Effects of nitrogen substitution in amorphous carbon films on electronic structure and surface reactivity studied with x-ray and ultra-violet photoelectron spectroscopies

We investigated the effects of incorporating a very low percentage of nitrogen on the local and the electronic structure of amorphous carbon (a-C) using X-ray pho- toelectron spectroscopy (XPS) and ultra-violet photoelectron spectroscopy (UPS). Nitrogen-doped amorphous carbon films (a-CNx) with varying nitrogen content, were prepared by a thermal decomposition method using a mixture of CH4 + NH3 under atmosphere. A slight shift of the C 1s core-level spectrum toward the higher binding energy (BE) side was detected in a-CNx as a function of nitrogen content. This was interpreted as a charge transfer between carbon and nitrogen atoms rather than as a shift of the Fermi level (EF). The C 1s peak shifts can be explained by the presence of two kinds of C{N local structures and the charge transferred bulk C{C compo- nents by nitrogen atoms. The two kinds of deconvoluted C 1s components could be well correlated with the two N 1s components. Two localized states were detected below the EF in UPS spectra of a-CNx, which could be assigned to defect bands. These defects played a significant role in the surface reactivity, and were stabilized in a-CNx. The adsorption and reaction of NO were carried out on a-CNx as well as a-C films. It was found that both defect sites and O2- species were responsible on a-C, while O2- species were selectively active for NO adsorption on a-CNx. We concluded that nitrogen doping reduces defect density to stabilize the surface of a-C, while at the same time inducing the selective adsorption capability of NO

Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN

The digitalization of society is rapidly developing toward the realization of the digital twin and metaverse. In particular, point clouds are attracting attention as a media format for 3D space. Point cloud data is contaminated with noise and outliers due to measurement errors. Therefore, denoising and outlier detection are necessary for point cloud processing. Among them, PointCleanNet is an effective method for point cloud denoising and outlier detection. However, it does not consider the local geometric structure of the patch. We solve this problem by applying two types of graph convolutional layer designed based on the Dynamic Graph CNN. Experimental results show that the proposed methods outperform the conventional method in AUPR, which indicates outlier detection accuracy, and Chamfer Distance, which indicates denoising accuracy.Comment: 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE 2023

Inexact proximal DC Newton-type method for nonconvex composite functions

We consider a class of difference-of-convex (DC) optimization problems where the objective function is the sum of a smooth function and a possible nonsmooth DC function. The application of proximal DC algorithms to address this problem class is well-known. In this paper, we combine a proximal DC algorithm with an inexact proximal Newton-type method to propose an inexact proximal DC Newton-type method. We demonstrate global convergence properties of the proposed method. In addition, we give a memoryless quasi-Newton matrix for scaled proximal mappings and consider a two-dimensional system of semi-smooth equations that arise in calculating scaled proximal mappings. To efficiently obtain the scaled proximal mappings, we adopt a semi-smooth Newton method to inexactly solve the system. Finally, we present some numerical experiments to investigate the efficiency of the proposed method, showing that the proposed method outperforms existing methods