Compared with silicon-based solar cells, organic-inorganic hybrid perovskite solar cells (PSCs) possess a distinct advantage, i.e., its application in the flexible field. However, the efficiency of the flexible device is still lower than that of the rigid one. First, it is found that the dense formamidinium (FA)-based perovskite film can be obtained with the help of N-methyl-2pyrrolidone (NMP) via low pressure-assisted method. In addition, CH 3 NH 3 Cl (MACl) as the additive can preferentially form MAPbCl 3−x I x perovskite seeds to induce perovskite phase transition and crystal growth. Finally, by using FAI·PbI 2 ·NMP+x%MACl as the precursor, i.e., ligand and additive synergetic process, a FA-based perovskite film with a large grain size, high crystallinity, and low trap density is obtained on a flexible substrate under ambient conditions due to the synergetic effect, e.g., MACl can enhance the crystallization of the intermediate phase of FAI·PbI 2 ·NMP. As a result, a record efficiency of 19.38% in flexible planar PSCs is achieved, and it can retain about 89% of its initial power conversion efficiency (PCE) after 230 days without encapsulation under ambient conditions. The PCE retains 92% of the initial value after 500 bending cycles with a bending radii of 10 mm. The results show a robust way to fabricate highly efficient flexible PSCs.
In this note an idea of quasi-homogeneous normal form theory using new grading functions is introduced, the definition of N th order normal form is given and some sufficient conditions for the uniqueness of normal forms are derived. A special case of the unsolved problem in a paper of Baider and Sanders for the unique normal form of Bogdanov Takens singularities is solved.
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In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the networkthus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2-6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.
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