The novel two-dimensional semiconductors with high carrier mobility and excellent stability are essential to the next-generation high-speed and low-power nanoelectronic devices. Because of the natural abundance, intrinsic gap, and chemical stability, metal oxides were also recently suggested as potential candidates for electronic materials. However, their carrier mobilities are typically on the order of tens of square centimeters per volt per second, much lower than that for commonly used silicon. By using first-principles calculations and deformation potential theory, we have predicted few-layer MoO as chemically stable wide-band-gap semiconductors with a considerably high acoustic-phonon-limited carrier mobility above 3000 cm V s, which makes them promising candidates for both electron- and hole-transport applications. Moreover, we also find a large in-plane anisotropy of the carrier mobility with a ratio of about 20-30 in this unusual system. Further analysis indicates that, because of the unique charge density distribution of whole valence electrons and the states near the band edge, both the elastic modulus and deformation potential are strongly directionally dependent. Also, the predicted high-mobility transport anisotropy of few-layer MoO can be attributed to the synergistic effect of the anisotropy of the elastic modulus and deformation potential. Our results not only give an insightful understanding for the high carrier mobility observed in few-layer MoO systems but also reveal the importance of the carrier-transport direction to the device performance.

No abstract

Two-dimensional (2D) layered materials and their van der Waals (vdW) heterostructures are promising candidates for highly efficient renewable energy application. On the basis of density functional theory, we investigated systematically the structure, stability, and electronic and optical properties of the group-VA trihalides AI3 (A = As, Sb) single layers and their vdW heterostructure. Our results suggest that the AI3 (A = As, Sb) single layers can be exfoliated from their bulk crystal easily and are also dynamically stable. Standard PBE predicts that the band gap of AI3 increases with element number of A, which is in conflict with the experimental results of the bulk. This unreasonable trend can be corrected when the spin–orbit coupling (SOC) effect is considered. The inconsistence between PBE and PBE+SOC calculations can be understood by the competition of two contrary effects for gap variation induced by lattice expansion and relativistic effect. Our PBE+SOC calculations indicate the AsI3 and SbI3 monolayers are potential photocatalysts for water splitting with indirect band gaps of 2.00 and 1.89 eV and moderate electron mobility (∼102 cm2 V–1 s–1). By stacking AsI3 and SbI3 vertically, a strongly binding vdW heterostructure with a type-II band alignment can be formed. Excitingly, the indirect band gap is reduced to 1.63 eV, and the absolute band edges still straddle the water redox potentials, implying that it can be used as a potential photocatalyst with strong adsorption for visible light. Moreover, such a vdW heterostructure can also be an effective excitonic solar cell material with theoretical power conversion efficiency up to 18%. These results show that the AI3 (A = As, Sb) single layers and their vdW heterostructure are potential candidates for future solar energy conversion applications.

In an underwater imaging system, a perspective camera is often placed outside a tank or in waterproof housing with a flat glass window. The refraction of light occurs when a light ray passes through the water-glass and air-glass interface, rendering the conventional multiple view geometry based on the single viewpoint (SVP) camera model invalid. While most recent underwater vision studies mainly focus on the challenging topic of calibrating such systems, no previous work has systematically studied the influence of refraction on underwater three-dimensional (3D) reconstruction. This paper demonstrates the possibility of using the SVP camera model in underwater 3D reconstruction through theoretical analysis of refractive distortion and simulations. Then, the performance of the SVP camera model in multiview underwater 3D reconstruction is quantitatively evaluated. The experimental results reveal a rather surprising and useful yet overlooked fact that the SVP camera model with radial distortion correction and focal length adjustment can compensate for refraction and achieve high accuracy in multiview underwater 3D reconstruction (within 0.7 mm for an object of dimension 200 mm) compared with the results of land-based systems. Such an observation justifies the use of the SVP camera model in underwater application for reconstructing reliable 3D scenes. Our results can be used to guide the selection of system parameters in the design of an underwater 3D imaging setup.

No abstract

The recent experimental discovery of intrinsic ferromagnetism in single-layer CrI 3 opens a new avenue to low-dimensional spintronics. However, the low Curie temperature Tc ∼ 45 K is still a large obstacle to its realistic device application. In this work, we demonstrate that the Tc and magnetic moment of CrX 3 (X=Br, I) can be enhanced simultaneously by coupling them to buckled two-dimensional Mene (M=Si, Ge) to form magnetic van der Waals (vdW) heterostructures. Our first-principles calculations reveal that n-doping of CrX 3 , induced by a significant spin-dependent interlayer charge-transfer from Mene, is responsible for its drastic enhancement of Tc and magnetic moment. Furthermore, the diversified electronic properties including halfmetallicity and semi-conductivity with configuration-dependent energy gap are also predicted in this novel vdW heterostructure, implying their broad potential applications in spintronics. Our study suggests that the vdW engineering may be an efficient way to tune the magnetic properties of 2D magnets, and the Mene/CrX 3 magnetic vdW heterostructures are wonderful candidates in spintronics and nanoelectronics device

Homography estimation refers to the problem of computing a 3 × 3 matrix which transfers image points between two images of a planar scene or two images captured from the same location. While existing algorithms exploiting hand-crafted sparse image features are well-established and efficient, recent methods based on convolutional neural networks (CNNs) achieve promising results especially for low-texture scenes. This work proposes to solve homography estimation using a hybrid framework HomoN-etComb which incorporates deep learning method and energy minimization. In particular, a customized lightweight CNN named HomoNetSim is designed to calculate an initial estimation of homography, where the network is trained in an end-to-end fashion using large amount of image pairs generated from a publicly available dataset. Due to the tiny size of the employed network, the computation time of both training and inference for HomoNetSim can be reduced significantly compared with existing CNN-based homography estimation method. The initial estimate is then refined via gradient-decent algorithm by minimizing the masked pixel-level photometric discrepancy between the warped image and the destination image in a parallel fashion. Extensive experiments on the large scale synthetic dataset demonstrate that the proposed HomoNetComb improves robustness of homography estimation significantly compared with traditional methods based on sparse image features, and meanwhile HomoNetComb achieves a mean average corner error (MACE) of 0.58 pixels which outperforms previous state-of-the-art CNN-based method. Moreover, the usefulness and applicability of the proposed method is demonstrated by applying it to solve a real-world image stitching problem.

No abstract