Evidence map›Paper›PMID 37233538›Full record

ReviewMembranes2023

Thin-Film Nanocomposite (TFN) Membranes for Water Treatment Applications: Characterization and Performance.

Amr Tayel, Ahmed B Abdelaal, Amal M K Esawi, Adham R Ramadan

Open access · goldAbstract readReview
In one paragraph

Review in Membranes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.3field-weighted citation impact, top 8% 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

4 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. 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

4 authors at 2 institutions in 2 countries.

Amr TayelDepartment of Chemistry, The American University in Cairo, AUC Avenue, New Cairo 11835, Egypt.ORCID 0000-0002-6842-9984
Ahmed B AbdelaalDepartment of Chemistry, McGill University, 845 Rue Sherbrooke O, Montreal, QC H3A 0G4, Canada.
Amal M K EsawiDepartment of Mechanical Engineering, The American University in Cairo, AUC Avenue, New Cairo 11835, Egypt.
Adham R RamadanDepartment of Chemistry, The American University in Cairo, AUC Avenue, New Cairo 11835, Egypt.
American University in Cairo · EGMcGill University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thin-film nanocomposite (TFN) membranes have been widely investigated for water treatment applications due to their promising performance in terms of flux, salt rejection, and their antifouling properties. This review article provides an overview of the TFN membrane characterization and performance. It presents different characterization techniques that have been used to analyze these membranes and the nanofillers within them. The techniques comprise structural and elemental analysis, surface and morphology analysis, compositional analysis, and mechanical properties. Additionally, the fundamentals of membrane preparation are also presented, together with a classification of nanofillers that have been used so far. The potential of TFN membranes to address water scarcity and pollution challenges is significant. This review also lists examples of effective TFN membrane applications for water treatment. These include enhanced flux, enhanced salt rejection, antifouling, chlorine resistance, antimicrobial properties, thermal stability, and dye removal. The article concludes with a synopsis of the current status of TFN membranes and future perspectives.

Indexed as

membrane characterizationmembrane performancenanofillersthin-film nanocomposite membraneswater treatment

Identifiers

PMID37233538
PMCPMC10224012
OpenAlexW4367310608

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.