Related papers: A comparative study on defect estimation using XPS…
The supercell approach enables us to treat the electronic structure of defective crystals, but the calculated energy bands are too complicated to understand or to compare with angle-resolved photoemission spectra because of inevitable zone…
The most important systematical errors in determination of level density and radiative strength functions for deformed nuclei have been estimated from the gamma-ray spectra in nuclear reactions like the stripping or pickup reactions.
X-ray photoelectron spectroscopy (XPS) is a widely used technique for chemical analysis of solid surfaces, sensitive to the chemical environments of atoms via core-level binding energy shifts. While modern instruments allow experimental…
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to…
Surface enhanced Raman spectroscopy, is a technique of fundamental importance to analytical science and technology where the amplified Raman spectrum of analytes is used for chemical fingerprinting. Here, we showcase an engineered…
The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility…
Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and…
We investigated Raman spectra of single-layer and multi-layer graphene under ultraviolet laser excitation at the wavelength of 325 nm. It was found that while the G peak of graphene remains pronounced in UV Raman spectra, the 2D band…
We propose a new approach, the calibrated nonparametric scan statistic (CNSS), for more accurate detection of anomalous patterns in large-scale, real-world graphs. Scan statistics identify connected subgraphs that are interesting or…
Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we…
We synthesized three-dimensional nanoporous graphene films by a chemical vapor deposition method with nanoporous copper as a catalytic substrate. The resulting nanoporous graphene has the same average pore size as the underlying copper…
The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets;…
Raman spectroscopy is one of the most extended experimental techniques to investigate thin-layered 2D materials. For a complete understanding and modeling of the Raman spectrum of a novel 2D material, it is often necessary to combine the…
Detailed analysis of scanning probe microscopy (SPM) data acquired for faceted and non-flat surfaces is usually complicated due to the presence of a large number of surface areas tilted by large/variable angles relative to the scanning…
Edges naturally exist in a single-layer graphene sample. Similarly, individual graphene layers in a multilayer graphene sample contribute their own edges. We study the Raman spectrum at the edge of a graphene layer laid on n layer graphene…
X-ray photoelectron spectroscopy (XPS) is among the most widely used methods for surface characterization. Currently, the analysis of XPS data is almost exclusively based on the main emission peak of a given element and the rest of the…
We present a computational study on the topology, energetics and structural deformations for a large number of experimentally observed defect configurations in graphene. We find that both the number of lost hexagonal carbon rings and…
Convolutional neural networks (CNNs) are widely used for image recognition and text analysis, and have been suggested for application on one-dimensional data as a way to reduce the need for pre-processing steps. Pre-processing is an…
We demonstrate insights into the three-dimensional structure of defects in graphene, in particular grain boundaries, obtained via a new approach from two transmission electron microscopy images recorded at different angles. The structure is…
With the advent of both modern X-ray fluorescence (XRF) methods and improved analytical reliability requirements the demand for suitable reference samples has increased. Especially in nanotechnology with the very low areal mass depositions,…