WeChat Mini Program
Old Version Features

Multiple Dynamic Hydrogen Bonding Networks Boost the Mechanical Stability of Flexible Perovskite Solar Cells

ADVANCED FUNCTIONAL MATERIALS(2024)

Liaoning Univ

Cited 4|Views15
Abstract
Flexible perovskite solar cells often experience constant or cyclic bending during their service life. Catastrophic failure of devices may occur due to the crack of polycrystalline perovskite films and delamination at the perovskite and the substrate interfaces, posing a significant stability concern. Here, a multiple dynamic hydrogen bonding polymer network is developed to enhance the mechanical strength of flexible perovskite solar cells in two ways. The main chain of poly(acrylic acid) decreases the mismatch of the coefficient of thermal expansion between the perovskite and the substrate by 16.7% through its flexibility and spatial occupation. The dopamine branch chains provide multiple dynamic hydrogen bonding sites, which contribute to increased energy dissipation upon stress deformation and reduce Young's modulus of perovskite by 54.3%. The inverted flexible perovskite solar cells achieve a champion power conversion efficiency of 23.02% and retain 81.3% of the initial PCE over 2000 h under continuous 1-sun equivalent illumination. Moreover, devices show excellent mechanical stability by remaining 90.2% of the original value after 5000 bending cycles. A multiple dynamic hydrogen bonding network (i.e., dopamine-grafted polyacrylic acid (PAA-DA)) is developed. The mechanical stability of flexible perovskite solar cells (f-PSCs) is enhanced through simultaneously tuning the two essential parameters, namely Young's modulus and thermal expansion coefficient. The f-PSCs show a high efficiency of 23.02%, long-term operational stability, and superior mechanical stability. image
More
Translated text
Key words
flexible,mechanical stability,perovskite,polymer,solar cells
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
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined