How Do Surface Polar Molecules Contribute to High Open-Circuit Voltage in Perovskite Solar Cells?

To date, the improvement of open-circuit voltage (VOC ) offers a breakthrough for the performance of perovskite solar cells (PSCs) toward their theoretical limit. Surface modification through organic ammonium halide salts (e.g., phenethylammonium ions PEA+ and phenmethylammonium ions PMA+ ) is one of the most straightforward strategies to suppress defect density, thereby leading to improved VOC . However, the mechanism underlying the high voltage remains unclear. Here, polar molecular PMA+ is applied at the interface between perovskite and hole transporting layer and a remarkably high VOC of 1.175 V is obtained which corresponds to an increase of over 100 mV in comparison to the control device. It is revealed that the equivalent passivation effect of surface dipole effectively improves the splitting of the hole quasi-Fermi level. Ultimately the combined effect of defect suppression and surface dipole equivalent passivation effect leads to an overall increase in significantly enhanced VOC . The resulted PSCs device reaches an efficiency of up to 24.10%. Contributions are identified here by the surface polar molecules to the high VOC in PSCs. A fundamental mechanism is suggested by use of polar molecules which enables further high voltage, leading ways to highly efficient perovskite-based solar cells.

Jointly estimating bias field and reconstructing uniform MRI image by deep learning.

Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estimate bias field and reconstruct uniform image. By modeling the quality degradation process as the product of a spatially varying field and a uniform image, the network was trained on 800 images with true bias fields from 12 healthy subjects. A network structure of bias field estimation and uniform image reconstruction was designed to compensate for the intensity loss. To further evaluate the benefit of bias field correction, a quantitative analysis was made on image segmentation. Experimental results show that the proposed BFCNet improves the image uniformity by 8.3% and 10.1%, the segmentation accuracy by 4.1% and 6.8% on white and grey matter in T2-weighted brain images. Moreover, BFCNet outperforms the state-of-the-art traditional methods and deep learning methods on estimating bias field and preserving image structure, and BFCNet is robust to different levels of bias field and noise.