Mitsuaki Yano

Characteristics of MoO3 films grown by molecular beam epitaxy

Characterization of the MoO3 films grown by molecular beam epitaxy on c-plane sapphire substrates was conducted. X-ray diffraction and Raman scattering measurements revealed that amorphous, (100) β-phase, and (010) α-phase MoO3 films were preferentially grown at 150, 200, and 350 °C, respectively. Their optical bandgap energies were estimated to be ~3.5 eV for the amorphous, ~3.7 eV for the β-phase, and ~4.1 eV for the α-phase films. Intense near-band-edge emission was observed from the α-phase films even at room temperature. Postgrowth annealing effect on the β- and the α-phase MoO3 films was also studied, and it was found that the β-phase films were completely transformed into stable α-phase films at 600 °C, accompanied by a bandgap increase to ~4.1 eV.

E-nose system by using neural networks

This paper considers a new construction of an electronic nose (E-nose) system based on a neural network. The neural network used here is a competitive neural network by the learning vector quantisation (LVQ). Various odors are measured with an array of many metal oxide gas sensors. After reducing noises from the odor data which are measured under different concentrations, we take the maximum values among the time series data of odors. As they are affected by concentration levels, we use a normalization method to reduce the fluctuation of the data and reorder the measurement data according to the concentration levels to make the features invariant with the concentration levels. Those data are used to classify the various odors of teas and coffees. The classification results are about 96% in case of four kinds of teas and about 89% for five kinds of coffees.

Mixed odors classification by neural networks

Recently, the importance of odor research has been attracted attention because of its potential applications in food quality, hygiene management, agriculture field, medical field, etc. Thus, the sensing and classification of odor become important. In this paper we develop electronic noses using neural network. The neural network used here are a multi-layered neural network. The sensors are commercially available metal oxide semiconductor sensors. After classifying the various odors, we consider the classification in case that mixed odors are measured. To improve the classification accuracy we adopt a genetic algorithm to find a reduction factor to separate two mixed odors.

A potentiometric immunosensor based on a ZnO field-effect transistor

The characteristics of an aptamer-modified Ta2O5/ZnO field-effect transistor as a prototype label-free immunosensor for the potentiometric electrical detection of immunoglobulin G (IgG) were studied. The Ta2O5/ZnO film was grown on a glass substrate by a facing-target sputtering method. Stable operation in electrolyte solution with a small hysteresis width and a low gate leakage current was realized. The immunosensor exhibited a proportional sensitivity to the logarithmic human IgG concentration in the range of 0.35–23 µmol·dm−3 with a rapid response time of ∼15 s.

Electrochromic properties of single-crystalline tungsten trioxide films grown by molecular beam epitaxy

C-axis oriented single-crystalline monoclinic WO3 films were grown by molecular beam epitaxy, and studied their properties by fabricating solution-gate electrochromic devices in H2SO4 aqueous solution. These devices exhibited reversible change in the following three states; a colored state with low electrical resistivity after biasing a positive gate voltage, a transparent state with low electrical resistivity after biasing a negative gate voltage, and another transparent state with high electrical resistivity after thermal annealing. At the former two states, the c-axis length of the monoclinic WO3 films was found to expand slightly compared with that of the latter state after annealing. These results were consistently explained in terms of the protonation and hydration of WO3.

Smell classification using weakly responding data

This paper considers an array sensing system of odors and adopts a layered neural network for classification. In order to classify odors, we use data from all fourteen sensors even if some of them are not sensitive so much. We will propose three methods to use the data by insensitive sensors to find the features of odors.

Smells Classification for Human Breath Using a Layered Neural Network

Progress of sensor technology enables us to measure smells although it is based on chemical reactions. We have developed the smell classification for various subjects using layered neural networks by training a special smell. But we must learn many smells by repeating the same process and it is endless jobs since too many smells exist in the world. In case of a breath smell, several molecules are mixed. Therefore, if we can train basic components of the breath and mixtures are estimated by combining the basic components which consist the breath, it is preferable. In this paper, we develop mixed smell classification after training a neural network for each component by using a genetic algorithm to find a reduction factor from the measurement data which show the maximum value of the output of a layered neural network.

Polarity dependent radiation hardness of GaN

The lattice polarity dependent tolerance of displacement damage was studied for GaN by irradiating its +c and -c surfaces with a proton beam of 8 MeV. In agreement with the in-situ monitored increase of electrical resistance during irradiation, post-irradiation analysis by positron annihilation measurement revealed that the +c surface had a higher tolerance compared with that of the -c one. These results indicate that the defect introduction rate depends on the incident axis of proton beams.

Characteristics of a microbridge type MEMS sensor for the thermal conductivity measurement of gases by a steady state method

Thermal conductivity λ of gases was successfully measured by a simple steady state method using a microbridge type MEMS sensor fabricated on a Si substrate. The sensor consisted of a hot wire with two adjacent thermocouples on the surface of a SiO2 microbridge. The temperature increase of the microbridge was measured by supplying step-like electrical power Q to the hot wire. Due to the small heat capacity of the microbridge, the temperature increased to a saturated value ΔT within several tens of millisecond. In moving gas, the difference of the ΔT between upstream and downstream thermocouples gives the flow velocity. In static gas, this difference intrinsically becomes zero, and the heat flow QG from the hot wire to the surrounding gas is calculated using the Q to yield the same ΔT for different gases with known λ. Once QG is obtained, the λ of any unknown gases can be estimated by measuring the Q to yield the same ΔT.