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Record W4412595545 · doi:10.1038/s41467-025-62119-9

Nanoscale fletching of liquid-like polydimethylsiloxane with single perfluorocarbons enables sustainable oil-repellency

2025· article· en· W4412595545 on OpenAlexafffund
Samuel Au, Jeremy R. Gauthier, Boran Kumral, Tobin Filleter, Scott A. Mabury, Kevin Golovin

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPolydimethylsiloxaneNanoscopic scaleMaterials scienceNanotechnologyOil spillEnvironmental scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Oil repellency is essential for enabling self-cleaning, anti-soiling and stain-repelling properties, which has broad application in industries liked textiles, healthcare and electronics. While per-and-polyfluoroalkyl substances (PFAS) exhibits strong oleophobicity, their environmental and health risks have led to prohibition on long-chain PFAS ( ≥ C8) and restriction on short-chain PFAS (C4, C6). However, there are few alternative materials demonstrating comparable oil repellency. Here, we introduce a novel method to fletch polydimethylsiloxane (PDMS) brushes with ultrashort PFAS (singe -CF3, the least toxic PFAS), achieving oil repellency similar to short-chain PFAS while drastically reducing the fluorine content. This work highlights that liquid-like molecular design, rather than chain length, can enable sustainable oil repellency, facilitating a smoother transition away from PFAS reliance. Oil repellency using polyfluoroalkyl substances are broadly applicable, though unfavorable due to high fluorine composition. Here the authors report a polydimethylsiloxane-based system using single CF3 groups affording oil repellency with low fluorine composition.

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)0.001
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 score0.077
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.265
Teacher spread0.252 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

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