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Record W4412361181 · doi:10.1002/advs.202507827

Ultra‐High Friction and Adhesion in Hydrogel Layer Driven by Wet‐to‐Dry Transition Dynamics

2025· article· en· W4412361181 on OpenAlexafffund
Chenxu Liu, Tianhui Sun, Wenqing Chen, Pan Huang, Lin Yang, Yuan Yao, Qiongyao Peng, Ying Hu, Yonggang Meng, Yu Tian, Hongbo Zeng

Bibliographic record

VenueAdvanced Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceAdhesionSelf-healing hydrogelsAdhesiveComposite materialPolymerShrinkageLayer (electronics)Phase transitionNanotechnologyGlass transitionPolymer chemistry

Abstract

fetched live from OpenAlex

Hydrogels are well-known for their antifriction and lubricating properties, particularly in hydrated environments, where their water-rich polymer networks enable effective friction reduction. However, during the wet-to-dry transition, a critical phase is identified during which the microstructures of a polyacrylamide hydrogel layer undergo volumetric shrinkage, leading to extensive interfacial contact and enhanced intermolecular interactions at solid interfaces. This process leads to a sharp increase in friction and adhesion forces. Sliding friction tests show that under a 2 mN load, the shear force peaked at 115 mN, corresponding to a remarkably high friction coefficient of 57.5. By leveraging this wet-to-dry transition, strong object gripping is successfully achieved across a range of surfaces. Notably, the hydrogel layer exhibits a high adhesion strength of 3.48 MPa on glass and 3.64 MPa on Si substrates. These findings offer new insights into hydrogel active gripping technologies and provide promising implications for soft robotics and adhesive interfaces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.232
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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