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Record W2966417471 · doi:10.1080/02670844.2019.1647374

Aluminium films roughened by hot water treatment and derivatized by fluoroalkyl phosphonic acid: wettability studies

2019· article· en· W2966417471 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.

Bibliographic record

VenueSurface Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsWestern University
Fundersnot available
KeywordsWettingContact angleMaterials scienceDerivatizationMonolayerAluminiumChemical engineeringSurface roughnessThermal stabilitySurface finishPorosityOrganic chemistryNanotechnologyComposite materialChemistry

Abstract

fetched live from OpenAlex

Aluminium (Al) films roughened by hot water treatment were used to investigate the impact of their roughness on hydrophobicity upon their derivatization via self-assembled monolayers (SAMs) of a fluoroalkyl phosphonic acid (FPA). Superhydrophobicity was achieved for FPA-derivatized Al films that had been treated in hot water briefly (e.g. 60–180 s), while for prolonged treatment times (e.g. beyond 240 s) degraded hydrophobicity was observed. The observed degradation of hydrophobicity is attributed to surface morphology changes causing water to enter the pores of the roughened Al films. Also studied is the surface chemistry of the FPA SAMs derivatized on Al films, including their thermal stability and a possible mechanism that improves the hydrophobicity. Our results demonstrate that an Al surface immersed in hot water for a couple of minutes generates a porous morphology suitable for rendering superhydrophobicity upon derivatization of the robust FPA SAMs.

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 categoriesMeta-epidemiology (narrow)
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 score0.007
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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