Evidence map›Paper›PMID 42696610›Full record

ReviewChemSusChem2026

From Furfural to Reprocessable Thermosets: Diels-Alder Monomers for Dynamic Polyurethane Networks.

Konstantin I Galkin, Valentine P Ananikov

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. 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

2 authors.

Konstantin I GalkinZelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia.ORCID https://orcid.org/0000-0001-7958-1490
Valentine P AnanikovZelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia.ORCID https://orcid.org/0000-0002-6447-557X

Funding

the P.L. Kapitsa Grant Program (Phase III)the Russian Science Foundation 23-73-00003
6 · The paper itself

Abstract

Covalent adaptable networks (CANs) offer a promising route toward sustainable polymers by addressing the inherent conflict between thermoset performance and thermoplastic reprocessability through exchangeable covalent bonds. This review highlights a bio-based synthetic approach utilizing functionalized furan-maleimide Diels-Alder adducts, derived from furfural, as preformed monomers for polyurethane CANs. Unlike conventional postpolymerization crosslinking, this adduct-based strategy allows predictable incorporation of dynamic motifs into the backbone. This enables precise control over network topology and crosslink density while advancing sustainability through renewable feedstocks and recyclable network design. We demonstrate the advantages of the adduct-based approach and establish structure-property relationships that yield materials combining thermoset-like mechanical properties with stimuli-triggered self-healing, shape memory, and recyclability. Emerging applications in 3D/4D printing, UV-curable and waterborne coatings, and debondable adhesives are surveyed. Persistent challenges, including thermal and chemical instability of furanic and maleimide components, high activation barriers for network rearrangement, reliance on nonrenewable comonomers, and potential toxicity concerns, are also discussed. Future directions emphasize rationally designed multidynamic networks, robust dienophiles, and nonisocyanate routes to bridge academic innovation and industrial viability toward fully circular polymer systems.

Indexed as

3D/4D‐printingcovalent adaptable networksDiels–Alder reactionfurfuralpolyurethanes

Identifiers

PMID42696610
PMCPMC13544639

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.