, 1106.02 U/mg for chitosan and 0.41 mM, 40.31 U/mg for colloidal chitin. To our knowledge, AoNagase is the first GH family 20 ß-N-acetylhexosaminidase capable of hydrolyzing both chitosan and chitin. AoNagase is an endo-type ß-N-acetylhexosaminidases and can potentially be used for the manufacturing of aminooligosaccharides.