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Record W2905573364 · doi:10.3390/nano9010002

Thermally Robust Non-Wetting Ni-PTFE Electrodeposited Nanocomposite

2018· article· en· W2905573364 on OpenAlex
Jason Tam, Jonathan Chun Fung Lau, U. Erb

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

VenueNanomaterials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWettingPolytetrafluoroethyleneComposite materialContact angleAbrasiveCoatingNanocompositeSilicon carbideAbrasion (mechanical)NickelMetallurgy

Abstract

fetched live from OpenAlex

The effect of high temperature exposure on the water wetting properties of co-electrodeposited superhydrophobic nickel-polytetrafluoroethylene (Ni-PTFE) nanocomposite coating on copper substrates was studied. This was accomplished by comparing the performance with a commercial superhydrophobic spray treatment (CSHST). The Ni-PTFE and CSHST coatings were both subjected to heating at temperatures up to 400 °C. Results showed that the Ni-PTFE was able to maintain its superhydrophobicity throughout the entire temperature range, whereas the CSHST became more wettable at 300 °C. Furthermore, additional abrasive wear tests were conducted on both materials that were subjected to heating at 400 °C. The Ni-PTFE remained highly non-wettable even after 60 m of abrasion length on 800 grit silicon carbide paper, whereas the CSHST coating was hydrophilic after 15 m.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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