In Situ Doping of Nitrogen in <110>-Oriented Bulk 3C-SiC by Halide Laser Chemical Vapour Deposition.

Doping of nitrogen is a promising approach to improve the electrical conductivity of 3C-SiC and allow its application in various fields. N-doped, <110>-oriented 3C-SiC bulks with different doping concentrations were prepared via halide laser chemical vapour deposition (HLCVD) using tetrachlorosilane (SiCl4) and methane (CH4) as precursors, along with nitrogen (N2) as a dopant. We investigated the effect of the volume fraction of nitrogen (ϕN2) on the preferred orientation, microstructure, electrical conductivity (σ), deposition rate (Rdep), and optical transmittance. The preference of 3C-SiC for the <110> orientation increased with increasing ϕN2. The σ value of the N-doped 3C-SiC bulk substrates first increased and then decreased with increasing ϕN2, reaching a maximum value of 7.4 × 102 S/m at ϕN2 = 20%. Rdep showed its highest value (3000 µm/h) for the undoped sample and decreased with increasing ϕN2, reaching 1437 µm/h at ϕN2 = 30%. The transmittance of the N-doped 3C-SiC bulks decreased with ϕN2 and showed a declining trend at wavelengths longer than 1000 nm. Compared with the previously prepared <111>-oriented N-doped 3C-SiC, the high-speed preparation of <110>-oriented N-doped 3C-SiC bulks further broadens its application field.

Structural Controlling of Highly-Oriented Polycrystal 3C-SiC Bulks via Halide CVD.

Highly-oriented polycrystal 3C-SiC bulks were ultra-fast fabricated via halide chemical vapor deposition (CVD) using tetrachlorosilane (SiCl4) and methane (CH4) as precursors. The effects of deposition temperature (Tdep) and total pressure (Ptot) on the orientation and surficial morphology were investigated. The results showed that the growth orientation of 3C-SiC columnar grains was strongly influenced by Tdep. With increasing Tdep, the columnar grains transformed from <111>- to <110>-oriented. The arrangement of <111>-oriented columnar grains was controlled by Ptot. Lotus-, turtle-, thorn-, and strawberry-like surface morphologies were naturally contributed by different arrangements of <111>-oriented grains, and the deposition mechanism was discussed. The wetting behaviors of CVD-SiC samples by molten aluminum were also examined at 1173 K in a high vacuum atmosphere.

Automated segmentation of mammary gland regions in non-contrast X-ray CT images.

The identification of mammary gland regions is a necessary processing step during the anatomical structure recognition of human body and can be expected to provide useful information for breast tumor diagnosis. This paper proposes a fully automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This scheme calculates the probability of each voxel belonging to the mammary gland or chest muscle in CT images as the reference of the segmentation, and decides the mammary gland regions based on CT number automatically. The probability is estimated from the location of the mammary glands and chest muscles in CT images. The location is investigated from a knowledge base that stores pre-recognized anatomical structures using a number of different CT scans. We applied this scheme to 66 patient cases (female, age: 20-80) and evaluated the accuracy by using the Jaccard similarity coefficient (JSC) between the segmented results and two gold standards that were generated manually by 2 medical experts independently for each CT case. The result showed that the mean value of the JSC score was 0.83 with the standard deviation of 0.09 for 66 CT cases. The proposed scheme was applied to investigate the breast density distributions in normal mammary gland regions so as to demonstrate the effect and usefulness of the proposed scheme.