Evidence map›Paper›PMID 32225061›Full record

ArticleMolecules (Basel, Switzerland)2020

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.

Rusul Khaleel Ibrahim, Seef Saadi Fiyadh, Mohammed Abdulhakim AlSaadi, Lai Sai Hin, Nuruol Syuhadaa Mohd, Shaliza Ibrahim, Haitham Abdulmohsin Afan, Chow Ming Fai, Ali Najah Ahmed, Ahmed Elshafie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecules (Basel, Switzerland), 2020. 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
–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

1 citing paper in PubMed.

  1. 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

10 authors.

Rusul Khaleel IbrahimDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia.
Seef Saadi FiyadhNanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur 50603, Malaysia.
Mohammed Abdulhakim AlSaadiDepartment of Materials Science and Metallurgy, University of Nizwa, Birkat Al Mawz 616, Oman.ORCID 0000-0001-9278-6490
Lai Sai HinDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia.
Nuruol Syuhadaa MohdDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia.
Shaliza IbrahimInstitute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 50603, Malaysia.
Haitham Abdulmohsin AfanDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia.ORCID 0000-0002-4957-756X
Chow Ming FaiInstitute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia.
Ali Najah AhmedInstitute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia.ORCID 0000-0002-5618-6663
Ahmed ElshafieDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia.ORCID 0000-0001-5018-8505

Funding

Universiti Malaya UMRG-RP025C-18SUSUniversiti Tenaga Nasional RJO 10436494
6 · The paper itself

Abstract

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R

Indexed as

Neural Networks, ComputerAdsorptionAlgorithmsKineticsModels, TheoreticalNanotubes, CarbonPhenolsSolventsWater Pollutants, ChemicalWater PurificationNanotubes, CarbonPhenolsSolventsWater Pollutants, Chemicaladsorptioncarbon nanotubesdeep eutectic solventsfeedforward back propagation neural networkwater quality

Identifiers

PMID32225061
PMCPMC7180483

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.