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Record W2316155371 · doi:10.1021/la300634v

Diblock-Copolymer-Coated Water- and Oil-Repellent Cotton Fabrics

2012· article· en· W2316155371 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de la Défense NationaleCanada Research Chairs
KeywordsCopolymerWater repellentPolymer scienceChemical engineeringMaterials sciencePolymer chemistryChemistryPolymerComposite materialEngineering

Abstract

fetched live from OpenAlex

A diblock copolymer consisting of a sol-gel-forming block and a fluorinated block was used to coat cotton fabrics, yielding textiles that were highly oil- and water-repellent. The coating procedure was simple. At grafted polymer amounts of as low as 1.0 wt %, water, diodomethane, hexadecane, cooking oil, and pump oil all had contact angles surpassing 150° on the coated cotton fabrics and were readily rolled. The liquids were not drawn into the interfiber space by the coated fabrics. Rather, droplets of the nonvolatile liquids such as cooking oil retained their beaded shapes for months with minimal contact angle changes. When forced into water, the coated fabrics trapped an air or plastron layer and this plastron layer was stable for months. In addition, the coating had high stability against simulated washing, and the mechanical properties were essentially identical to those of uncoated cotton fabrics.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.033
Threshold uncertainty score1.000

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)0.0010.001

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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