Evidence map›Paper›PMID 35160447›Full record

ArticlePolymers2022

Preparation and Characterization of Polymer Membranes Impregnated with Carbon Nanotubes for Olive Mill Wastewater.

Muna A Abu-Dalo, Maysa A Al-Atoom, Mohannad T Aljarrah, Borhan A Albiss

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
  2. Removal of phenolic compounds from olive mill wastewater (OMW) by tailoring the surface of activated carbon under acidic and basic conditions.Water science and technology : a journal of the International Association on Water Pollution Research · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

4 authors.

Muna A Abu-DaloDepartment of Chemistry, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan.ORCID 0000-0002-3800-4715
Maysa A Al-AtoomDepartment of Chemistry, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan.
Mohannad T AljarrahDepartment of Chemical Engineering, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan.
Borhan A AlbissNanomaterials Laboratory, Department of Physics, Faculty of Science and Arts, Jordan University of Science and Technology, Irbid 22110, Jordan.ORCID 0000-0001-9760-7594

Funding

Jordan University of Science and Technology 175/2019
6 · The paper itself

Abstract

In this study, polymer membrane(s) impregnated with carbon nanotubes (CNTs) were developed, characterized and evaluated for removing phenolic compounds from olive mill wastewater; thus, protecting the environment and public health. Polyethersulfone/functionalized, multi-walled carbon nanotube (PES/fCNTs) membranes were synthesized via the phase inversion method using PES and acid-treated CNTs. The prepared membranes were then characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR) and contact angle. Results obtained from this study indicate a more hydrophilic surface for the prepared PES/fCNTs membranes, with a higher pure water flux compared to the polyethersulfone (PES) membranes. In addition, the amount of fCNTs in the membranes was found to be the most significant factor affecting the morphology and water flux of the membranes. The PES/fCNTs membranes at 1 bar with 0 wt.% and 1 wt.% of CNTs showed water flux of 37.8 and 69.71 kg/h.m

Indexed as

carbon nanotube (CNTs)polymeric membranetotal phenolic compounds (TPC)

Identifiers

PMID35160447
PMCPMC8840152

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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.