Evidence map›Paper›PMID 35457845›Full record

ArticleMicromachines2022

Liquid-Infused Porous Film Self-Assembly for Superior Light-Transmitting and Anti-Adhesion.

Yang Liu, Xiaoyang Zhan, Yan Wang, Guang Liu, Deyuan Zhang, Liwen Zhang, Huawei Chen

Abstract read
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yang LiuSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Xiaoyang ZhanSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Yan WangSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Guang LiuSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.ORCID 0000-0001-7756-1787
Deyuan ZhangSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Liwen ZhangSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Huawei ChenSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.ORCID 0000-0003-1766-421X

Funding

National Science Fund for Distinguished Young Scholars 51725501
6 · The paper itself

Abstract

Liquid-Infused Surfaces (LISs), particularly known for their liquid-repelling feature, have demonstrated plenty of applications in the medical, marine, and energy fields. To improve the durability and transparency highly demanded on glass-based vision devices such as an endoscope, this study proposed a novel self-assembly method to fabricate well-ordered porous Poly-Styrene (PS)/Styrene-Butadiene-Styrene (SBS) films by simply dripping the PS/SBS dichloromethane solutions onto the glass before spinning. The effects of the solutions' concentrations and spin speeds on the porous structure were experimentally investigated. The results showed that a certain mass ratio of PS/SBS can make the structure of the ordered porous film more regular and denser under the optimal solution concentration and spin-coating speed. Superior transparency and durability were also realized by dripping silicone oil on the porous film to build a liquid-infused surface. Applications of the as-prepared surface on devices like endoscopes, viewfinders, and goggles have been explored respectively.

Indexed as

anti-adhesionimproved durabilitylight-transmittingliquid-infused surfaceself-assemblywell-ordered porous film

Identifiers

PMID35457845
PMCPMC9025966

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.