Facile Self-Assembled Monolayer Deposition on Copper Foil for High-Performance Lithium-Metal Batteries
ELECTROCHIMICA ACTA(2024)
Gachon Univ | Incheon Natl Univ | Sungkyunkwan Univ
Abstract
Li metal is widely acknowledged as the optimal negative electrode material for high-energy-density Li rechargeable batteries. However, challenges arise owing to the formation of Li dendrites and poor surface stability, leading to reduced cycle life and energy efficiency, thereby limiting widespread application. In this study, we introduced a novel approach of a molecular single-layer surface modification onto a Cu foil using a selfassembled monolayer with hexamethyldisilane (HMDS) to enhance the performance of Li-metal electrodes. The deposition of a single molecular layer at the & Aring;-level was achieved by a simple combination of precursor casting and heat treatment without greatly affecting the energy and power density. Furthermore, this process was both scalable and cost-effective, making it highly suitable for large-scale battery manufacturing. The molecular deposition of HMDS effectively mitigated electrolyte decomposition and promoted uniform Li deposition by enhancing the surface stability under repeated Li plating-stripping processes. Optical microscopy revealed the homogeneous Li plating even after extended cycling; the first cycle on the modified surface depicted uniform plating without blank spots owing to the reduced gas-phase by-products from electrolyte decomposition. Thus, HMDS modification greatly improved the cycle stability of Li-metal batteries by successfully mitigating polarization due to electrolyte decomposition. Such improvement improved the Coulombic efficiencies and enhanced the energy efficiency, especially over a wide current density range of 1-5 mA cm- 2. Furthermore, full cells with LiFePO4 cathodes and HMDS-modified Cu foils exhibited extended cyclability with increased energy efficiencies.
MoreTranslated text
Key words
Li metal batteries,Li dendrite,Cu current collector,Self-assembled monolayer,Hexamethyldisilane
求助PDF
上传PDF
View via Publisher
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