MétaCan
Menu
Back to cohort
Record W2327410521 · doi:10.1021/am402286x

Superhydrophobic Lignocellulosic Wood Fiber/Mineral Networks

2013· article· en· W2327410521 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

VenueACS Applied Materials & Interfaces · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFiberMineralComposite materialLigninNanotechnologyMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Lignocellulosic wood fibers and mineral fillers (calcium carbonate, talc, or clay) were used to prepare paper samples (handsheets), which were then subjected to a fluorocarbon plasma treatment. The plasma treatment was performed in two steps: first using oxygen plasma to create nanoscale roughness on the surface of the handsheet, and second fluorocarbon deposition plasma to add a layer of low surface energy material. The wetting behavior of the resulting fiber/mineral network (handsheet) was determined. It was found the samples that were subjected to oxygen plasma etching prior to fluorocarbon deposition exhibit superhydrophobicity with low contact angle hysteresis. On the other hand, those that were only treated by fluorocarbon plasma resulted in "sticky" hydrophobicity behavior. Moreover, as the mineral content in the handsheet increases, the hydrophobicity after plasma treatment decreases. Finally, it was found that although the plasma-treated handsheets show excellent water repellency they are not good water vapor barriers.

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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0370.019

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 head0.213
Teacher spread0.201 · 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