Diffusible Capping Layer Enabled Homogeneous Crystallization and Component Distribution of Hybrid Sequential Deposited Perovskite.

Nowadays, the development of wide-bandgap perovskite by thermal evaporation and spin-coating hybrid sequential deposition (HSD) method has special meaning on textured perovskite/silicon tandem solar cells. However, the common issues of insufficient reaction caused by blocking of perovskite capping layer are exacerbated in HSD, because evaporated precursors are usually denser with higher crystallinity and the widely used additive-assisted microstructure is also difficult to access. Here, a facile "diffusible perovskite capping layer" (DPCL) strategy to solve this dilemma is presented. With DPCL, crystallization alleviation of perovskite and more diffusion channels of organic salts can be realized simultaneously, contributing to a homogenization process. The resultant perovskite films exhibit complete conversion, uniform crystallization, enhanced quality, and reduced defect, leading to obvious improvements in device efficiency, repeatability, and stability. This work offers a way to promote the development of textured tandems a step further.

Aluminum and Molybdenum Co-Doped Zinc Oxide Films as Dual-Functional Carrier-Selective Contact for Silicon Solar Cells.

Aluminum-doped zinc oxide (AZO) is considered as a promising candidate as transparent conductive oxide (TCO) for silicon heterojunction solar cells due to its high carrier density, nontoxic nature, and low cost. Herein, it is presented that the transparency of the AZO film can be optimized through co-sputtering of AZO and molybdenum oxide (MoOx). Furthermore, aluminum and molybdenum co-doped zinc oxide (MAZO) can be used as both the TCO layer and electron-selective contact (ESC) for silicon heterojunction solar cells. The surface morphology, cation oxidation state, and optical and electrical properties of all MAZO films are characterized. It is found that the transmittance of all MAZO films is significantly increased at a wavelength of 450-800 nm due to MAZO with a stronger Zn-O bond and a wider band gap. The conductivity of MAZO films is approximate to AZO films at a low MoOx target deposit power (50 W), and the sheet resistance of MAZO films increases significantly by increasing the deposition power up to 100 W. Finally, the optimized MAZO films are used as TCO and ESC for silicon heterojunction solar cells, showing a power conversion efficiency of 19.58%. The results show an effective stage to improve the optical properties of AZO through co-doping and the possibility of applying MAZO as a dual-functional layer for silicon solar cells.

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.

[Research on Segmentation Method of Lung with Juxta-pleural Tumor Based on the Improved Active Shape Model].

Lung segmentation is the premise of the computer aided diagnosis of lung cancer.The traditional segmentation method based on local low-level features can not get the correct result when a tumor is connected with pleura due to their similar computed tomography(CT)values.Moreover,because the big size of tumor leads to the loss of a large part of lung area,the traditional segmentation methods of lung with juxta-pleural nodule whose diameter is less than 3cm are not suitable.Acitve shape model(ASM)combined with prior shape and low level features might be appropriate.But the search steps in conventional ASM is an optimization method based on the least square,which is sensitive to outlier marker points,and it makes profile update to the transition area of normal lung tissue and tumor rather than a true lung contour.To solve the problem,we proposed an improved ASM algorithm.Firstly,we identified these outlier marker points by distance,and then gave the different searching functions to the abnormal and normal marker points.And the search processing should be limited in volume of interesting(VOI).We selected 30 lung images with juxta-pleural tumors,and got the overlap rate with the gold standard as 93.6%.The experimental results showed that the improved ASM could get good segmentation results for the lungs with juxta-pleural tumors,and the running time of the algorithm could be tolerated in clinical.

Juxta-vascular nodule segmentation based on flow entropy and geodesic distance.

Computed aided diagnosis of lung CT data is a new quantitative analysis technique to distinguish malignant nodules from benign ones. Nodule growth rate is a key indicator to discriminate between benign and malignant nodules. Accurate nodule segmentation is the essential for calculating the nodule growth rate. However, it is difficult to segment juxta-vascular nodules, due to the similar gray levels in nodule and attached blood vessels. To distinguish the nodule region from the adjacent vessel region, a flowing direction feature, referred to as the direction of the normal vector for a pixel, is introduced. Since blood is flowing in one single direction through a vessel, the normal vectors of pixels in the vessel region typically point in similar orientations while the directions of those in the nodule region can be viewed as disorganized. The entropy value of the flowing direction features in a neighboring region for a vessel pixel is smaller than that for a nodule pixel. Moreover, vessel pixels typically have a larger geodesic distance to the nodule center than nodule pixels. Based on k -means clustering method, the flow entropy, combined with the geodesic distance, is used to segment vessel attached nodules. The validation of the proposed segmentation algorithm was carried out on juxta-vascular nodules, identified in the Chinalung-CT screening trial and on Lung Image Database Consortium (LIDC) dataset. In fully automated mode, accuracies of 92.9% (26/28), 87.5%(7/8), and 94.9% (149/157) are reached for the outlining of juxta-vascular nodules in the Chinalung-CT, and the first and second datasets of LIDC, respectively. Furthermore, it is demonstrated that the proposed method has low time complexity and high accuracies.