Catalyst-assisted chemical etching is an emerging technology in the formation of semiconductor surfaces with various structures and functions. Because this etching utilizes chemical reactions, it does not leave mechanical damages on a processed surface. In addition, by controlling solution conditions, a semiconductor surface in contact with a catalyst can be etched selectively, which has a potential to realize surface structures with a high-aspect ratio. In general, precious metals such as Pt and Ag are used as catalysts in this etching mode. In contrast, we propose to use a carbon-based catalyst such as reduced graphene oxide which can be well dispersed in liquid. In this report, we present our recent results on nanocarbon-assisted chemical etching of a Ge surface in water with dissolved oxygen molecules. After showing fundamental properties, we demonstrate to form a trench pattern on Ge by a patterned catalytic film composed of nanocarbons.
Germanene, a graphene-like honeycomb crystal of germanium, has been attracting immense attention owing to its exotic properties such as a tunable bandgap and high carrier mobility. However, the fabrication of germanene-based electronic devices is difficult owing to its chemical instability. To overcome this problem, we proposed and developed a new method of germanene growth at graphene/Ag(111) and hexagonal boron nitride/Ag(111) interfaces. The grown germanene at the interfaces was stable in air and uniform over the entire area covered with a van der Waals (vdW) material. As an important finding, a vdW interface provides a nanoscale platform for growing germanene similarly to that in vacuum, while this cannot be achieved with a typical oxide interface (Al2O3). We believe that our work is of significantly importance not only for the growth of germanene but also for the fabrication of future germanene-based electronic devices.
Electrocatalytic denitrification is one of the most environmentally friendly approaches to remove nitrate ions from groundwater. Polycrystalline Pt and Pd electrodes modified with tin are known to show high activity in electrocatalytic nitrate reduction. In this study, we have prepared Sn-modified Pt, Pd and Pd-Pt alloy single crystalline electrodes and investigated the relationship between the electrode surface structure and nitrate reduction activity.
In this paper, we report construction of neural network potentials (NNPs) of Au-Li binary systems based on density functional theory (DFT) calculations and analyses of alloying properties. To accelerate construction of NNPs, we proposed an efficient method of structural dataset generation using the symmetry function-based principal component analysis. We investigated the mixing energy of Au1－xLix with fine composition grids, which were achieved owing to the lower computational cost of NNPs. The obtained results agree well with the DFT values, where we found previously unreported stable compositions. In addition, we examined the alloying process starting from the phase separated structure to the complete mixing phase using Au/Li superlattice structures. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. These results demonstrate the applicability of NNPs toward miscible phase and provides the understanding of the alloying mechanism.
In this study, the deposition temperature dependence of film structures, optical characteristics and thermal durabilities of hydrogenated amorphous silicon (a-Si:H) prepared by dc reactive sputtering were investigated for application to short wave near infrared (SWNIR) band pass filter (BPF). When the deposition temperature was 300℃, a film with a low hydrogen composition and less structural disorder was obtained, and it was found that thermal deteriorations after annealing (300℃, 2 hours) were suppressed. The transmittance of the BPF with stacked a-Si:H and SiO2 deposited at 300℃ were stabilized after annealing due to the characteristics of a-Si:H. However, the optical extinction coefficient k of a-Si:H became the minimum value at 200℃, and tended to increase as the deposition temperature increased. It may be attributed to the decrease in bandgap and the increase in dangling bonds due to hydrogen thermal desorption confirmed by decreasing of hydrogen composition as deposition temperature increases.
Informatics techniques support improving the efficiency of data analysis in spectral imaging measurements. Applications of informatics to the measurement technique are categorized as the measurement informatics, which is growing parallel alongside with materials informatics. Recently, we introduced a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets obtained by synchrotron soft X-ray scanning photoelectron microscopy. The advantage of the proposed method is that high-speed peak fitting can be performed with stable behavior of calculation compared to previous methods. We performed the proposed method to the series of spectra collected from graphene field-effect transistors devices. The calculation completed in less than 10 minute per set and successfully detected systematic peak shifts of the C 1s core level spectra in graphene. In contrast, the calculation time was 1day when manually performing the peak fitting by using a nonlinear least-squares method with commercial software. Moreover, we improve the spectrum-adapted EM algorithm by adopting Expectation–Conditional Maximization (ECM) algorithm to use various fitting functions and background functions. This method enables us to conduct the high-through put analysis of peak shift using various fitting functions.