Evidence map›Paper›PMID 39050490›Full record

ArticleEnergy & fuels : an American Chemical Society journal2024

Supercritical Water Liquefaction of Mixed Waste Polystyrene, Polypropylene, and Polyethylene for Production of High Yield Oils.

Maria Mathew, Mohamad A Nahil, Andrew B Ross, Paul T Williams

Abstract read
In one paragraph

Article in Energy & fuels : an American Chemical Society journal, 2024. 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. 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

4 authors.

Maria MathewSchool of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK.
Mohamad A NahilSchool of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK.
Andrew B RossSchool of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK.
Paul T WilliamsSchool of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK.ORCID https://orcid.org/0000-0003-0401-9326

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Supercritical water liquefaction of different plastic wastes has been investigated under high-temperature and high-pressure conditions. The supercritical water liquefaction of commonly used plastic types, comprising polystyrene (PS), polypropylene (PP), and low-density polyethylene (LDPE) as well as their mixtures, is reported. The experiments were carried out at varying feedstock-to-water ratios with a residence time of 60 min under supercritical water reaction conditions. The process produced high oil yields of over 97 wt %, with the highest yields obtained at a plastic:water ratio of 1:3; at higher levels of input water, the yield of oil decreased slightly. The gas phase mainly consisted of light hydrocarbons such as methane, ethane, propane, and butane, with propane found to be the most abundant gas component. Aromatic hydrocarbons and alicyclic hydrocarbons were the major products in the product oil from the supercritical water liquefaction of polystyrene and polypropylene, whereas alkanes were predominant in the oil obtained from LDPE. Analysis of the oil obtained from binary (1:1) and ternary (1:1:1) plastic mixtures showed it exhibited aromatic hydrocarbons as the major constituent, indicating synergistic interaction. It was found that the incorporation of PP in the mixture facilitated the production of cyclic compounds and suppressed the production of alkanes. Supercritical water liquefaction offers an effective solution to plastic pollution, producing valuable products without the need for catalysts.

Identifiers

PMID39050490
PMCPMC11264203

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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.