Single-junction organic solar cells have reached a power conversion efficiency of 20% with narrow bandgap non-fullerene electron acceptor materials such as Y6, as well as with large band gap electron donor materials and their derivatives. The power conversion efficiency improvement of single-junction organic solar cells is a result of highly efficient light harvesting in the near-infrared light range and reduced energy losses with the most promising active layer layout currently available, Bulk-Heterojunction. Ternary blending is known to be the most advanced strategy to construct Bulk-Heterojunction structures in organic solar cells at present. In this review, we examine different devices based on Bulk-Heterojunction structures with efficient electron donors and acceptors. Then, we review the performance of binary and ternary organic solar cells with high power conversion efficiency, in conjunction with different anode and cathode interfaces used in recent studies of high-power conversion efficiency. Finally, we present perspectives on the future development of single-junction organic solar cells.

Objective evaluation of pilot fatigue is crucial for enhancing aviation safety. Although electroencephalography (EEG) is regarded as an effective tool for recognizing pilot fatigue, the direct application of deep learning models to raw EEG signals faces significant challenges due to issues such as massive data volume, excessively long training time, and model overfitting. Moreover, existing feature-based methods often suffer from data redundancy due to the lack of effective feature and channel selections, which compromises the model’s recognition efficiency and accuracy. To address these issues, this paper proposes a framework, named ASFT-Transformer, for fast and accurate detection of pilot fatigue. This framework first extracts time-domain and frequency-domain features from the four EEG frequency bands. Subsequently, it introduces a feature and channel selection strategy based on one-way analysis of variance and support vector machine (ANOVA-SVM) to identify the most fatigue-relevant features and pivotal EEG channels. Finally, the FT-Transformer (Feature Tokenizer + Transformer) model is employed for classification based on the selected features, transforming the fatigue recognition problem into a tabular data classification task. EEG data is collected from 32 pilots before and after actual simulator training to validate the proposed method. The results show that ASFT-Transformer achieved average accuracies of 97.24% and 87.72% based on cross-clip data partitioning and cross-subject data partitioning, which were significantly superior to several mainstream machine learning and deep learning models. Under the two types of cross-validation, the proposed feature and channel selection strategy not only improved the average accuracy by 2.45% and 8.07%, respectively, but also drastically reduced the average training time from above 1 h to under 10 min. This study offers civil aviation authorities and airline operators a tool to manage pilot fatigue objectively and effectively, thereby contributing to flight safety.