Evidence map›Paper›PMID 42226366›Full record

ArticleMacromolecular rapid communications2026

Reversibly Interlocked Macromolecular Networks Unlock a Stretchable, Self-Healing Polymer With Ultra-High White-Light Quantum Yield.

Wen Wen Deng, Fei Gao, Ze Ping Zhang, Min Zhi Rong

Abstract read
In one paragraph

Article in Macromolecular rapid communications, 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

4 authors.

Wen Wen DengSchool of Chemistry, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, GD HPPC Lab, Sun Yat-sen University, Guangzhou, China.
Fei GaoSchool of Chemistry, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, GD HPPC Lab, Sun Yat-sen University, Guangzhou, China.
Ze Ping ZhangSchool of Chemistry, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, GD HPPC Lab, Sun Yat-sen University, Guangzhou, China.
Min Zhi RongSchool of Chemistry, Key Laboratory for Polymeric Composite and Functional Materials of Ministry of Education, GD HPPC Lab, Sun Yat-sen University, Guangzhou, China.

Funding

National Natural Science Foundation of China 52373095Science and Technology Planning Project of Guangzhou City 2025A0412436
6 · The paper itself

Abstract

To suppress phase separation and improve fluorophore dispersion in physically blended white-light-emitting polymer materials, while also achieving stretchability and repairability, reversibly interlocked macromolecular networks (RILNs) are employed in this work. A stretchable, self-healable, and robust RILNs-based white-light material with adjustable multi-color fluorescence is synthesized from a Schiff base bond crosslinked single network containing red-light-emitting and green-light-emitting groups and a boronic ester bond crosslinked epoxy network carrying blue-light-emitting side chains. The interlocking network's phase separation suppression effect reduces aggregation-induced quenching of the incorporated luminophores, thereby improving white-light regulation convenience and achieving an ultra-high white-light quantum yield of 58.1% for polymer-based materials (photoluminescence luminance = 359 cd m

Indexed as

LightMacromolecular SubstancesPolymersMolecular StructureSchiff BasesMacromolecular SubstancesPolymersSchiff Basesflexibilitypolymer white‐light materialsrecyclabilityself‐healabilitystretchability

Identifiers

PMID42226366
PMCPMC13435073

What OpenQuestion holds

Textmetadata
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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.