Mechanism of Graphene Formation via Detonation Synthesis: A DFTB Nanoreactor Approach

纳米反应器 石墨烯 乙炔 起爆 分子 分子动力学 碳纤维 化学物理 氧化物 材料科学 聚合 化学 纳米技术 计算化学 有机化学 纳米颗粒 爆炸物 复合数 复合材料 聚合物
作者
Tingyu Lei,Wenping Guo,Qingya Liu,Haijun Jiao,Dong‐Bo Cao,Botao Teng,Yongwang Li,Xingchen Liu,Xiaodong Wen
出处
期刊:Journal of Chemical Theory and Computation [American Chemical Society]
卷期号:15 (6): 3654-3665 被引量:34
标识
DOI:10.1021/acs.jctc.9b00158
摘要

With the development of theoretical and computational chemistry, as well as high-performance computing, molecular simulation can now be used not only as a tool to explain the experimental results but also as a means for discovery or prediction. Quantum chemical nanoreactor is such a method which can automatically explore the chemical process based only on the basic mechanics without prior knowledge of the reactions. Here, we present a new method which combines the semiempirical quantum mechanical density functional tight-binding (DFTB) method with the nanoreactor molecular dynamic (NMD) method, and we simulated the reaction process of graphene synthesis via detonation at different oxygen/acetylene mole ratios. The formation of graphene is initiated by the breaking of acetylene (C2H2) molecules by collision into pieces such as H atoms, ethynyl (HC≡C•), and vinylidene (H2C═C:) radicals. It is followed by the formation of long straight carbon chains coupled with a few branched carbon chains, which then turned into a 2-D framework made of carbon rings. Trace oxygen could modulate the size of the rings during graphene formation and promote the formation of regular graphene with fused six-membered rings as we see, but the addition of high oxygen content makes more C-containing species oxidized to small oxide molecules instead of polymerization. The calculation speed of the DFTB nanoreactor is greatly improved compared to the ab initio nanoreactor, which makes it a valuable option to simulate chemical processes of large sizes and long time scales and to help us uncover the "unknown unknowns".
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
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
脑洞疼应助圣晟胜采纳,获得10
刚刚
霓娜酱发布了新的文献求助10
刚刚
2秒前
852应助xiaoxiao采纳,获得10
4秒前
lingjuanwu完成签到,获得积分10
4秒前
janice发布了新的文献求助10
5秒前
5秒前
快乐慕灵完成签到,获得积分10
7秒前
7秒前
JianYugen完成签到,获得积分10
7秒前
happy发布了新的文献求助10
8秒前
8秒前
9秒前
abe发布了新的文献求助10
10秒前
天天开心完成签到 ,获得积分10
10秒前
11秒前
12秒前
13秒前
所所应助clean采纳,获得10
14秒前
sad完成签到,获得积分10
15秒前
学术地瓜发布了新的文献求助10
15秒前
16秒前
17秒前
爱静静应助跳跃的访烟采纳,获得10
17秒前
在水一方应助圣晟胜采纳,获得10
18秒前
19秒前
19秒前
19秒前
segama完成签到 ,获得积分10
19秒前
在人中完成签到,获得积分10
19秒前
顾矜应助tangyuyi采纳,获得10
19秒前
我是老大应助满意冷荷采纳,获得10
22秒前
凝子老师发布了新的文献求助10
22秒前
Qinpy发布了新的文献求助20
23秒前
跳跃的访烟完成签到,获得积分10
23秒前
bkagyin应助janice采纳,获得10
24秒前
24秒前
clean发布了新的文献求助10
24秒前
会飞的木头应助Anquan采纳,获得10
26秒前
炫哥IRIS完成签到,获得积分10
26秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3528020
求助须知:如何正确求助?哪些是违规求助? 3108260
关于积分的说明 9288139
捐赠科研通 2805889
什么是DOI,文献DOI怎么找? 1540202
邀请新用户注册赠送积分活动 716950
科研通“疑难数据库(出版商)”最低求助积分说明 709849