Evidence map›Paper›PMID 42662388›Full record

ArticlePolymer science & technology (Washington, D.C.)2026

Upcycling of Post-Consumer Polycaprolactone to Polyamides and Poly(ester amide)s via Aminolysis-Dehydrogenative Polymerization.

Yiwei Rao, Shuo Wen, Fangxin Zhao, Shiyu Shen, Chenglong Yu, Yanqing Peng, Yichao Lin, Changguang Yao

Abstract read
In one paragraph

Article in Polymer science & technology (Washington, D.C.), 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

8 authors.

Yiwei RaoSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.
Shuo WenSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.
Fangxin ZhaoSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.
Shiyu ShenSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.
Chenglong YuJiangxi Province Zhengbai Technology Co., Ltd, Guixi 335499, China.
Yanqing PengSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.
Yichao LinState Key Laboratory of Coal Mine Disaster Prevention and Control, School of Safety and Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.ORCID https://orcid.org/0000-0003-4376-4505
Changguang YaoSchool of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China.ORCID https://orcid.org/0000-0002-9564-4225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Upcycling waste polyesters into valuable materials to address plastic pollution and reduce the waste of carbon resources is a necessary and highly challenging task. Herein, we present an "aminolysis-dehydrogenative polymerization" strategy that enables one-pot upcycling of polycaprolactone (PCL) into polyamides (PAs) and poly-(ester amide)-s (PEAs) in high yields with controllable ester-to-amide ratios and tunable functional moieties. Detailed mechanism analysis elucidates that the process involves four kinds of reactions: aminolysis, dehydrogenative cross-coupling of amine and alcohol, transesterification, and dehydrogenative self-coupling of alcohol. Significantly, the upcycling of post-consumer PCL into PAs and PEAs is also achieved, demonstrating a strong industrial application potential of this approach.

Indexed as

aminolysisdehydrogenative polymerizationpolyamidespolycaprolactonepoly(ester amide)supcycling

Identifiers

PMID42662388
PMCPMC13520749

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.