Evidence map›Paper›PMID 42841012›Full record

ArticleRSC applied polymers2026

Up/down frunk: comparing recycling methods for non-woven poly(ethylene terephthalate) from an electric vehicle.

Claire L Seitzinger, Lynn Anderson, Fabian Mehner, Helena Simmons, Ciaran W Lahive, Michael P Shaver

Abstract read
In one paragraph

Article in RSC applied polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Claire L SeitzingerDepartment of Materials, The University of Manchester Manchester UK michael.shaver@manchester.ac.uk.ORCID https://orcid.org/0000-0002-4700-9964
Lynn AndersonDepartment of Materials, The University of Manchester Manchester UK michael.shaver@manchester.ac.uk.
Fabian MehnerDepartment of Materials, The University of Manchester Manchester UK michael.shaver@manchester.ac.uk.
Helena SimmonsPolestar Automotive UK Ltd Envisage Unit F Coventry UK.
Ciaran W LahiveDepartment of Materials, The University of Manchester Manchester UK michael.shaver@manchester.ac.uk.
Michael P ShaverDepartment of Materials, The University of Manchester Manchester UK michael.shaver@manchester.ac.uk.ORCID https://orcid.org/0000-0002-7152-6750

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When assessing optimal recycling routes for a specific plastic product, the object geometry and formulation complexity play a significant role. For example, poly(ethylene terephthalate), PET, depolymerization processes that work well on bottles may not apply well to other sectors. Here, we compare the technical feasibility of two recycling methods for a non-woven PET textile from an electric vehicle. We consider thermomechanical recycling through shredding and re-extruding to produce a new filament, as well as chemical recycling by glycolysis. The success of each technique and the scale up thereof is compared, along with the need for pretreatment. Thermomechanical recycling proved challenging, as the material did not feed consistently into the extruder, resulting in an inconsistent and degraded filament. Glycolysis was successful, and indeed faster than when utilizing bottle feedstocks, and could be readily scaled to 1 kg of material. Therefore, for a non-woven PET with a carbon black colorant, glycolysis performed better than mechanical processing, highlighting the need to consider object-level technical feasibility alongside sustainability when proposing recycling methods.

Identifiers

PMID42841012
PMCPMC13640944

What OpenQuestion holds

Textmetadata
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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.