Highly stable mixed-phase Cs-Cu-I films with tunable optoelectronic properties for UVB photodetector applications.

Ultraviolet (UV) photodetector plays an important role in military, civilian and people's daily life, and is an indispensable part of spectral detection. However, photodetectors target at the UVB region (280-320 nm) are rarely reported, and the devices detected by medium-wave UV light generally have problems such as low detection rate, low sensitivity, and poor stability, which are difficult to meet the market application needs. Herein, Cs-Cu-I films with mixed-phase have been prepared by vacuum thermal evaporation. By adjusting the proportion of evaporation sources (CsI and CuI), the optical bandgaps of mixed-phase Cs-Cu-I films can be tuned between 3.7 eV and 4.1 eV. This absorption cut-off edge is exactly at both ends of the UVB band, which indicating its potential application in the field of UVB detection. Finally, the photodetectors based on Cs-Cu-I/n-Si heterojunction are fabricated. The photodetector shows good spectral selectivity for UVB band, and has a photoresponsivity of 22 mA/W, a specific detectivity of 1.83*1011 Jones, an EQE over 8.7% and an on/off ratio above 20.

3D segmentation of lungs with juxta-pleural tumor using the improved active shape model approach.

BACKGROUND AND OBJECTIVE: At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed. METHODS: To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM. RESULTS: Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%. CONCLUSIONS: The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.