石墨烯
热重分析
材料科学
纳米技术
化学工程
工程类
作者
Farzaneh Farivar,Pei Lay Yap,Kamrul Hassan,Trần Thanh Tùng,Diana Tran,Andrew J. Pollard,Dušan Lošić
出处
期刊:Carbon
[Elsevier BV]
日期:2021-04-23
卷期号:179: 505-513
被引量:205
标识
DOI:10.1016/j.carbon.2021.04.064
摘要
The absence of rapid, reliable and cost-effective quality control for industrially manufactured graphene materials is an immediate problem for the emerging graphene industry. Recent studies have alarmingly revealed that a large percentage of manufactured graphene materials traded worldwide have a large variation of properties, and some of them are overpriced graphite powders. Currently, benchmark graphene characterization methods based on localized analysis can provide information of key properties of graphene such as the number of layers, particle size, and defects, only on individual graphene particles, which do not represent the properties of “bulk” material. To address these limitations, we developed and validated thermogravimetric analysis (TGA) as a simple analytical tool for characterization and quality control of manufactured few-layer graphene (FLG) and their non-graphene impurities in powder forms. Our results, using verified control and industrial samples, revealed that the derivative TGA graphs of FLG, graphene oxide and graphite powders have signatory distinctive peaks with temperature of maximum mass decomposition rates (T max ) in specific ranges, reflecting differences of their structural, chemical, and thermal properties, which are suitable for their qualitative and quantitative analysis. The method is applicable for graphene manufacturers and end-users for simple, low-cost and reliable quality control of graphene materials that will not fail to detect “fake” graphene. • A TGA method for quality control of graphene powders is introduced and validated. • Method allows quantitative and qualitative analysis of non-graphene components in graphene. • Number DTG peaks, their position and area are identified as key analytical parameters. • It is recommended as simple low-cost and reliable method to detect “fake graphene”.
科研通智能强力驱动
Strongly Powered by AbleSci AI