Effects of Finite Deformed Length in Carbon Nanotubes

The effect of finite deformed length is demonstrated by squashing an armchair (10,10) single-walled carbon nanotube with two finite tips. Only when the deformed length is long enough, an effectual metal-semiconductor-metal heterojunction can be formed in the metallic tube. The effect of finite deformed length is explained by the quantum tunnelling effect. Furthermore, some conceptual designs of nanoscale devices are proposed from the metal-semiconductor-metal heterojunction.Comment: 4 pages, 4 figure

Structural Trends Interpretation of the Metal-to-Semiconductor Transition in Deformed Carbon Nanotubes

Two mechanisms that drive metal-to-semiconductor transitions in single-walled carbon nanotubes are theoretically analyzed through a simple tight-binding model. By considering simple structural trends, the results demonstrate that metal-to-semiconductor transitions can be induced more readily in metallic zigzag nanotubes than in armchair nanotubes. Furthermore, it is shown that both mechanisms have the effect of making the two originally equivalent sublattices physically distinguishable.Comment: 4 pages, 4 figure

Effect of Na Doping on the Nanostructures and Electrical Properties of ZnO Nanorod Arrays

The p-type ZnO nanorod arrays were prepared by doping Na with hydrothermal method. The structural, electrical, and optical properties were explored by XRD, Hall-effect, PL, and Raman spectra. The carrier concentrations and the mobility of Na-doped ZnO nanorod arrays are arranged from 1.4×1016 cm−3 to 1.7×1017 cm−3 and 0.45 cm2 v−1 s−1 to 106 cm2 v−1 s−1, respectively

Generative Modeling for Tabular Data via Penalized Optimal Transport Network

The task of precisely learning the probability distribution of rows within tabular data and producing authentic synthetic samples is both crucial and non-trivial. Wasserstein generative adversarial network (WGAN) marks a notable improvement in generative modeling, addressing the challenges faced by its predecessor, generative adversarial network. However, due to the mixed data types and multimodalities prevalent in tabular data, the delicate equilibrium between the generator and discriminator, as well as the inherent instability of Wasserstein distance in high dimensions, WGAN often fails to produce high-fidelity samples. To this end, we propose POTNet (Penalized Optimal Transport Network), a generative deep neural network based on a novel, robust, and interpretable marginally-penalized Wasserstein (MPW) loss. POTNet can effectively model tabular data containing both categorical and continuous features. Moreover, it offers the flexibility to condition on a subset of features. We provide theoretical justifications for the motivation behind the MPW loss. We also empirically demonstrate the effectiveness of our proposed method on four different benchmarks across a variety of real-world and simulated datasets. Our proposed model achieves orders of magnitude speedup during the sampling stage compared to state-of-the-art generative models for tabular data, thereby enabling efficient large-scale synthetic data generation.Comment: 37 pages, 23 figure