Evidence map›Paper›PMID 40125329›Full record

ArticleNational science review2025

High-entropy thermal-stiffening hydrogels with fast switching dynamics.

Li Li, Baohu Wu, Shengtong Sun, Peiyi Wu

Abstract read
In one paragraph

Article in National science review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Li LiState Key Laboratory of Advanced Fiber Materials, College of Chemistry and Chemical Engineering & Center for Advanced Low-Dimension Materials, Donghua University, Shanghai 201620, China.
Baohu WuJülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ) Forschungszentrum Jülich, Garching 85748, Germany.
Shengtong SunState Key Laboratory of Advanced Fiber Materials, College of Chemistry and Chemical Engineering & Center for Advanced Low-Dimension Materials, Donghua University, Shanghai 201620, China.ORCID https://orcid.org/0000-0001-7471-686X
Peiyi WuState Key Laboratory of Advanced Fiber Materials, College of Chemistry and Chemical Engineering & Center for Advanced Low-Dimension Materials, Donghua University, Shanghai 201620, China.ORCID https://orcid.org/0000-0001-7235-210X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thermal-stiffening hydrogels exhibit a dramatic soft-to-stiff transition upon heating, making them ideal candidates for temperature-triggered self-protection and shape memory applications. However, their practical use is still hampered by a slow recovery process (generally >30 min) during cooling, attributed to sluggish mass diffusion and delayed phase dissolution. Herein, we present a high-entropy phase separation design to significantly accelerate the recovery dynamics of these materials. We demonstrate this concept using a thermal-stiffening poly(calcium acrylate)-based copolymer hydrogel by incorporating hydrophilic units. Mechanistically, the hydrophilic units disrupt the dense packing of thermal-stiffening clusters, creating a high-entropy topological structure with a low energy barrier for rapid mass diffusion. This approach retains the impressive thermal-stiffening response with a 760-fold increase in storage modulus, while dramatically reducing the characteristic recovery time to merely 28 s. We anticipate this high-entropy strategy to be broadly applicable in designing modulus-adaptive materials with fast switching dynamics.

Indexed as

hydrogelsimpact-resistantmodulus controlphase separationresponsive materialstoughening

Identifiers

PMID40125329
PMCPMC11929134

What OpenQuestion holds

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LicenceCC BY
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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.