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- Chen, Haoran, et al.
(författare)
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Decoupling engineering of formamidinium-cesium perovskites for efficient photovoltaics
- 2022
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Ingår i: National Science Review. - : Oxford University Press. - 2095-5138 .- 2053-714X. ; 9:10
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Tidskriftsartikel (refereegranskat)abstract
- Sequential Cs incorporation strategy is developed to decouple crystallization of FACs perovskite with reduced electron-phonon coupling, resulting in highly stable FACs tri-iodide perovskite photovoltaics with record efficiency. Although pure formamidinium iodide perovskite (FAPbI(3)) possesses an optimal gap for photovoltaics, their poor phase stability limits the long-term operational stability of the devices. A promising approach to enhance their phase stability is to incorporate cesium into FAPbI(3). However, state-of-the-art formamidinium-cesium (FA-Cs) iodide perovskites demonstrate much worse efficiency compared with FAPbI(3), limited by the different crystallization dynamics of formamidinium and cesium, which result in poor composition homogeneity and high trap densities. We develop a novel strategy of crystallization decoupling processes of formamidinium and cesium via a sequential cesium incorporation approach. As such, we obtain highly reproducible, highly efficient and stable solar cells based on FA(1)(-)(x)Cs(x)PbI(3) (x = 0.05-0.16) films with uniform composition distribution in the nanoscale and low defect densities. We also revealed a new stabilization mechanism for Cs doping to stabilize FAPbI(3), i.e. the incorporation of Cs into FAPbI(3) significantly reduces the electron-phonon coupling strength to suppress ionic migration, thereby improving the stability of FA-Cs-based devices.
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| 2. |
- Hu, Yan, 1985-, et al.
(författare)
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An Optimized CNN Model for Engagement Recognition in an E-Learning Environment
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:16
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Tidskriftsartikel (refereegranskat)abstract
- In the wake of the restrictions imposed on social interactions due to the COVID-19 pandemic, traditional classroom education was replaced by distance education in many universities. Under the changed circumstances, students are required to learn more independently. The challenge for teachers has been to duly ascertain students’ learning efficiency and engagement during online lectures. This paper proposes an optimized lightweight convolutional neural network (CNN) model for engagement recognition within a distance-learning setup through facial expressions. The ShuffleNet v2 architecture was selected, as this model can easily adapt to mobile platforms and deliver outstanding performance compared to other lightweight models. The proposed model was trained, tested, evaluated and compared with other CNN models. The results of our experiment showed that an optimized model based on the ShuffleNet v2 architecture with a change of activation function and the introduction of an attention mechanism provides the best performance concerning engagement recognition. Further, our proposed model outperforms many existing works in engagement recognition on the same database. Finally, this model is suitable for student engagement recognition for distance learning on mobile platforms. © 2022 by the authors.
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