Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion.

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

Research on local sound field intensity control technique in metasurface based on deep neural networks.

The use of tunable metasurface technology to realize the underwater tracking function of submarines, which is one of the hotspots and difficulties in submarine design. The structure-to-sound-field metasurface design approach is a highly iterative process based on trial and error. The process is cumbersome and inefficient. Therefore, an inverse design method was proposed based on parallel deep neural networks. The method took the global and local target sound field feature information as input and the metasurface physical structure parameters as output. The deep neural network was trained using a kernel loss function based on a radial basis kernel function, which established an inverse mapping relationship between the desired sound field to the metasurface physical structure parameters. Finally, the sound field intensity modulation at a localized target range was achieved. The results indicated that within the regulated target range, this method achieved an average prediction error of less than 5 dB for 92.9% of the sample data.

Direct Z-scheme GaN/WSe2 heterostructure for enhanced photocatalytic water splitting under visible spectrum.

van der Waals heterostructures are widely used in the field of photocatalysis due to the fact that their properties can be regulated via an external electric field, strain engineering, interface rotation, alloying, doping, etc. to promote the capacity of discrete photogenerated carriers. Herein, we fabricated an innovative heterostructure by piling monolayer GaN on isolated WSe2. Subsequently, a first principles calculation based on density functional theory was performed to verify the two-dimensional GaN/WSe2 heterostructure and explore its interface stability, electronic property, carrier mobility and photocatalytic performance. The results demonstrated that the GaN/WSe2 heterostructure has a direct Z-type band arrangement and possesses a bandgap of 1.66 eV. The built-in electric field is caused by the transfer of positive charge between the WSe2 layers to the GaN layer, directly leading to the segregation of photogenerated electron-hole pairs. The GaN/WSe2 heterostructure has high carrier mobility, which is conducive to the transmission of photogenerated carriers. Furthermore, the Gibbs free energy changes to a negative value and declines continuously during the water splitting reaction into oxygen without supplementary overpotential in a neural environment, satisfying the thermodynamic demands of water splitting. These findings verify the enhanced photocatalytic water splitting under visible light and can be used as the theoretical basis for the practical application of GaN/WSe2 heterostructures.

Tunable electronic properties and Schottky barrier in a graphene/WSe2 heterostructure under out-of-plane strain and an electric field.

Tuning the electrical transport behavior and reducing the Schottky barrier height of nanoelectronic devices remain a great challenge. To solve this issue, the electronic properties and Schottky barrier of the graphene/WSe2 heterostructure are investigated by the first-principles method under out-of-plane strain and an electric field. Our results show that the WSe2 monolayer and graphene could form a stable van der Waals heterostructure and the intrinsic electronic properties are well preserved. Furthermore, a transformation of a Schottky contact from the n-type to p-type occurs at d = 3.87 Å and E = +0.06 V Å-1. In addition, an ohmic contact is formed with E = -0.50, ±0.60 V Å-1. Lastly, the effective masses of electrons and holes are calculated to be 0.057m0 and -0.055m0 at the equilibrium state, respectively, indicating that the heterostructure has a high carrier mobility. Our research will provide promising approaches for the future design and development of graphene/WSe2 nano-field effect transistors.