Evidence map›Paper›PMID 35335433›Full record

ArticlePolymers2022

Chemical Cleaning Process of Polymeric Nanofibrous Membranes.

Aysegul Gul, Jakub Hruza, Lukas Dvorak, Fatma Yalcinkaya

Abstract 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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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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

4 authors.

Aysegul GulInstitute for Nanomaterials, Advanced Technology and Innovation, Technical University of Liberec, Studentska 1402/2, 46117 Liberec, Czech Republic.ORCID 0000-0003-1754-0479
Jakub HruzaInstitute for Nanomaterials, Advanced Technology and Innovation, Technical University of Liberec, Studentska 1402/2, 46117 Liberec, Czech Republic.
Lukas DvorakInstitute for Nanomaterials, Advanced Technology and Innovation, Technical University of Liberec, Studentska 1402/2, 46117 Liberec, Czech Republic.
Fatma YalcinkayaInstitute for Nanomaterials, Advanced Technology and Innovation, Technical University of Liberec, Studentska 1402/2, 46117 Liberec, Czech Republic.ORCID 0000-0002-5057-3718

Funding

Ministry of Education, Youth and Sports of the Czech Republic CZ.02.1.01/0.0/0.0/16_019/0000843
6 · The paper itself

Abstract

Membrane fouling is one of the most significant issues to overcome in membrane-based technologies as it causes a decrease in the membrane flux and increases operational costs. This study investigates the effect of common chemical cleaning agents on polymeric nanofibrous membranes (PNM) prepared by polyvinylidene fluoride (PVDF), polyacrylonitrile (PAN), and polyamide 6 (PA6) nanofibers. Common alkaline and acid membrane cleaners were selected as the chemical cleaning agents. Membrane surface morphology was investigated. The PAN PNM were selected and fouled by engine oil and then cleaned by the different chemical cleaning agents at various ratios. The SEM results indicated that the use of chemical agents had some effects on the surface of the nanofibrous membranes. Moreover, alkaline cleaning of the fouled membrane using the Triton X 100 surfactant showed a two to five times higher flux recovery than without using a surfactant. Among the tested chemical agents, the highest flux recovery rate was obtained by a binary solution of 5% sodium hydroxide + Triton for alkaline cleaning, and an individual solution of 1% citric acid for acidic cleaning. The results presented here provide one of the first investigations into the chemical cleaning of nanofiber membranes.

Indexed as

chemical agentscleaningmembranemicrofiltrationnanofiberPAN

Identifiers

PMID35335433
PMCPMC8950600

What OpenQuestion holds

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LicenceCC BY
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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.