3 research outputs found
Ternary strategy and material modification for high efficiency organic solar cell
No full textOrganic solar cells (OSCs) have gained plenty of attention due to their unique properties, including light-weight, environmental friendly, semi-transparent, solution-processable and low cost. However, the problems such as low Voc, low charge mobilities, and recombination losses challenge the efficiency improvement of OSCs. Exploring new ternary material systems and modifying the involved materials could be the promising approaches to overcome these issues. In this thesis. I mainly focused on introducing the third component to the OSCs and modifying materials to achieve efficient OSCs. Fullerenes have high charge mobilities and introducing of PCBM as the third component can achieve better device efficiencies. Here, we added the PCBM to the high efficiency OSCs, and the device VOC can be effectively promoted and both the Jsc and FF are also concurrently increased. A high efficiency of 16.7% were obtained with PM6:Y6:PC71BM. We also investigate their optoelectronic properties and morphology and evacuate the mechanism of the improvement from the third component. Modifying the acceptor materials is another strategy to improve the OSC performance. we introduced a strategy of modifing Y6-series NFA via combining the branched alkyl chain and alkoxy chain substitution of the outer thiophene unit in Y6. Due to the upshifted LUMO, the Y6-O2BO based binary device can yield a much-enhanced VOC of 0.96 V and a PCE of 16.2%. Ternary devices by mixing Y6 with Y6-O2BO were fabricated and the VOC could be elevated progressively with the increasing fraction of Y6-O2BO. A maximum efficiency of 17.5% of the ternary device was achieved. Modifying the polymer donor is also important for OSCs. We also focused on the application of BDF units in D18 polymer donor and synthesized a new polymer D18-Fu. Y6-1O was chosen as the acceptor. The D18-Fu-based OSC achieved an excellent PCE of 16.38%, When PC71BM was used as the third component, ternary device obtained a PCE of 17.07% with an excellent FF of 80.4%. The D18-Fu show more compact π-π stacking in the OOP direction, thus benefit the charge transport and results in higher FF.</p
A Text Visualization Method Based on A Label Cloud
Get PDFAn important direction of visualization technology research is the visualization of text data. Based on the characteristics of text information visualization, a text visualization method based on label cloud is studied, which puts forward the data index, complexity index and identification index to describe visualization, and calculates the weight of the total evaluation score by the calculation formula of three kinds of indexes. Through the visualization experiments of various kinds of text information, the results show that the method has some validity in visual measurement, and the index values at all levels are also relevant
Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization
No full textParticleboard is an important forest product that can be reprocessed using wood processing by-products. This approach has the potential to achieve significant conservation of forest resources and contribute to the protection of forest ecology. Most current detection models require a significant number of tagged samples for training. However, with the advancement of industrial technology, the prevalence of surface defects in particleboard is decreasing, making the acquisition of sample data difficult and significantly limiting the effectiveness of model training. Deep reinforcement learning-based detection methods have been shown to exhibit strong generalization ability and sample utilization efficiency when the number of samples is limited. This paper focuses on the potential application of deep reinforcement learning in particleboard defect detection and proposes a novel detection method, PPOBoardNet, for the identification of five typical defects: dust spot, glue spot, scratch, sand leak and indentation. The proposed method is based on the proximal policy optimization (PPO) algorithm of the Actor-Critic framework, and defect detection is achieved by performing a series of scaling and translation operations on the mask. The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. In addition, this paper proposes a state characterization strategy of multi-scale feature fusion, which integrates global features, local features and historical action sequences of the defect image and provides reliable guidance for action selection. On the particleboard defect dataset with limited images, PPOBoardNet achieves a mean average precision (mAP) of 79.0%, representing a 5.3% performance improvement over the YOLO series of optimal detection models. This result provides a novel technical approach to the challenge of defect detection with limited samples in the particleboard domain, with significant practical application value
