Probing the Key Roles of the Back Interface in the Performance of Carbon-Based Hole-Transport-layer Free Perovskite Solar Cells

Xinwei Li,Nianqing Fu, Xiaocao Peng, Hehui Lin, Jiaang Cheng,Ziming Chen,Peng Lin,Jun Du

JOURNAL OF MATERIALS CHEMISTRY A（2024）

Cited 0|Views11

Abstract

Carbon electrodes have gained widespread attention as a sustainable, stable, and low-cost alternative to metal electrodes in perovskite solar cells (PSCs). However, the power conversion efficiency (PCE) of carbon electrode-based PSCs (C-PSCs) without the hole-transport-layer (HTL) lags far behind their metal-electrode-based counterparts (M-PSCs), and the key factors causing this PCE downgrading have not been comprehensively elucidated. Herein, we study the photovoltaic performance of various HTL-free C-PSCs employing four typical absorbers, namely MAPbI(3) (MA = CH3NH3), FAPbI(3) (FA = CH(NH2)(2)) , one-step processed FA(0.85)MA(0.15)PbI(3) (FA/MA-OS), and two-step processed FA1-xMAxPbI(3) (FA/MA-TS). Unexpectedly, we found that the PCE of C-PSCs follows the order MAPbI3 > FAPbI3 > FA/MA-TS > FA/MA-OS, quite different from that of devices with the Ag-electrode (FAPbI3 > FA/MA-TS > FA/MA-OS > MAPbI3). The in-depth studies reveal that the remarkable differences in surface roughness, surface potential (SP) distribution, and local built-in potential (Vbi) of the four absorber films directly affect both the physical and electrical contacts between the perovskite and carbon electrode, which finally determine the efficiency of C-PSCs. Among them, the MAPbI(3) films possess the smallest roughness and minimum SP gaps between the grain boundaries (GBs) and the grain interiors (GIs), which enable compact contact at the perovskite/carbon interface and higher Vbi within the C-PSCs for fast charge transfer, significantly suppressed nonradiative recombination, and thus the highest PCE (15.42%). Based on these findings, we provide some promising approaches for the development of high-efficiency C-PSCs, especially for the ones employing FA-based perovskite absorbers which have performed excellently in M-PSCs.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper