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Record W4391898667 · doi:10.1002/adfm.202314589

Simultaneous Segment Orientation and Anchoring for Robust Hydrogel Coating with Underwater Superoleophobicity

2024· article· en· W4391898667 on OpenAlexaff
Xusheng Jiang, Xiubin Xu, Zhaoji Xia, Lin Dian, Yanting Chen, Yaozhi Wang, Danfeng Yu, Xu Wu, Hongbo Zeng

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceCoatingAnchoringAdhesionUnderwaterAdhesiveNanotechnologyComposite materialLayer (electronics)Structural engineering

Abstract

fetched live from OpenAlex

Abstract Underwater adhesive superoleophobic coatings are in high demand for underwater activities. However, hydrogel coatings have limited adhesiveness for long periods. In this work, a novel approach is presented that employs synergistic segment orientation and covalent anchoring strategies to fabricate superoleophobic hydrogel coatings on different substrates without prior surface treatments. The coating demonstrated remarkable long‐lasting superoleophobic properties even in extreme underwater environments. It also exhibited strong adhesion with a shear strength of up to 5 MPa and antiswelling and antibiofouling properties, making it highly effective for applications in antiliquid adhesion, crude oil self‐cleaning, and antibiofouling. The simplicity, ease of preparation, and exceptional performance of the hydrogel coating provide a notable example for the synergistic segment orientation and covalent anchoring of hydrogel‐based underwater coatings. Therefore, this study provides a new paradigm for designing straightforward surface‐functional materials.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations49
Published2024
Admission routes1
Has abstractyes

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