Evidence map›Paper›PMID 41591007›Full record

ArticleNanomaterials (Basel, Switzerland)2026

Preparation of Robust Superhydrophobic Surfaces Based on the Screen Printing Method.

Yinyu Sun, Qing Ding, Qiaoqiao Zhang, Yuting Xie, Zien Zhang, Yudie Pang, Zhongcheng Ke, Changjiang Li

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2026. 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

8 authors.

Yinyu SunSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.ORCID 0000-0002-1248-5549
Qing DingSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Qiaoqiao ZhangSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Yuting XieSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Zien ZhangSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Yudie PangSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Zhongcheng KeSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.
Changjiang LiSchool of Chemistry and Chemical Engineering, Huangshan University, Huangshan 245021, China.

Funding

Anhui Education Department Quality Engineering Project Grant No.2024jyxm0409Anhui Nature Science Research Program Grant No. 2022AH051954Huangshan University Science Research Program Grant No. 2022xkjzd001Youth Teacher Training Action Project of Anhui Province Grant No. DTR2023046
6 · The paper itself

Abstract

The bioinspired superhydrophobic surfaces have demonstrated many fascinating performances in fields such as self-cleaning, anti-corrosion, anti-icing, energy-harvesting devices, and antibacterial coatings. However, developing a low-cost, feasible, and scalable production approach to fabricate robust superhydrophobic surfaces has remained one of the main challenges in the past decades. In this paper, we propose an uncommon method for the fabrication of a durable superhydrophobic coating on the surface of the glass slide (GS). By utilizing the screen printing method and high-temperature curing, the epoxy resin grid (ERG) coating was uniformly and densely loaded on the surface of GS (ERG@GS). Subsequently, the hydrophobic silica (H-SiO

Indexed as

chemical stabilitymechanical durabilityscreen printingself-cleaningsuperhydrophobic surfaces

Identifiers

PMID41591007
PMCPMC12844342

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.