Issue 28, 2023

Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

Abstract

The introduction of suitable passivation materials has led to a significant improvement in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) in recent years. In this paper, the relationship between the molecular fingerprints of the passivation material and the PCE of p–i–n type PSCs is investigated using machine learning (ML). Data relating to around 100 passivation materials used to passivate the interfaces of perovskite/electron transport layers are collected. It is found that nitrogen atoms and acryloyl groups in the passivation material have the most influence on the PCE of p–i–n type PSCs. Therefore, a non-fullerene material, 3,9-bis(2-methylene-(3-(1,1-dicyanomethylene)-indanone)-5,5,11,11-tetralcis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b′]-dithiophene (ITIC), which has both nitrogen atoms and acryloyl groups, is selected to passivate perovskite defects. Moreover, according to photoluminescence and time-resolved photoluminescence analyses, treatment with ITIC can enhance charge transport and diminish the defect density of the perovskite layer. Additionally, the Urbach energy of a perovskite film treated with ITIC is reduced from 127.1 meV to 96.8 meV, which reveals that the number of defects on the perovskite surface treated with ITIC is effectively reduced. More importantly, the introduction of ITIC dramatically improves the crystallinity and reduces the surface roughness of the perovskite films. Meanwhile, density-functional theory (DFT) calculations validate that incorporating ITIC into the anti-solvent effectively passivates the uncoordinated Pb2+ ions. In addition, compared with a non-treated PSC, the PCE of the ITIC-treated PSC shows a 20.97% enhancement. To sum up, ML has great application potential in the field of photovoltaics for choosing effective passivation materials in PSCs.

Graphical abstract: Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

Supplementary files

Article information

Article type
Paper
Submitted
31 mar 2023
Accepted
08 iyn 2023
First published
12 iyn 2023

J. Mater. Chem. C, 2023,11, 9602-9610

Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

D. Huang, C. Guo, Z. Li, H. Zhou, X. Zhao, Z. Feng, R. Zhang, M. Liu, J. Liang, L. Zhao and J. Meng, J. Mater. Chem. C, 2023, 11, 9602 DOI: 10.1039/D3TC01140B

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements