Abstract
Self-assembled molecules (SAMs) represent a highly promising hole transport layer material for perovskite solar cells (PSCs). However, their energy level mismatching, instability with perovskite, and with the substrate often influence their potential as an optimal hole transport layer (HTLs). Molecular engineering offers significant room to effectively tailor the specific component of SAMs molecules, which enhances their stability as well as carrier extraction ability in photovoltaic devices. To take advantage of this, we systematically designed four novel carbazole-based SAMs, via careful engineering of various substituents and linkers. This research aims to improve the electronic properties and interface interactions between SAMs and perovskite layers. Furthermore, we employed density functional theory (DFT) to analyze the effects of molecular design on energy levels, dipole moments, and interfacial polarization, for optimizing the hole extraction and minimizing the recombination losses. Our results demonstrated that the newly designed SAMs, particularly MeO-BzPhPACz, significantly outperformed previous counterparts in terms of photovoltaic efficiency, by achieving a remarkable power conversion efficiency (PCE) of 28.8% in single-junction PSCs. These findings highlight the potential of molecular engineering in designing organic HTLs to enhance the performance of PSCs, offering insights into the future development of efficient and stable inverted PSCs.
| Original language | English |
|---|---|
| Article number | e70431 |
| Journal | Advanced Theory and Simulations |
| Volume | 9 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2026 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2026 Wiley-VCH GmbH.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- carbazole group
- energy level alignment
- inverted perovskite solar cells
- molecular engineering
- self-assembled molecules
ASJC Scopus subject areas
- Statistics and Probability
- Numerical Analysis
- Modeling and Simulation
- General
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