Evidence map›Paper›PMID 37932301›Full record

ArticleScientific reports2023

Gamma irradiation-enhanced performance of waste LLDPE thermally transformed into advanced sponge-like material for oil decontamination.

H M Gayed, Mohamed Mohamady Ghobashy

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

H M GayedRadiation Research of Polymer Chemistry Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt. hanygayed83@gmail.com.
Mohamed Mohamady GhobashyRadiation Research of Polymer Chemistry Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority (EAEA), Cairo, Egypt. Mohamed_ghobashy@yahoo.com.

Funding

Science and Technology Development Fund 46339
6 · The paper itself

Abstract

In this study, the development of advanced materials for the removal of oil-water pollution was explored, with a focus on environmental protection. The primary novelty of this research involved the conversion of waste Linear low-density polyethylene (LLDPE) into a sponge-like material denoted as sLLDPE. The process of converting involved thermal treatment in castor oil, resulting in the creation of a porous structure within the material. This sLLDPE material exhibited remarkable oil adsorbent properties and demonstrated enhanced performance in the removal of various organic contaminants from both aqueous and oil-based systems. Furthermore, gamma irradiation-induced crosslinking reactions were implemented within a dose range of 0 up to 90 kGy to further improve its oil removal capabilities. Comparing samples subjected to a radiation dose of 50 kGy with those receiving no irradiation (0 kGy), it was observed that the maximum adsorption capacities for various oils, including crude oil, gasoline oil, motor oil, pump oil, and waste oil, increased significantly. Specifically, the adsorption capacities increased by approximately 216.2%, 235.3%, 24.1%, 111.5%, and 18.6% for the respective oils. It rapidly separated oil-water mixtures with ~ 100% efficiency in a column system and maintained performance over 20 reuse cycles. The converted sLLDPE sponge exhibited excellent organics removal across solvents. The findings of this study not only shed light on the impact of irradiation on polymeric materials but also contribute to our understanding of their potential applications in environmental cleanup processes.

Identifiers

PMID37932301
PMCPMC10628294

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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.