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Record W2517998284 · doi:10.1038/srep31574

Photo Initiated Chemical Vapour Deposition To Increase Polymer Hydrophobicity

2016· article· en· W2517998284 on OpenAlexafffund
Ariane Bérard, Gregory S. Patience, Gérald Chouinard, Jason R. Tavares

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsInstitut de Recherche et de Développement en AgroenvironnementPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesAgriculture and Agri-Food CanadaUniversitaire StichtingPolytechnique Montréal
KeywordsContact angleHigh-density polyethylenePolyethyleneMaterials sciencePolyethylene terephthalateDeposition (geology)PolymerTube (container)CoatingComposite material

Abstract

fetched live from OpenAlex

Apple growers face new challenges to produce organic apples and now many cover orchards with high-density polyethylene (HDPE) nets to exclude insects, rather than spraying insecticides. However, rainwater- associated wetness favours the development of apple scabs, Venturia inaequalis, whose lesions accumulate on the leaves and fruit causing unsightly spots. Treating the nets with a superhydrophobic coating should reduce the amount of water that passes through the net. Here we treat HDPE and polyethylene terephthalate using photo-initiated chemical vapour deposition (PICVD). We placed polymer samples in a quartz tube and passed a mixture of H2 and CO through it while a UVC lamp (254 nm) illuminated the surface. After the treatment, the contact angle between water droplets and the surface increased by an average of 20°. The contact angle of samples placed 70 cm from the entrance of the tube was higher than those at 45 cm and 20 cm. The PICVD-treated HDPE achieved a contact angle of 124°. Nets spray coated with a solvent-based commercial product achieved 180° but water ingress was, surprisingly, higher than that for nets with a lower contact angle.

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 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 score0.011
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)0.0010.000

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 head0.022
GPT teacher head0.210
Teacher spread0.189 · 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.

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

Citations30
Published2016
Admission routes2
Has abstractyes

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