Evidence map›Paper›PMID 32443617›Full record

ArticleBiomimetics (Basel, Switzerland)2020

Composite Magnetic Sorbents Based on Iron Oxides in Different Polymer Matrices: Comparison and Application for Removal of Strontium.

Andrei Egorin, Eduard Tokar, Anna Matskevich, Nikita Ivanov, Ivan Tkachenko, Tatiana Sokolnitskaya, Larisa Zemskova

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2020. 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
–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

1 citing paper in PubMed.

  1. 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

7 authors.

Andrei EgorinInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Eduard TokarInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Anna MatskevichInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Nikita IvanovInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Ivan TkachenkoInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Tatiana SokolnitskayaInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.
Larisa ZemskovaInstitute of Chemistry, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia.

Funding

Ministry of Science and Higher Education of the Russian Federation 0265-2019-0002
6 · The paper itself

Abstract

Introduction of magnetic nanoparticles into composite sorbents based on polymer matrices has received great attention due to the possibility of using cheap iron oxides and removing spent sorbents by means of magnetic separation. In the present paper, we discuss the problem of creating magnetic sorbents using two types of matrices as host materials: synthetic cation exchange resin and natural aminopolysaccharide chitosan. The possibilities of applying matrices for the in situ formation of oxide phases of a specified composition with the required content of an inorganic component in a composite material were estimated. The composition of the oxide phase formed in the composite material was studied, and particle sizes were evaluated by the method of X-ray diffraction analysis. Magnetic characteristics were investigated. Sorption characteristics with respect to strontium for the composites containing iron oxides were determined.

Indexed as

adsorptioniron oxidesmagnetic chitosan compositespolymeric sorbents and biosorbentsstrontium

Identifiers

PMID32443617
PMCPMC7345435

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.