Chemical suppression of contaminant metal ions using a metastable state in precolumn derivatizing HPLC: an ultratrace fluorometric detection of Al(III)

The contamination of metal ions from reagents used frequently restricts the practical detection limit of the metal ion, which itself is a source of contamination. We have found a novel solution to this problem, a chemical-suppressing method of contaminant metal ions on a reversed-phase HPLC for Al3+ with a detection limit of 7.6 x 10(-11) mol dm(-3) (2.1 ng dm(-3)) by only adding a certain agent into all stock solutions without any preconcentration or purification steps. This technique decreases the concentration of the contaminant Al3+ originating from the reagents by more than 1 order of magnitude using selective derivatization of sample Al3+ ions to a powerful fluorescent complex at a metastable state in the precolumn chelation processes. Meanwhile, the contaminant Al3+ remains as a nonfluorescent complex with a blocking reagent in order to suppress the contamination. This selective derivatization is achieved by the accumulation of several complexation processes based on the difference of formation, dissociation, and ligand-exchange kinetics and the thermodynamics between the derivatizing reagent, the 4',5'-geometorical isomer of calcein, and the blocking reagent, o,o'-dihydroxyazobenzene. This simple and smart HPLC system was validated through recovery tests of environmental and biological samples.     

Temporary overvoltage capability of surge arresters for 1000 kV power systems

Insulation level (LIWV 2250 kV) of 1000 kV gas insulated switchgear will be adopted by installing high performance 1000 kV surge arresters. But when temporary overvoltage (TOV) is generated in power systems at line faults and load rejection including Ferranti effects, high performance surge arresters flow discharge current according to the value of TOV. Therefore, it is important to check TOV discharge capability of surge arresters. This paper describes the screening of zinc oxide elements using thermography (thermal uniformity factor) obtained by an infrared radiation camera, with discharge capability of surge arresters for 1000 kV power system.     

Effect of Firing Atmosphere on Sintered and Mechanical Properties of Vanadium-Doped Alumina

Vanadium-doped alumina was sintered at 1650°C in both an oxidizing (O) and a reducing (R) atmosphere and the sintered bodies examined. In the O-sintered body, vanadium was present preferentially between the alumina grains, forming a phase (AlVO₄). In the R-sintered body, on the other hand, most of the vanadium was dissolved in alumina as V³⁺, and a small proportion of the vanadium was present as V⁴⁺ in the grain-boundary region. During O sintering, V₂O₅ doping depressed both densification and grain growth, whereas R sintering had no effect on densification but did depress grain growth. The O-sintered, V-doped body exhibited low flexural strength and hardness, whereas the R-sintered body showed comparatively high hardness.     

Thermodynamics applied to compatibility of UN with Ni,Cr and Fe

The compatibility of UN with Ni, Cr and Fe has been studied from the viewpoint of phase equilibrium relationships. The free energies of formation of UNi₅ and U₂CrN₃ have been determined from the measurement of equilibrium pressures of nitrogen for the reactions, $\text{U}{}_2\text{N}{}_3\text{+ 10 Ni ? 2UNi}{}_5\text{+}\text{3}\text{2}\text{N}{}_2$ and $\text{U}{}_2\text{CrN}{}_3\text{? 2 UN +}\text{1}\text{2}\text{Cr}{}_2\text{N +}\text{1}\text{4}\text{N}{}_2$, respectively, and the values are expressed by $\text{ΔG(UNi}{}_5\text{) = ?67 650 + 22.9T}$, $\text{ΔG(U}{}_2\text{CrN}{}_3\text{) = ? 165 900 + 54.64 T}$. Combination of these and other thermodynamic data with some additional experimental work has generated a log $\text{P}$ versus $\text{1/T}$ diagram which shows the phase relationships in the U-N-Ni-Cr system. It is concluded that UN is not compatible with Ni and Cr simultaneously but, for practical purposes, is compatible with Fe below 1400°C.     

A novel synthesis of B-Al-Ca-O-(N) glasses by self-propagating high-temperature combustion method

Glasses in the B-Al-Ca-O-(N) system have been prepared by self-propagating high-temperature combustion synthesis (SHS) directly from mixed powders of B, Al and CaO under pressurized dry-air of 3 MPa. The nitrogen content of the SHS product is 0.63 mass%, the value of which is about 30 times higher than that of conventional molten glass fabricated in air. Their initial reactants, B and Al when ignited, transform instantaneously into oxides, nitrides and oxynitride with a high exothermic heat of reaction. Sequentially the reacted products in a liquid state solidify into a glass material by rapid cooling. The glass forming region is similar to that of the same oxide glass system prepared by the conventional melting method.     

Measurement method for dispersion states of filler particles in particulate composite materials by macroscopic permittivity

A new model for estimating macroscopic permittivity was proposed to predict filler particles' dispersion states in a particulate composite material. In the model, the estimation targets are randomly packed composite materials. The composite materials were represented as a cluster of unit cells. A proposed layer structure model connected the unit cells. The macroscopic permittivity was estimated by calculating the synthetic capacity of the cluster. The proposed model was validated by comparisons between estimated and measured macroscopic permittivity of several particulate composite materials. It was also identified that the proposed model could estimate the permittivity more accurately than an existing theoretical equation's one due to considering the effects for the dispersion states of filler particles. Furthermore, it was indicated that the proposed model could also estimate the dispersion states of filler particles by the measured permittivity. The applicability of the method was confirmed by comparisons between estimated and experimental dispersion states of filler particles.     

Giant thermoelectric Seebeck coefficient of a two-dimensional electron gas in SrTiO3

Enhancement of the Seebeck coefficient (S ) without reducing the electrical conductivity (sigma) is essential to realize practical thermoelectric materials exhibiting a dimensionless figure of merit (ZT=S2 x sigma x T x kappa-1) exceeding 2, where T is the absolute temperature and kappa is the thermal conductivity. Here, we demonstrate that a high-density two-dimensional electron gas (2DEG) confined within a unit cell layer thickness in SrTiO(3) yields unusually large |S|, approximately five times larger than that of SrTiO(3) bulks, while maintaining a high sigma2DEG. In the best case, we observe |S|=850 microV K-1 and sigma2DEG=1.4 x 10(3) S cm-1. In addition, by using the kappa of bulk single-crystal SrTiO(3) at room temperature, we estimate ZT approximately 2.4 for the 2DEG, corresponding to ZT approximately 0.24 for a complete device having the 2DEG as the active region. The present approach using a 2DEG provides a new route to realize practical thermoelectric materials without the use of toxic heavy elements.     

Rule pool updating through Sarsa‐learning to improve adaptability in changing environments

Genetic network programming (GNP) is a new evolutionary algorithm using the directed graph as its chromosome. A GNP‐based rule accumulation (GNP‐RA) method was proposed previously for multiagent control. However, in changing environments where new situations appear frequently, the old rules in the rule pool become incompetent for guiding the agent's actions, and therefore updating them becomes necessary. This paper proposes a more robust rule‐based model which can adapt to the environment changes. In order to realize this, Sarsa‐learning is used as a tool to update the rules to cope with the unexperienced situations in new environments. Furthermore, Sarsa‐learning helps to generate better rules by selecting really important judgments and actions during training. In addition, the ε‐greedy policy of Sarsa enables GNP‐RA to explore the solutions space sufficiently, generating more rules. Simulations on the tile world problem show that the proposed method outperforms the previous ones, namely GP and reinforcement learning. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.