A systematic study of interface properties and fracture behavior of graphene/aluminum: Insights from a first-principles study

材料科学 石墨烯 极限抗拉强度 断裂韧性 复合材料 断裂(地质) 断裂力学 压力(语言学) 纳米技术 语言学 哲学
作者
Jingtao Huang,Yong Liu,Zhonghong Lai,Jin Hu,Fei Zhou,Jingchuan Zhu
出处
期刊:Vacuum [Elsevier]
卷期号:204: 111346-111346 被引量:11
标识
DOI:10.1016/j.vacuum.2022.111346
摘要

In-depth study of graphene/aluminum interface properties with different structures is of great significance for improving the toughness and strength of next-generation aluminum alloys. In this paper, different structural models of Al matrix and graphene/Al composites were studied by first-principles calculations, revealing the interface strength and fracture behavior mechanism. The stability, fracture energy and tensile strength of different surfaces of Al(1 0 0), Al(1 1 0), and Al(1 1 1) were investigated by first-principles calculations, and the interfacial strength and fracture behavior of three common surfaces were revealed. The Al(1 1 1) surface has the lowest surface energy, lowest fracture energy and lowest tensile stress intensity. On this basis, we considered coating graphene on Al(1 1 1) surface, and then investigated the effects of C vacancies and Si doping on the fracture energy and tensile strength of the composites. The results show that with the increase of C vacancies and the increase of Si introduction concentration, the fracture energy and tensile stress strength of the system tend to increase. The density of states calculations show that there is a certain hybridization effect between Al-3s, Al-3p and C-2p, Si-3p orbitals, forming s-p hybrid orbitals. The most stable interfaces contain low-energy state electrons with the most uniform electron distribution, which is a key factor for interface stabilization and strengthening. Our theoretical research results have certain guiding significance for the experimental synthesis of graphene-reinforced aluminum matrix composites and the improvement of the strength of aluminum and aluminum alloys.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
爆米花应助耍酷依玉采纳,获得10
刚刚
2秒前
科研通AI2S应助外向的又柔采纳,获得10
2秒前
2秒前
3秒前
咕咕发布了新的文献求助10
4秒前
5秒前
Triumph发布了新的文献求助10
5秒前
5秒前
123完成签到,获得积分10
7秒前
8秒前
汤孤风完成签到,获得积分20
8秒前
8秒前
ewk发布了新的文献求助10
8秒前
9秒前
9秒前
小袁冲冲冲完成签到,获得积分10
9秒前
带头大哥应助tlight1740采纳,获得1000
10秒前
李爱国应助gyhuang采纳,获得10
10秒前
桐桐应助bbw采纳,获得10
12秒前
kiteWYL发布了新的文献求助10
13秒前
WIK完成签到,获得积分10
14秒前
16秒前
NI发布了新的文献求助10
16秒前
16秒前
山川完成签到,获得积分10
17秒前
18秒前
好事花生发布了新的文献求助20
18秒前
yyy发布了新的文献求助10
19秒前
20秒前
21秒前
印染发布了新的文献求助10
21秒前
可爱的函函应助香蕉以菱采纳,获得10
22秒前
23秒前
MLi发布了新的文献求助10
24秒前
胖川完成签到,获得积分10
24秒前
25秒前
生信人完成签到 ,获得积分10
25秒前
kiteWYL完成签到,获得积分10
25秒前
执着俊驰发布了新的文献求助10
26秒前
高分求助中
Shape Determination of Large Sedimental Rock Fragments 2000
Sustainability in Tides Chemistry 2000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
A Dissection Guide & Atlas to the Rabbit 600
Very-high-order BVD Schemes Using β-variable THINC Method 568
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3129330
求助须知:如何正确求助?哪些是违规求助? 2780114
关于积分的说明 7746436
捐赠科研通 2435295
什么是DOI,文献DOI怎么找? 1294036
科研通“疑难数据库(出版商)”最低求助积分说明 623516
版权声明 600542