Evidence map›Paper›PMID 36354610›Full record

ArticleGels (Basel, Switzerland)2022

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis.

Nimer Murshid, Omar Mouhtady, Mahmoud Abu-Samha, Emil Obeid, Yahya Kharboutly, Hamdi Chaouk, Jalal Halwani, Khaled Younes

Open access · goldAbstract read
In one paragraph

Article in Gels (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

8 authors at 2 institutions in 2 countries.

Nimer MurshidCollege of Engineering and Technology, American University of the Middle East, Kuwait.ORCID 0000-0002-8329-239X
Omar MouhtadyCollege of Engineering and Technology, American University of the Middle East, Kuwait.ORCID 0000-0001-8502-2337
Mahmoud Abu-SamhaCollege of Engineering and Technology, American University of the Middle East, Kuwait.
Emil ObeidCollege of Engineering and Technology, American University of the Middle East, Kuwait.ORCID 0000-0002-0171-4668
Yahya KharboutlyCollege of Engineering and Technology, American University of the Middle East, Kuwait.
Hamdi ChaoukCollege of Engineering and Technology, American University of the Middle East, Kuwait.
Jalal HalwaniWater and Environment Sciences Lab, Lebanese University, Tripoli, Lebanon.ORCID 0000-0001-9049-2309
Khaled YounesCollege of Engineering and Technology, American University of the Middle East, Kuwait.ORCID 0000-0003-0966-2433
American University of the Middle East · KWLebanese University · LB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe

Indexed as

dye removalhydrogelhydrogel compositesmachine learningmetal oxide nanoparticlesprincipal component analysiswastewater

Identifiers

PMID36354610
PMCPMC9689451
OpenAlexW4307623945

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.