In order to analyze the threshold of photoelectron yield spectroscopy (PYS) automatically, we compared the accuracy of the analysis between a fitting (least squares method) method and a machine learning method.
Both methods are comparable in terms of the performance of analysis, but machine learning shows better performance in data with high noise components.
In the analysis of measured Au data, machine learning shows close to the analyzed value by an analyst, but the fitting method shows the different value due to the influence of a high signal intensity.
In the field of material science the size of measured or calculated data is rather limited compared to the variety of materials, causing difficulty in material search with big-data type machine learning techniques. The present search system is designed to help material search by relating various material properties, enabling utilizing data from higher perspective viewpoint. Among properties in different categories such as thermal conductivity and electrical conductivity, relationship is extracted mainly from scientific principles. The relationships are made into a database and can be searched using algorithms in graph theory. In this paper, the framework, brief description of the relationship database, the graph expression of the relationships, and a relationship search using graph theory of the developed prototype system are presented. Examples are given to demonstrate the usefulness of the search system on material search.
In a system of Au/TiO2 prepared on a Ti polycrystalline foil, CO oxidation kinetics is investigated. Corresponding to Au overlayer coverage from multilayer to sub-monolayer, bulk like Au 5d states change to those of small Au clusters in the valence region. The rate of CO oxidation depends on Au coverage, while the activation energy is kept to be almost 35 kJ/mol. Hence, it is most likely that CO oxidation proceeds by the presence of low-coordinated Au atoms on the surface of Au particles.
Advanced principal identification technique is required owing to development of information-oriented society. One of the most widely used authentication technologies today is the biometric authentication. However, there are fragilitas, such as falsification or imitation. In this situation, an artifact metrics authentication technique was proposed. It is authenticated by intrinsic random patterns of the artifacts. Since a carbon nanotube has various properties e.g. Raman scattering and photoluminescence, we demonstrate authentication of carbon nanotube composite papers for artifact metrics. The merits are easiness of normalization and fabrication processes, and impossible cloning. In this study, an estimated error rate as small as 10−15 was achieved when random patterns of densities, positions, or the different chiralities were authenticated.
Water molecules adsorbed on metal surfaces can assemble into various H-bonding networks. Visualization of individual water molecules is one of the shortest route to clarify wetting processes on the surfaces ; however, in many cases, it remains difficult to identify the atomic configurations in the first water layers by scanning tunneling microscopy (STM) images alone. In this study, we observed one-dimensional water chains on Cu(110) by non-contact atomic force microscopy (AFM) with a probe tip terminated by carbon monoxide. Oxygen skeletons in the AFM images reveal the local structures in the water chains at high spatial resolution beyond STM. Therefore, AFM is an alternative option for characterization of water assembles on surfaces.
Scanning nonlinear dielectric microscopy (SNDM) can easily distinguish the dopant type (pn) and has a wide dynamic range of sensitivity from low to high concentrations of dopants, because it has a high sensitivity to capacitance variation on the order of 10−22 F. It is also applicable to the analysis of compound semiconductors with much lower signal levels than Si. We can avoid misjudgments from the two-valued function (contrast reversal) problem of dC/dV signals. As the extended versions of SNDM, super-higher-order SNDM, local-deep-level transient spectroscopy have been developed and introduced. The favorable features of SNDM originate from its significantly high sensitivity.
We have developed a new non-evaporable getter (NEG) coating using titanium and palladium vacuum sublimation. The inner wall of the test chamber was coated with a 1-µm Ti thin film using the Ti sublimation pumps under a vacuum of 6.5×10−6 Pa (Ti-coated chamber). This chamber was then coated with a 10-nm Pd thin film using the Pd sublimation filament under a vacuum of 2.4×10−4 Pa (Pd/Ti-coated chamber). The uncoated, Ti-coated, and Pd/Ti-coated chambers were baked for 6 h at a maximum temperature of 185℃. Five hours after closing the valve, the pressures in the Pd/Ti-coated chamber were about 1.4×10−6 Pa even after six heating–venting cycles. The relatively low activation temperature was attributed to the low concentration of oxygen in the Pd/Ti thin films. The Pd/Ti coating can be used for vacuum systems that are frequently vented.
An apparatus for terahertz spectroscopy in an ultrahigh vacuum has been developed. We used broadband Mylar for the beam splitter in a Fourier transform spectrometer, diamond for optical windows, and a liquid-helium-cooled Si bolometer for the terahertz detector to achieve the spectral range of 50–650 cm−1. For the purpose of keeping the ultrahigh vacuum in a sample chamber, we evacuated the whole optical path by turbo molecular pumps and made its pressure down to 10−4 Pa. Using this apparatus, we measured temperature dependence of the terahertz spectrum of D2O ice vapor-deposited at 10 K. The spectral changes due to the structural transformation from amorphous ice to cubic crystalline ice Ic were successfully observed in the range of 140–160 K. We report the spectral difference between low-density and high-density amorphous ice as well as that between hexagonal crystalline ice Ih and cubic one.