Growth of Double-Network Tough Hydrogel Coatings by Surface-Initiated Polymerization

材料科学 聚合 纳米技术 复合材料 化学工程 聚合物 工程类
作者
Yuhong Li,Junjie Liu,Qifang Zhang,Nan Hu,Zhouhu Jiang,Qianhua Kan,Guozheng Kang
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:16 (8): 10822-10831 被引量:7
标识
DOI:10.1021/acsami.4c00370
摘要

Hydrogel coatings exhibit versatile applications in biomedicine, flexible electronics, and environmental science. However, current coating methods encounter challenges in simultaneously achieving strong interfacial bonding, robust hydrogel coatings, and the ability to coat substrates with controlled thickness. This paper introduces a novel approach to grow a double-network (DN) tough hydrogel coating on various substrates. The process involves initial substrate modification using a silane coupling agent, followed by the deposition of an initiator layer on its surface. Subsequently, the substrate is immersed in a DN hydrogel precursor, where the coating grows under ultraviolet (UV) illumination. Precise control over the coating thickness is achieved by adjusting the UV illumination duration and the initiator quantity. The experimental measurement of adhesion reveals strong bonding between the DN hydrogel coating and diverse substrates, reaching up to 1012.9 J/m2 between the DN hydrogel coating and a glass substrate. The lubricity performance of the DN hydrogel coating is experimentally characterized, which is dependent on the coating thickness, applied pressure, and sliding velocity. The incorporation of 3D printing technology into the current coating method enables the creation of intricate hydrogel coating patterns on a flat substrate. Moreover, the hydrogel coating's versatility is demonstrated through its effective applications in oil–water separation and antifogging glasses, underscoring its wide-ranging potential. The robust DN hydrogel coating method presented here holds promise for advancing hydrogel applications across diverse fields.
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
葡萄糖完成签到,获得积分10
刚刚
哈哈完成签到,获得积分10
刚刚
在水一方应助CC采纳,获得10
刚刚
刚刚
余笙完成签到 ,获得积分10
1秒前
神勇的雅香应助科研混子采纳,获得10
1秒前
TT发布了新的文献求助10
2秒前
李顺完成签到,获得积分20
3秒前
ayin发布了新的文献求助10
3秒前
wait发布了新的文献求助10
3秒前
我是站长才怪应助xg采纳,获得10
4秒前
童话艺术佳完成签到,获得积分10
4秒前
稀罕你完成签到,获得积分10
4秒前
junzilan发布了新的文献求助10
4秒前
anny.white完成签到,获得积分10
5秒前
科研通AI5应助平常的毛豆采纳,获得10
7秒前
SciGPT应助paul采纳,获得10
10秒前
12秒前
英姑应助书生采纳,获得10
13秒前
科研钓鱼佬完成签到,获得积分10
14秒前
16秒前
petrichor应助C_Cppp采纳,获得10
16秒前
nan完成签到,获得积分10
16秒前
16秒前
17秒前
17秒前
勤恳的雨文完成签到,获得积分10
17秒前
木森ab发布了新的文献求助10
18秒前
paul完成签到,获得积分10
18秒前
小鞋完成签到,获得积分10
19秒前
开心青旋发布了新的文献求助10
19秒前
fztnh发布了新的文献求助10
19秒前
无名花生完成签到 ,获得积分10
19秒前
21秒前
22秒前
22秒前
杜若完成签到,获得积分10
22秒前
22秒前
木森ab完成签到,获得积分20
24秒前
paul发布了新的文献求助10
25秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
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
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 3527990
求助须知:如何正确求助?哪些是违规求助? 3108173
关于积分的说明 9287913
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540119
邀请新用户注册赠送积分活动 716941
科研通“疑难数据库(出版商)”最低求助积分说明 709824