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Shin-Ichi Nishimura

Publications and source records attributed to Shin-Ichi Nishimura.

2 recordsLinked to original sources

Room-temperature miscibility gap in LixFePO4.

The rechargeable lithium-ion cell is an advanced energy-storage system. However, high cost, safety hazards, and chemical instability prohibit its use in large-scale applications. An alternative cathode material, LiFePO(4), solves these problems, but has a kinetic problem involving strong electron/hole localization. One reason for this is believed to be the limited carrier density in the fixed monovalent Fe(3+)PO(4)/LiFe(2+)PO(4) two-phase electrode reaction in LixFePO4. Here, we provide experimental evidence that LixFePO4, at room temperature, can be described as a mixture of the Fe(3+)/Fe(2+) mixed-valent intermediate LialphaFePO4 and Li1-betaFePO4 phases. Using powder neutron diffraction, the site occupancy numbers for lithium in each phase were refined to be alpha=0.05 and 1-beta=0.89. The corresponding solid solution ranges outside the miscibility gap (0<x<alpha,1-beta<x<1) were detected by the anomaly in the configurational entropy, and also by the deviation of the open-circuit voltage from the constant equilibrium potential. These findings encourage further improvement of this important class of compounds at ambient temperatures.

Journal Article↗

Improved parameter estimation for variance-stabilizing transformation of gene-expression microarray data.

A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outlier exclusion and works well even in the case of small sample size. Validation of this method with experimental data has suggested that it is superior to the conventional method.

Algorithms↗