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Record W4386500259 · doi:10.1039/d3sm00720k

Plant-based, aqueous, water-repellent sprays for coating textiles

2023· article· en· W4386500259 on OpenAlexafffund
Sara K. Fleetwood, Sydney Bell, Reinhard Jetter, E. Johan Foster

Bibliographic record

VenueSoft Matter · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsWaxPolyesterCoatingMaterials scienceAqueous solutionSolventWater repellentParaffin waxContact angleSuperhydrophobic coatingRecrystallization (geology)Chemical engineeringComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Novel superhydrophobic coatings, that are both biodegradable and biosourced, have the potential to revolutionize the water-repellent coating industry. Here, water-repellent coatings were prepared from commercially unavailable plant waxes, isolated using solvent extraction and characterized using DSC, GC-MS and DLS. In the first stage, a plant survey was conducted to identify an ideal plant source for the final spray, in which Whatman filter paper was submerged in a wax-solvent solution with recrystallization occurring upon air-drying. In the second stage, aqueous, PFC-free wax dispersions were prepared, coated onto textiles (cotton and polyester), and heat-treated with a home drying machine to allow for the spreading and recrystallization of the waxes. In both stages, SEM visualization verified the coating's morphology, and contact angle measurements showed them to be superhydrophobic. It was concluded that, using less coating material than commercial coatings, high-performing petroleum-free coatings could be made and applied onto textiles of various polarities.

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 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 score0.012
Threshold uncertainty score0.998

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)0.0030.015

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 head0.029
GPT teacher head0.257
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; both teacher heads agree on what is shown here.

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

Citations11
Published2023
Admission routes2
Has abstractyes

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