Numerical Analysis of Stable (FAPbI3)0.85(MAPbBr3)0.15-Based Perovskite Solar Cell with TiO2/ZnO Double Electron Layer.

Although perovskite solar cells have achieved excellent photoelectric conversion efficiencies, there are still some shortcomings, such as defects inside and at the interface as well as energy level dislocation, which may lead to non-radiative recombination and reduce stability. Therefore, in this study, a double electron transport layer (ETL) structure of FTO/TiO2/ZnO/(FAPbI3)0.85(MAPbBr3)0.15/Spiro-OMeTAD is investigated and compared with single ETL structures of FTO/TiO2/(FAPbI3)0.85(MAPbBr3)0.15/Spiro-OMeTAD and FTO/ZnO/(FAPbI3)0.85(MAPbBr3)0.15/Spiro-OMeTAD using the SCAPS-1D simulation software, with special attention paid to the defect density in the perovskite active layer, defect density at the interface between the ETL and the perovskite active layer, and temperature. Simulation results reveal that the proposed double ETL structure could effectively reduce the energy level dislocation and inhibit the non-radiative recombination. The increases in the defect density in the perovskite active layer, the defect density at the interface between the ETL and the perovskite active layer, and the temperature all facilitate carrier recombination. Compared with the single ETL structure, the double ETL structure has a higher tolerance for defect density and temperature. The simulation outcomes also confirm the possibility of preparing a stable perovskite solar cell.

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy.

Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.

Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training.

Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data.

Clearance of free silica in rat lungs by spraying with chinese herbal kombucha.

The effects of spraying with kombucha and Chinese herbal kombucha were compared with treatments with tetrandrine in a rat silicosis model. Silica dust (50 mg) was injected into the lungs of rats, which were then treated with one of the experimental treatments for a month. The rats were then killed and the effects of the treatments were evaluated by examining the extent and severity of the histopathological lesions in the animals' lungs, measuring their organ coefficients and lung collagen contents, determining the dry and wet weights of their lungs, and measuring the free silica content of the dried lungs. In addition, lavage was performed on whole lungs taken from selected rats, and the numbers and types of cells in the lavage fluid were counted. The most effective treatment in terms of the ability to reduce lung collagen content and minimize the formation of pulmonary histopathological lesions was tetrandrine treatment, followed by Chinese herbal kombucha and non-Chinese herbal kombucha. However, the lavage fluid cell counts indicated that tetrandrine treatment had severe adverse effects on macrophage viability. This effect was much less pronounced for the kombucha and Chinese herbal kombucha treatments. Moreover, the free silica levels in the lungs of animals treated with Chinese herbal kombucha were significantly lower than those for any other silica-exposed group. These preliminary results indicate that spraying with Chinese herbal kombucha preparations can effectively promote the discharge of silica dust from lung tissues. Chinese herbal kombucha inhalation may thus be a useful new treatment for silicosis and other pneumoconiosis diseases.