Exploration of conjugated π-bridge units in N , N -bis(4-methoxyphenyl)naphthalen-2-amine derivative-based hole transporting materials for perovskite solar cell applications: a DFT and experimental investigation.
Puhang ChengQian ChenHongyuan LiuXiaorui LiuPublished in: RSC advances (2022)
Organic small molecules as hole-transporting materials (HTMs) are an important part of perovskite solar cells (PSCs). On basis of the arylamine-based HTM ( e.g. H101), two N , N -bis(4-methoxyphenyl)naphthalen-2-amine derivative-based HTMs (CP1 and CP2) with different conjugated π-bridge cores of fused aromatic ring are designed. The CP1 and CP2 were investigated by DFT and TD-DFT in combination with Marcus theory. The calculated results indicate that the designed CP1 and CP2 have better properties with good stability and high hole mobility compared with the parent H101. To validate the computational model for the screening of N , N -bis(4-methoxyphenyl)naphthalen-2-amine derivative-based HTMs, the promising CP1 and CP2 were synthesized and applied to PSC devices. The results show that the experimental data used in this paper can reproduce the theoretical results, such as frontier molecular orbital energies, optical properties and hole mobility, very well. Among them, the results show that the power conversion efficiency (PCE) of the H101-based PSC device is 14.78%, while the CP1-based PSC shows a better PCE of 15.91%, due to its high hole mobility and uniform smooth film morphology, which ultimately promoted a higher fill factor. Finally, this work shows that the computational model is a feasible way to obtain potential N , N -bis(4-methoxyphenyl)naphthalen-2-amine derivative-based HTMs.
Keyphrases
- perovskite solar cells
- density functional theory
- ionic liquid
- solar cells
- photodynamic therapy
- molecular docking
- water soluble
- electronic health record
- machine learning
- gold nanoparticles
- mass spectrometry
- room temperature
- single cell
- high resolution
- artificial intelligence
- single molecule
- cell therapy
- deep learning