Core–shell Fe2O3@MnO2 Nanoring Composites As Anode Materials for High-Performance Lithium-Ion Batteries
Ionics(2024)
Lanzhou University of Technology
Abstract
The combination of Fe2O3 with other transition metal oxides can effectively improve the electrochemical performance of Fe2O3. Transition metal oxide MnO2 was used to modify ring-like Fe2O3. Core–shell Fe2O3@MnO2 nanoring composites were synthesized by hydrothermal method. The Fe2O3@MnO2 composite exhibits excellent electrochemical performance in cycling and rate performance tests due to the synergistic effect of the Fe2O3 ring and MnO2 shell. After 100 cycles at 0.1 C current density, the specific discharge capacity can reach 893.6 mAh g−1. After cycling at 2 C high current density, when the current density is restored to 0.1 C, the reversible specific capacity can reach 867.1 mAh g−1. The experimental results show that the core–shell composite with other transition metal oxides can effectively improve the cycle stability and rate performance of Fe2O3 anode materials.
MoreTranslated text
Key words
Fe2O3@MnO2,Lithium-ion battery,Anode materials,Core–shell structure
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
Summary is being generated by the instructions you defined