Evidence map›Paper›PMID 42627269›Full record

ReviewChemSusChem2026

Transition Metal-Catalyzed Chemical Recycling and Upcycling of Polyethylene Terephthalate.

Shuai Zhang, Xuerou Yu, Haobing Wang

Abstract readReview
In one paragraph

Review in ChemSusChem, 2026. 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

3 authors.

Shuai ZhangAdvanced Institute for Soft Matter Science and Technology (AISMST), School of Emergent Soft Matter, State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong, P. R. China.
Xuerou YuAdvanced Institute for Soft Matter Science and Technology (AISMST), School of Emergent Soft Matter, State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong, P. R. China.
Haobing WangAdvanced Institute for Soft Matter Science and Technology (AISMST), School of Emergent Soft Matter, State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong, P. R. China.ORCID https://orcid.org/0000-0002-9104-1726

Funding

Science and Technology Program of Guangzhou 2024D03J0003
6 · The paper itself

Abstract

The rapid growth of the polymer industry has driven the widespread application of polyester plastics, especially polyethylene terephthalate (PET). However, the nonbiodegradability of waste PET poses persistent threats to ecosystems. Conventional mechanical recycling struggles to achieve closed-loop material circulation and preserve intrinsic material properties. Therefore, developing efficient, low-energy chemical recycling technologies that depolymerize waste PET into monomers or high-value chemicals has become a critical route toward a circular plastic economy. This review systematically presents various transition metal-catalyzed chemical depolymerization processes for waste PET, with a focused analysis of reaction mechanisms and performance of different catalytic systems in key reactions such as glycolysis, hydrolysis, hydrogenolysis, and methanolysis. Furthermore, the review outlines future research directions for enabling high-value, closed-loop recycling of waste polyesters, including rational catalyst design and process optimization and integration. Overall, this review aims to provide theoretical guidance and technological insights for the sustainable chemical recycling of polyesters.

Indexed as

catalysispolyethylene terephthalate (PET)recyclingtransition metalupcycling

Identifiers

PMID42627269
PMCPMC13496109

What OpenQuestion holds

Textmetadata
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.