孙相彧

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工程师

性别:男

所在单位:化工学院

电子邮箱:sunxy@dlut.edu.cn

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Growth modes of condensates on nano-textured surfaces and mechanism of partially wetted droplet formation

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论文类型:期刊论文

发表时间:2013-11-07

发表刊物:SOFT MATTER

收录刊物:SCIE、EI、Scopus

卷号:9

期号:41

页面范围:9807-9815

ISSN号:1744-683X

摘要:Condensed droplets on different nano-textured surfaces may appear in three distinct wetting states, the Cassie-Baxter state with composite wetting, Wenzel state with complete wetting, and the partially wetted (PW) state. To maintain the super-hydrophobicity of a textured surface, condensed drops on it are usually expected to be in a Cassie-Baxter or PW state. Therefore, it is of importance to clarify the relation between condensed droplet wetting states and the nano-pillar geometries of surfaces. In view of the fact that all condensed droplets in diverse wetting states originate from the nuclei and/or condensate spots growing along different pathways, we think that the distinct growth modes of a condensate correspond to different energy increasing rates (EIRs), and a condensed drop should grow along the route with the minimum EIR. In this paper, accordingly, the EIRs of a droplet on different textured surfaces were analyzed during its growth along three pathways. The results show that the smallest initial EIR of a condensate spot occurs in the increasing contact angel (CA) mode, so that it will grow with the CA enlarging and the base area initially remaining unchanged. Then the EIR of the increasing CA mode becomes much higher than that of the other two modes. The base area of the drop begins to enlarge while the CA remains unchanged. During this period, the increasing base area can be either in a wetted or composite state, resulting in a Wenzel or PW droplet forming, respectively. The growth mode and the wetting state of a condensed droplet are strongly related to the nanostructure of the surface. Additionally, the calculation results of this model are consistent with experimental observations in the literature for the wetting states of condensed drops on nano-textured surfaces, with an accuracy of 91.9%, which is higher than the accuracy of results calculated with previously reported formulas.