Evidence map›Paper›PMID 38389687›Full record

Article3D printing and additive manufacturing2024

Capacitance of Flexible Polymer/Graphene Microstructures with High Mechanical Strength.

Vahid Nasirian, Amir Ehsan Niaraki-Asli, Saurabh S Aykar, Mehrnoosh Taghavimehr, Reza Montazami, Nicole N Hashemi

Abstract read
In one paragraph

Article in 3D printing and additive manufacturing, 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

6 authors.

Vahid NasirianDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.
Amir Ehsan Niaraki-AsliDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.
Saurabh S AykarDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.
Mehrnoosh TaghavimehrDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.
Reza MontazamiDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.
Nicole N HashemiDepartment of Mechanical Engineering, Iowa State University, Ames, Iowa, USA.ORCID https://orcid.org/0000-0001-8921-7588

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Carbon-modified fibrous structures with high biocompatibility have attracted much attention due to their low cost, sustainability, abundance, and excellent electrical properties. However, some carbon-based materials possess low specific capacitance and electrochemical performance, which pose significant challenges in developing electronic microdevices. In this study, we report a microfluidic-based technique of manufacturing alginate hollow microfibers incorporated by water dispersed modified graphene (bovine serum albumin-graphene). These architectures successfully exhibited enhanced conductivity ∼20 times higher than alginate hollow microfibers without any significant change in the inner dimension of the hollow region (220.0 ± 10.0 μm) compared with pure alginate hollow microfibers. In the presence of graphene, higher specific surface permeability, active ion adsorption sites, and shorter pathways were created. These continuous ion transport networks resulted in improved electrochemical performance. The desired electrochemical properties of the microfibers make alginate/graphene hollow fibers an excellent choice for further use in the development of flexible capacitors with the potential to be used in smart health electronics.

Indexed as

alginate/graphenecapacitorconductivityion transportmicrofibers

Identifiers

PMID38389687
PMCPMC10880642

What OpenQuestion holds

Textmetadata
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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.