Organic-inorganic hybrid film using conjugated materials and quantum dots (QDs) are of great interest for solution-processed optoelectronic devices, including photovoltaics (PVs). However, it is still challenging to fabricate conductive hybrid films to maximize their PV performance. Herein, for the first time, superior PV performance of hybrid solar cells consisting of CsPbI3 perovskite QDs and Y6 series non-fullerene molecules is demonstrated and further highlights their importance on hybrid device design. In specific, a hybrid active layer is developed using CsPbI3 QDs and non-fullerene molecules, enabling a type-II energy alignment for efficient charge transfer and extraction. Additionally, the non-fullerene molecules can well passivate the QDs, reducing surface defects and energetic disorder. The champion CsPbI3 QD/Y6-F hybrid device has a record-high efficiency of 15.05% for QD/organic hybrid PV devices, paving a new way to construct solution-processable hybrid film for efficient optoelectronic devices. 相似文献
The equation of computing the reflection coefficient between two meshes of different sizes is derived. Using the equation, quasi-network characteristics of nonuniform mesh for the finite-difference time-domain technique is found and analyzed. The so-called mesh network (MN) here is a kind of structure composed of the sections of mesh in cascade. The cell sizes of these sections change regularly. By means of choosing the number of mesh sections, length of each section, and cell sizes, some novel network characteristics are obtained, which can be used to match the reflecting wave of nonuniform mesh or improve the transmitted characteristics for a mesh wave to travel along the nonuniform mesh. Formulas for analyzing the MN are given. The characteristics are realized in both one- and three-dimensional cases. The applications and advantages of the MN are shown by computing three different structures, i.e., microstrip-gap capacitor, parallel-coupling filter, and microstrip slot-line transformer. 相似文献
Sensor-based activity recognition (AR) depends on effective feature representation and classification. However, many recent studies focus on recognition methods, but largely ignore feature representation. Benefitting from the success of Convolutional Neural Networks (CNN) in feature extraction, we propose to improve the feature representation of activities. Specifically, we use a reversed CNN to generate the significant data based on the original features and combine the raw training data with significant data to obtain to enhanced training data. The proposed method can not only train better feature extractors but also help better understand the abstract features of sensor-based activity data. To demonstrate the effectiveness of our proposed method, we conduct comparative experiments with CNN Classifier and CNN-LSTM Classifier on five public datasets, namely the UCIHAR, UniMiB SHAR, OPPORTUNITY, WISDM, and PAMAP2. In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM. The results show our proposed method consistently outperforms the state-of-the-art methods.
