疏水耐磨UV固化聚硅氧烷涂层的制备及性能
Preparation and Properties of Hydrophobic Wear-resistant UV-curable Polysiloxane Coating
袁国铭 1吴昆 2杨慧 1刘智军 1张月苗 1史珺 2杨立3
作者信息
- 1. 中国科学院广州化学研究所 中国科学院新型特种精细化学品工程实验室,广东 广州 510650;中国科学院大学,北京 100049
- 2. 中国科学院广州化学研究所 中国科学院新型特种精细化学品工程实验室,广东 广州 510650;国科广化韶关新材料研究院 国科广化(南雄)新材料研究院有限公司,广东 南雄 512400
- 3. 南雄市科学技术开发服务中心,广东 韶关 512000
- 折叠
摘要
以含氢硅油、1,2-环氧-5-己烯和烯丙基苯基醚为原料,通过硅氢加成,采用一锅法合成了可UV固化的前驱体并固化得到了一系列无氟聚硅氧烷涂层.通过静态水接触角、耐磨擦、透过率、附着力及硬度的测试,考察了交联密度和芳香基团含量对UV固化聚硅氧烷涂层的性能影响.结果表明,提高交联密度有利于涂层的附着力,然而过高的交联密度会影响其透明性.芳香基团由于极性较低,可以提高涂层的疏水性,但其含量过高也会导致涂层附着力下降.当环氧基团和苯环数量为 1∶1 时,即涂层PSO-3,附着力水平为1级,在 800 nm处透过率为 86.71%,静态水接触角可达 104°.同时,由于刚性的芳香基团的引入,涂层PSO-3 还具有良好的耐磨性能,经 20 次摩擦循环后水接触角仍达 98°.
Abstract
UV-curable precursors were synthesized by a one-pot method using hydrogen-containing silicone oil,1,2-epoxy-5-hexene and arylphenyl ether as raw materials via silica-hydrogen addition reaction.And a series of fluorine-free polysiloxane coatings were obtained by UV curing.The effects of crosslink density and aromatic group content on the properties of UV-cured polysiloxane coatings were investigated by testing static water contact angle,abrasion resistance,transmittance,adhesion and hardness.The results show that the increase of crosslinking density was favourable to the adhesion of the coating,however,too high crosslinking density affects its transparency.Aromatic groups can improve the hydrophobicity of the coating due to their low polarity,but excessive levels can lead to reduced coating adhesion.When the number of epoxy groups and benzene rings is 1:1,namely coating PSO-3,the adhesion level is class 1,the transmittance at 800 nm is 86.71%,and the static water contact angle is up to 104°.At the same time,due to the introduction of rigid aromatic groups,the coating PSO-3 also has good wear resistance,with a water contact angle of 98° even after 20 friction cycles.
关键词
聚硅氧烷涂层/疏水/耐磨/附着力/UV固化Key words
polysiloxane coating/hydrophobicity/abrasion resistance/adhesion/UV curing引用本文复制引用
基金项目
广东省科技专项韶关科技计划(210906134536798)
出版年
2024
国家科技期刊平台
NSTL
万方数据