Unexpected Solvent Effect Leading to Interface Segregation of Single-Chain Nanoparticles in All-Polymer Nanocomposite Films upon Solvent Evaporation

溶剂 聚合物 色散(光学) 单体 纳米复合材料 纳米颗粒 化学工程 溶剂效应 蒸发 材料科学 化学 高分子化学 纳米技术 有机化学 热力学 物理 光学 工程类
作者
Qian Zhao,You‐Liang Zhu,Zhong‐Yuan Lu,Hu‐Jun Qian
出处
期刊:Macromolecules [American Chemical Society]
卷期号:56 (5): 2175-2182 被引量:3
标识
DOI:10.1021/acs.macromol.2c02061
摘要

In athermal all-polymer nanocomposites (all-PNCs), single-chain nanoparticles (SCNPs) are often considered to be well miscible with polymer matrixes due to their similarity in chemical compositions. However, internal cross-linking units of SCNPs must have different chemistries from the backbone monomers and, therefore, also from matrix chains. Here, we use large-scale molecular dynamics simulations to study the influence of solvent selectivity, particularly to internal cross-linkers in SCNPs, on dispersion state of SCNPs in all-PNC films upon solvent evaporation. Surprisingly, we find distinct dispersion states of SCNPs in drying films with different solvent selectivities. When the solvent is both good for cross-linkers and backbone/matrix monomers, SCNPs can be uniformly dispersed. However, when the backbone/matrix monomers have better solvophilicity than the cross-linkers and the solvophilicity of the latter is weak enough, we find segregation of SCNPs in surface regions. Such phenomena can be attributed to the intrinsic difference in the solvent density at an interface region from that in the bulk, which eventually results in the aggregation of SCNPs at the interface region where the solvent particles are much less than in the bulk. At the interface region, cross-linkers in the SCNPs will have less contact with the solvent and, therefore, less enthalpy penalty than being located in the bulk region of the film. The results demonstrate that solvent selectivity has a non-negligible effect on the structure of the composite film, which will inevitably have impacts on macroscopic properties of the film.

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
知之发布了新的文献求助10
1秒前
1秒前
过冷风应助modoun采纳,获得10
2秒前
碧蓝柠檬发布了新的文献求助10
2秒前
3秒前
Akim应助椰肉采纳,获得10
3秒前
丘比特应助小赵采纳,获得10
3秒前
4秒前
Zz完成签到 ,获得积分10
4秒前
6秒前
6秒前
7秒前
8秒前
9秒前
lizhuang发布了新的文献求助10
10秒前
郭耀锐发布了新的文献求助10
10秒前
JamesPei应助知性的二娘采纳,获得10
12秒前
12秒前
grx发布了新的文献求助10
12秒前
13秒前
111发布了新的文献求助10
13秒前
algain发布了新的文献求助10
14秒前
善学以致用应助逝月采纳,获得10
14秒前
15秒前
15秒前
林夕相心发布了新的文献求助10
18秒前
小赵发布了新的文献求助10
19秒前
共享精神应助命苦科研人采纳,获得10
19秒前
20秒前
ding应助小武wwwww采纳,获得10
20秒前
learner1994发布了新的文献求助10
22秒前
22秒前
23秒前
RONG发布了新的文献求助10
25秒前
知性的二娘完成签到,获得积分10
25秒前
25秒前
Egg发布了新的文献求助10
26秒前
26秒前
WQ发布了新的文献求助10
27秒前
moonveil完成签到,获得积分10
27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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Instituting Science: The Cultural Production of Scientific Disciplines 666
Signals, Systems, and Signal Processing 610
The Organization of knowledge in modern America, 1860-1920 / 600
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6360322
求助须知:如何正确求助?哪些是违规求助? 8174527
关于积分的说明 17218068
捐赠科研通 5415387
什么是DOI,文献DOI怎么找? 2865877
邀请新用户注册赠送积分活动 1843138
关于科研通互助平台的介绍 1691313