Evidence map›Paper›PMID 30424462›Full record

ReviewMicromachines2018

Tunable Adhesion for Bio-Integrated Devices.

Zhaozheng Yu, Huanyu Cheng

Abstract readReview
In one paragraph

Review in Micromachines, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Linking energy loss in soft adhesion to surface roughness.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  5. Article
  6. 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

2 authors.

Zhaozheng YuDepartment of Engineering Science and Mechanics, The Pennsylvania State University, University Park, State College, PA 16802, USA. zqy5106@psu.edu.
Huanyu ChengDepartment of Engineering Science and Mechanics, The Pennsylvania State University, University Park, State College, PA 16802, USA. huanyu.cheng@psu.edu.ORCID 0000-0001-6075-4208

Funding

ASME Haythornthwaite Foundation Research Initiation Grant NADoctoral New Investigator grant from American Chemical Society Petroleum Research Fund NADorothy Quiggle Career Development Professorship in Engineering and Global Engineering Leadership Program at Penn State NAStart-up fund provided by the Engineering Science and Mechanics Department, College of Engineering, and Materials Research Institute at The Pennsylvania State University NA
6 · The paper itself

Abstract

With the rapid development of bio-integrated devices and tissue adhesives, tunable adhesion to soft biological tissues started gaining momentum. Strong adhesion is desirable when used to efficiently transfer vital signals or as wound dressing and tissue repair, whereas weak adhesion is needed for easy removal, and it is also the essential step for enabling repeatable use. Both the physical and chemical properties (e.g., moisture level, surface roughness, compliance, and surface chemistry) vary drastically from the skin to internal organ surfaces. Therefore, it is important to strategically design the adhesive for specific applications. Inspired largely by the remarkable adhesion properties found in several animal species, effective strategies such as structural design and novel material synthesis were explored to yield adhesives to match or even outperform their natural counterparts. In this mini-review, we provide a brief overview of the recent development of tunable adhesives, with a focus on their applications toward bio-integrated devices and tissue adhesives.

Indexed as

bio-integrated devicesdry/wet conditionssoft biological tissuetissue adhesivestunable adhesion

Identifiers

PMID30424462
PMCPMC6215118

What OpenQuestion holds

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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.