Superhydrophobic Polymer Surface with Hierarchical Patterns Fabricated in Hot Imprinting Process

Moon, In Yong; Kim, Bo Hyun; Lee, Ho Won; Oh, Youngseok; Kim, Ji Hoon; Kang, Seong-Hoon

doi:10.1007/s40684-019-00094-5

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…Different processes have been employed to produce hierarchical microstructures on hard molds, such as soft lithography, etching followed by coating, electroplating, or electrical discharge machining [ 3 , 16 , 17 , 18 , 19 , 20 ]. Hierarchical surface geometries in the range of 100 nm to 800 µm already have been reported [ 17 , 18 ]. While these methods allow high-precision processing, they become more expensive and complex upon treating larger areas, especially for cylindrical tools needed in roll-to-roll hot embossing systems.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Microtextures Embossed on PET from Laser-Patterned Stamps

et al. 2021

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Nowadays, the demand for surface functionalized plastics is constantly rising. To address this demand with an industry compatible solution, here a strategy is developed for producing hierarchical microstructures on polyethylene terephthalate (PET) by hot embossing using a stainless steel stamp. The master was structured using three laser-based processing steps. First, a nanosecond-Direct Laser Writing (DLW) system was used to pattern dimples with a depth of up to 8 µm. Next, the surface was smoothed by a remelting process with a high-speed laser scanning at low laser fluence. In the third step, Direct Laser Interference Patterning (DLIP) was utilized using four interfering sub-beams to texture a hole-like substructure with a spatial period of 3.1 µm and a depth up to 2 µm. The produced stamp was used to imprint PET foils under controlled temperature and pressure. Optical confocal microscopy and scanning electron microscopy imaging showed that the hierarchical textures could be accurately transferred to the polymer. Finally, the wettability of the single- and multi-scaled textured PET surfaces was characterized with a drop shape analyzer, revealing that the highest water contact angles were reached for the hierarchical patterns. Particularly, this angle was increased from 77° on the untreated PET up to 105° for a hierarchical structure processed with a DLW spot distance of 60 µm and with 10 pulses for the DLIP treatment.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Microtextures Embossed on PET from Laser-Patterned Stamps

et al. 2021

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“…36 Surface treatments oen refer to silane vapor deposition or liquid cast. [37][38][39][40][41] Surface roughness can be fabricated via colloidal lithography, 42 hot press, [43][44][45] mould, [46][47][48] and selective evaporation. 49 In addition, switchable hydrophilic and hydrophobic patterns have been designed for lotus-like self-cleaning capabilities.…”

Section: Introductionmentioning

confidence: 99%

Biobased superhydrophobic coating enabled by nanoparticle assembly

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Blisko

et al. 2021

Nanoscale Adv.

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“…The representative function of hierarchical patterns is the water-repellent surface, which means that the solid surface repels water. The hierarchical patterns are defined as patterns in which two or more kind of patterns coexist, and generally consist of a macro (or micro) sized primary pattern and a micro (or nano) sized secondary pattern [ 30 ]. Recently, we presented that when the hierarchical pattern is formed on the aluminum surface by imprinting using pattern mold produced by electric discharge machining (EDM), the hydrophobicity can be imparted to the aluminum surface [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

et al. 2021

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A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.

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Superhydrophobic Polymer Surface with Hierarchical Patterns Fabricated in Hot Imprinting Process

Cited by 26 publications

References 21 publications

Hierarchical Microtextures Embossed on PET from Laser-Patterned Stamps

Hierarchical Microtextures Embossed on PET from Laser-Patterned Stamps

Biobased superhydrophobic coating enabled by nanoparticle assembly

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

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