4.8 Article

Molecular-Structure-Induced Under-Liquid Dual Superlyophobic Surfaces

期刊

ACS NANO
卷 14, 期 11, 页码 14869-14877

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.0c03977

关键词

reconfigurable molecular conformation; chain length; under-liquid dual superlyophobicity; switchable wettability; on-demand water/oil separation

资金

  1. National Natural Science Foundation [21871020]
  2. Beijing Young Talent Support Program
  3. 111 project
  4. Fundamental Research Funds for the Central Universities

向作者/读者索取更多资源

Surfaces with under-water superoleophobicity or under-oil superhydrophobicity have attractive features due to their widespread applications. However, it is difficult to achieve under-liquid dual superlyophobic surfaces, that is, under-oil superhydrophobicity and under-water superoleophobicity coexistence, due to the thermodynamic contradiction. Herein, we report an approach to obtain the under-liquid dual superlyophobic surface through conformational transitions of surface self-assembled molecules. Preferential exposure of either hydrophobic or hydrophilic moieties of the hydroxythiol (HS(CH2)(n)OH, where n is the number of methylene groups) self-assembled monolayers to the surrounding solvent (water or oil) can be used to manipulate macroscopic wettability. In water, the surfaces modified with different hydroxythiols exhibit under-water superoleophobicity because of the exposure of hydroxyl groups. In contrast, surface wettability to water is affected by molecular orientation in oil, and the surface transits from under-oil superhydrophilicity to superhydrophobicity when n >= 4. This surface design can amplify the molecular-level conformational transition to the change of macroscopic surface wettability. Furthermore, on-demand oil/water separation relying on the under-liquid dual superlyophobicity is successfully demonstrated. This work may be useful in developing the materials with opposite superwettability.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据