Evidence map›Paper›PMID 37374801›Full record

ReviewMicromachines2023

Biomimetic Superhydrophobic Materials through 3D Printing: Progress and Challenges.

Haishuo Liu, Zipeng Zhang, Chenyu Wu, Kang Su, Xiaonan Kan

Abstract readReview
In one paragraph

Review in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
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

5 authors.

Haishuo LiuSchool of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Zipeng ZhangCollege of Polymer Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China.
Chenyu WuQingdao Institute for Theoretical and Computational Sciences, Institute of Frontier and Interdisciplinary Science, Shandong University, Qingdao 266237, China.
Kang SuSchool of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.ORCID 0000-0002-8235-4288
Xiaonan KanCollege of Polymer Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China.ORCID 0000-0002-4269-0869

Funding

National Natural Science Foundation of China 21802082Science Research Project of the Education Department of Hebei Province ZD2021093
6 · The paper itself

Abstract

Superhydrophobicity, a unique natural phenomenon observed in organisms such as lotus leaves and desert beetles, has inspired extensive research on biomimetic materials. Two main superhydrophobic effects have been identified: the "lotus leaf effect" and the "rose petal effect", both showing water contact angles larger than 150°, but with differing contact angle hysteresis values. In recent years, numerous strategies have been developed to fabricate superhydrophobic materials, among which 3D printing has garnered significant attention due to its rapid, low-cost, and precise construction of complex materials in a facile way. In this minireview, we provide a comprehensive overview of biomimetic superhydrophobic materials fabricated through 3D printing, focusing on wetting regimes, fabrication techniques, including printing of diverse micro/nanostructures, post-modification, and bulk material printing, and applications ranging from liquid manipulation and oil/water separation to drag reduction. Additionally, we discuss the challenges and future research directions in this burgeoning field.

Indexed as

3D printingapplicationsbiomimeticfabricationssuperhydrophobic

Identifiers

PMID37374801
PMCPMC10301086

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.