Evidence map›Paper›PMID 35736337›Full record

ArticleMembranes2022

The Design of Ternary Composite Polyurethane Membranes with an Enhanced Photocatalytic Degradation Potential for the Removal of Anionic Dyes.

Usman Zubair, Muhammad Zahid, Nimra Nadeem, Kainat Ghazal, Huda S AlSalem, Mona S Binkadem, Soha T Al-Goul, Zulfiqar Ahmad Rehan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Membranes, 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
0.7field-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, 9 citations in OpenAlex.

  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

8 authors at 5 institutions in 2 countries.

Usman ZubairDepartment of Textile Engineering, School of Engineering and Technology, National Textile University, Faisalabad 37610, Pakistan.
Muhammad ZahidDepartment of Chemistry, University of Agriculture Faisalabad, Faisalabad 38040, Pakistan.ORCID 0000-0001-8407-6886
Nimra NadeemDepartment of Textile Engineering, School of Engineering and Technology, National Textile University, Faisalabad 37610, Pakistan.ORCID 0000-0002-2362-8672
Kainat GhazalDepartment of Materials, School of Engineering and Technology, National Textile University, Faisalabad 37610, Pakistan.
Huda S AlSalemDepartment of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.ORCID 0000-0003-0110-7769
Mona S BinkademDepartment of Chemistry, College of Science, University of Jeddah, Jeddah 21589, Saudi Arabia.
Soha T Al-GoulDepartment of Chemistry, College of Sciences & Arts, King Abdulaziz University, Rabigh 25732, Saudi Arabia.
Zulfiqar Ahmad RehanDepartment of Materials, School of Engineering and Technology, National Textile University, Faisalabad 37610, Pakistan.
National Textile University · PKKing Abdulaziz University · SAPrincess Nourah bint Abdulrahman University · SAUniversity of Faisalabad · PKUniversity of Jeddah · SA

Funding

Princess Nourah bint Abdulrahman University PNURSP2022R185
6 · The paper itself

Abstract

Photocatalysis is an efficient and an eco-friendly way to eliminate organic pollutants from wastewater and filtration media. The major dilemma coupled with conventional membrane technology in wastewater remediation is fouling. In this study, the photocatalytic degradation potential of novel thermoplastic polyurethane (TPU) based NiO on aminated graphene oxide (NH

Indexed as

aminated graphene oxideantifouling membranesNiphotocatalysisternary compositewastewater treatment

Identifiers

PMID35736337
PMCPMC9230618
OpenAlexW4283018515

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.