Glycine functionalized boron nitride nanosheets with improved dispersibility and enhanced interaction with matrix for thermal composites

材料科学 复合材料 氮化硼 基质(化学分析) 甘氨酸 热的 化学 有机化学 生物化学 物理 气象学 氨基酸
作者
Xiaojuan Tian,Ni Wu,Bing Zhang,Yifan Wang,Zishuo Geng,Yongfeng Li
出处
期刊:Chemical Engineering Journal [Elsevier]
卷期号:408: 127360-127360 被引量:101
标识
DOI:10.1016/j.cej.2020.127360
摘要

Hexagonal boron nitride nanosheets (BNNS) have been widely investigated as promising fillers for thermal composites because of their high thermal conductivity and electrical insulation, which avoid the short circuit risk effectively when applied in electronics. However, the poor dispersibility and weak interaction with matrix have hindered the further improvement of BNNS based thermal composites. Here, we propose a simple and green amino acid-assisted ball milling exfoliation process for highly hydrophilic glycine (NH2-CH2-COOH)-functionalized BNNS (BNNS-Gly) to improve their dispersion and reduce the thermal interface resistance between fillers and matrix. Three different types of thermal management materials have been prepared by dispersing BNNS-Gly into water, epoxy resin, and cellulose respectively, and their thermal properties have been investigated. As a result, the BNNS-Gly water nanofluid exhibits a 110% increment in thermal conductivity at 1.6 vol% loading compared with pure water. For BNNS-Gly/Epoxy composite, an enhancement of 109% of thermal conductivity compared with pure BNNS/Epoxy is achieved. According to the calculation of the Maxwell-Garnett effective medium theory (EMT) model, the thermal interface resistance between BNNS-Gly and epoxy is reduced by 62%. Besides, the thermal conductivity of BNNS-Gly/cellulose nanofiber (CNF) film reaches up to 16.2 W/mK at 70 wt% loading, which is 1.8-fold of that for BNNS/CNF. In summary, BNNS-Gly fabricated by this work show great advantages in thermal properties compared with widely-used pure BNNS based composites for improved dispersibility and enhanced interaction with matrix.
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
英姑应助梁书凡采纳,获得10
刚刚
StevenNG完成签到,获得积分10
刚刚
Zeal完成签到,获得积分10
刚刚
1秒前
Lucas应助ze采纳,获得10
1秒前
2秒前
科研通AI6.2应助wan采纳,获得10
2秒前
2秒前
hanqun1111完成签到,获得积分10
3秒前
3秒前
3秒前
乐乐应助lxaiczn采纳,获得10
3秒前
求求科研完成签到,获得积分10
4秒前
4秒前
4秒前
西红柿发布了新的文献求助10
4秒前
minss发布了新的文献求助10
4秒前
dawn发布了新的文献求助10
4秒前
YY完成签到,获得积分10
5秒前
孙一完成签到,获得积分10
5秒前
Monicadd完成签到 ,获得积分10
5秒前
yeyeyeyeyeyeyeye完成签到,获得积分10
5秒前
冷酷的芷容完成签到,获得积分10
5秒前
初夏的晴天完成签到,获得积分10
6秒前
StevenNG发布了新的文献求助10
6秒前
Wdw2236发布了新的文献求助10
7秒前
7秒前
李健应助美好的老黑采纳,获得10
8秒前
8秒前
充电宝应助谦让的发箍采纳,获得10
8秒前
9秒前
无限白枫完成签到,获得积分10
9秒前
9秒前
香蕉觅云应助小混蛋采纳,获得10
9秒前
克诺尔普完成签到,获得积分10
9秒前
小北发布了新的文献求助10
9秒前
9秒前
10秒前
李哲完成签到,获得积分10
10秒前
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6016722
求助须知:如何正确求助?哪些是违规求助? 7599299
关于积分的说明 16153405
捐赠科研通 5164494
什么是DOI,文献DOI怎么找? 2764681
邀请新用户注册赠送积分活动 1745695
关于科研通互助平台的介绍 1634980