Evidence map›Paper›PMID 35745938›Full record

ArticlePolymers2022

Modeling of the Electrotransport Process in PP-Based and PLA-Based Composite Fibers Filled with Carbon Nanofibers.

Olga Moskalyuk, Diana Vol'nova, Ekaterina Tsobkallo

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 33% of its field
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

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. Review
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

3 authors at 2 institutions in 1 country.

Olga MoskalyukLaboratory of Polymer and Composite Materials-SmartTextiles, IRC-X-ray Coherent Optics, Immanuel Kant Baltic Federal University, 236029 Kaliningrad, Russia.ORCID 0000-0002-1057-5989
Diana Vol'novaDepartment of Engineering Materials Science and Metrology, St. Petersburg State University of Industrial Technology and Design, 191186 Saint-Petersburg, Russia.
Ekaterina TsobkalloDepartment of Engineering Materials Science and Metrology, St. Petersburg State University of Industrial Technology and Design, 191186 Saint-Petersburg, Russia.
Saint-Petersburg State University of Technology and Design · RUImmanuel Kant Baltic Federal University · RU

Funding

Russian Federal Academic Leadership Program Priority 2030 at the Immanuel Kant Baltic Federal University no
6 · The paper itself

Abstract

Polypropylene and polylactide-based composite fibers have been produced by a melt technology. Long vapor-grown carbon fibers (CNFs) have been used as electrical conductivity fillers. It is clearly shown by experimental methods that the CNFs are evenly distributed in the polymer matrix, orienting themselves along the direction of fiber extrusion and retaining their initial dimensions. It is shown that for composites fibers based on crystallizing (polypropylene) and amorphous (polylactide acid) polymer matrix, the dependence of electrical resistance on the filler concentration is percolation character and can be described as a double Boltzmann function. Four sections are identified on the dependences of the electrical resistance on the filler concentration for composite fibers, and the reasons for this character of this dependence on the formation of electrically conductive circuits are analyzed. Investigated in this work are the PP-based and PLA-based composites filled with carbon nanofibers that can be used as antistatic, shielding materials, or as sensors.

Indexed as

carbon nanofiberscrystallinityelectrotransportfibersmodelingpolymer matrix

Identifiers

PMID35745938
PMCPMC9229486
OpenAlexW4282575683

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.