Evidence map›Paper›PMID 39685417›Full record

ArticleMaterials (Basel, Switzerland)2024

Superhydrophobic Surfaces as a Potential Skin Coating to Prevent Jellyfish Stings: Inhibition and Anti-Tentacle Adhesion in Nematocysts of Jellyfish

Yichen Xie, Yuanyuan Sun, Rongfeng Li, Song Liu, Ronge Xing, Pengcheng Li, Huahua Yu

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. 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.

Yichen XieCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Yuanyuan SunCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Rongfeng LiCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Song LiuCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Ronge XingCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Pengcheng LiCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
Huahua YuCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.ORCID 0000-0003-3213-6212

Funding

National Natural Science Foundation of China 41776163, 41876164Natural Science Foundation of Shandong Province ZR2019QD012the National Key R&D Program of China 2019YFC0312605
6 · The paper itself

Abstract

The development of skin-protective materials that prevent the adhesion of cnidarian nematocysts and enhance the mechanical strength of these materials is crucial for addressing the issue of jellyfish stings. This study aimed to construct superhydrophobic nanomaterials capable of creating a surface that inhibits nematocyst adhesion, therefore preventing jellyfish stings. We investigated wettability and nematocyst adhesion on four different surfaces: gelatin, polydimethylsiloxane (PDMS), dodecyl trichlorosilane (DTS)-modified SiO

Indexed as

bioadhesionjellyfish stingsnanocellulosenanochitinnano materialsnematocystsnemopilema nomuraisuperhydrophobic surface

Identifiers

PMID39685417
PMCPMC11643888

What OpenQuestion holds

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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.