Upgraded Structure and Application of Coal‐Based Graphitic Carbons Through Flash Joule Heating
Advanced Functional Materials(2024)SCI 1区
Abstract
Facilitating the transition and new application of fossil energy sources are crucial to attaining carbon neutrality. Conversion of coals into graphitic carbons represents an effective route to achieve their high‐value utilization, while this process always involves corrosive/toxic chemical reagents and time‐intensive heating treatment. Here, this work reports a green, rapid, and efficient flash Joule heating (FJH) technique to produce high‐quality carbons from diverse coals within 1 s. The surface groups, defects, and graphitization degree of the resultant carbon materials are controlled during the instantaneous thermal shock process, and the relationships between the coal structures and the product properties are established. The results suggest that the anthracite with high coalification degree tends to form highly graphitic carbons at a peak temperature of ≈3300 K, presenting higher rate capability (79.1% capacity retention at 30 A g–1) and low relaxation time constant (τ0 = 0.27 s) toward capacitive energy storage. Besides, the flash carbon materials derived from lignite and bituminous coal with low coal rank show better capacitive performance with capacity above 80 F g–1 at 1 A g–1. This study evidences that the FJH technology holds great potential to steer coals into valuable carbon materials.
MoreTranslated text
Key words
carbon materials,electrochemical performance,flash Joule heating,graphitization,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
Related Papers
Journal of Solid State Electrochemistry 2025
被引用0
Construction of NiCo2S4 Wrapped CeO2/Co3O4 Nanorod Arrays for Excellent Performance Supercapacitors
Journal of Solid State Electrochemistry 2024
被引用0
WSe2 Nanoflakes on Graphite Sheets for Flexible Symmetric Supercapacitors
ACS APPLIED NANO MATERIALS 2024
被引用1
ACS APPLIED NANO MATERIALS 2024
被引用0
Influence of MWCNTs on the Electrochemical Performance and Temperature Usage Range of Rgo Material
JOURNAL OF SOLID STATE ELECTROCHEMISTRY 2024
被引用0
CHEMICAL ENGINEERING JOURNAL 2025
被引用0
JOURNAL OF MATERIALS CHEMISTRY A 2025
被引用0
Journal of Solid State Electrochemistry 2025
被引用0
Journal of Solid State Electrochemistry 2025
被引用0
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