Chrome Extension
WeChat Mini Program
Use on ChatGLM

Effect of Precursor Particle Size on the Microstructure and Na Storage Performance of Semi-Coke Derived Carbon

JOURNAL OF ENERGY STORAGE(2024)

Henan Polytech Univ

Cited 0|Views7
Abstract
The microstructure of carbon-based materials exerts a decisive influence on their Na storage performance. Furthermore, its evolution process may be influenced by the size of precursor particles during the pyrolysis preparation process. In this work, semi-coke has been used as the precursor, and its particle size (median particle sizes of 3, 7, 11, 15, and 19 mu m) is investigated for its impact on the microstructure and Na storage performance of the resulting carbon materials (SDC-X, X = 3, 7, 11, 15, or 19). As the size of semi-coke increases from 3 mu m to 19 mu m, the highly-disordered carbon content of SDC-X decreases from 41.27 % to 30.94 %, while the content of pseudo-graphitic carbon associated with plateau capacity remains nearly constant. When SDC-X are employed as anodes for sodium-ion batteries, the initial coulombic efficiency (ICE) rose from 77.4 % to 82.3 % with the increase of size, primarily due to the increase in the ICE of slope region. However, despite SDC-19 having the highest ICE, its reversible capacity, rate, and cycle performance are inferior compared with the others due to its higher order degree and larger particle size. Therefore, carbon-based materials used as anodes for SIBs require a trade-off between particle size and the electrochemical performance.
More
Translated text
Key words
Carbon-based materials,Microstructure,Semi-coke,Particle size,Na storage performance
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
Related Papers
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

要点】:本文研究了半焦颗粒大小对所得碳材料的微结构和钠存储性能的影响,发现颗粒大小会影响材料的电化学性能。

方法】:通过改变半焦的粒度(3、7、11、15和19微米),研究了不同粒度对微结构和钠存储性能的影响。

实验】:使用不同粒度的半焦作为前驱体制备碳材料(SDC-X,X=3、7、11、15或19),在钠离子电池中作为负极材料测试其性能,实验数据集为SDC-X的微结构和电化学性能数据,结果显示随着颗粒大小的增加,初始库仑效率提高,但可逆容量、倍率和循环性能有所下降。