Towards Enhanced Sodium Storage of Hard Carbon Anodes: Regulating the Oxygen Content in Precursor by Low-Temperature Hydrogen Reduction
ENERGY STORAGE MATERIALS(2022)
Chinese Acad Sci
Abstract
The oxygen content of precursors plays a key role in regulating the structural stability and microstructures of hard carbon anodes towards sodium-ion batteries, but this is often neglected in the previous reports. Herein, we select the esterified starch as a model precursor and quantitatively regulate its oxygen content by low-temperature hydrogen reduction. Through the correlation analysis of oxygen content changes and the microstructural information of derived hard carbons, we find that decreasing the oxygen content of precursors but guaranteeing the stability of crosslinking structures can promote the closure of open pores and the orientated alignment of carbon layers at relatively low carbonization temperature (1100 °C). The optimal sample exhibits a low specific surface area of 2.96 m2 g−1 and high proportion of pseudo-graphitic domains. The structural advantages of the hard carbon contribute to a high reversible sodium storage capacity of 369.8 mAh g−1 with an initial Coulombic efficiency (ICE) of 82.5% at 20 mA g−1. Furthermore, in-situ Raman spectroscopy results demonstrate that pseudo-graphitic structures, with large interlayer spacing, provide sufficient diffusion channels for Na+ ions intercalation and pore-filling. This work provides new insights for the microstructure regulation and design of other precursor-derived hard carbons.
MoreTranslated text
Key words
Esterified starch,Hydrogen reduction,Oxygen content regulation,Hard carbon,Sodium -ion batteries
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