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Start Over Author "Zhou, Kang" Publisher elsevier b.v.
207 results on '"Zhou, Kang"'

47. Dual interfacial modification with 1D perovskite for self-assembled monolayer based inverted perovskite solar cells.

Author

Zhou, Chao, Wang, Fei, Ai, Xinbo, Liu, Yujun, Han, Yonglei, Han, Ling, Wu, Junsheng, Zhou, Kang, Hu, Hanlin, Wang, Shiyu, Lu, Wang-Ting, Zhao, Zhuo, Wang, Yongfei, and Lin, Haoran

Abstract

Recently, self-assembled monolayer (SAM) based inverted perovskite solar cells (PSCs) have been extensively studied due to their remarkable stability and compatibility with large-area devices. Microporesat the SAM/perovskite buried interface are commonly observed, which result in the increased defect density and hindered charge extraction. Herein, a multifunctional organic material, namely 2-(methylthio)-4,5-dihydro-1 H-imidazole ammonium iodide (MTIm), is introduced as an interfacial modification material to react with the residual PbI 2 , forming one-dimensional (1D) (MTIm)PbI 3 small grains. We found that the 1D perovskite distributes at both top and buried interfaces of PSC, achieving dual-interfacial passivation of defects. Moreover, the 1D perovskite leads to a favorable n-type doping at the top interface, and relaxes the lattice strain at the buried interface. As a result, the champion PSC device achieves significantly improved power conversion efficiency from 21.7 % to 23.8 %, and retains over 90 % of its initial PCE after storing in air for 1500 hours. [Display omitted] • Dual interfacial modification of inverted perovskite solar cell with 1D perovskite is achieved. • A multifunctional organic material (MTIm) can passivate various defect sites of perovskite. • High concentration of 1D perovskite at the top interface result in favorable n-type doping. • The lattice strain at the buried interface is relaxed. • The highest power conversion efficiency of 23.8 % is among the best 1D modified perovskite solar cells. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI.

Author

Eresen, Aydin, Sun, Chong, Zhou, Kang, Shangguan, Junjie, Wang, Bin, Pan, Liang, Hu, Su, Ma, Quanhong, Yang, Jia, Zhang, Zhuoli, and Yaghmai, Vahid

Abstract

Rationale and Objectives: Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers.Materials and Methods: Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients.Results: The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01).Conclusion: MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Unsupervised domain adaptation with hard-sample dividing and processing strategy.

Author

He, Chunmei, Zhou, Kang, Tang, Jing, Wu, Shengyu, and Ye, Zhengchun

Subjects
*ARTIFICIAL neural networks, *IMAGE recognition (Computer vision), *ENTROPY, *CONFIDENCE
Abstract

In Unsupervised Domain Adaptation(UDA), the hard samples cause serious negative transfer to model performance. However, the existing UDA methods don't fully pay attention to the fine-grained processing of hard samples in target and source domains. To address these problems, the UDA framework with Hard-sample Dividing and Processing Strategy (HDPS) is proposed in this paper. In HDPS, we define sample division criteria in target domain and source domain respectively and divide the samples into easy samples, hard samples with low confidence and hard samples with high confidence. We further design systematic processing strategy of the three types of samples. Specifically, we firstly define geometric metrics based on the class prototype to divide the hard samples and eliminate them in source domain. We further design two weight allocation strategies based on sample classification entropy in target domain. Finally, we build a teacher-student guidance mechanism to learn the discriminative features of hard samples. The HDPS framework can be easily loaded to other methods as a plug-in, can promote the diversity feature learning of samples, and significantly improve the discriminant performance of the model. Extensive experiments on six benchmark datasets verify that HDPS is effective and superior to most existing UDA methods. [ABSTRACT FROM AUTHOR]

Published
2024
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