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Record W2127921110 · doi:10.1007/bfb0114445

Wetting measurements on smooth, rough and porous solid surfaces

2007· book-chapter· en· W2127921110 on OpenAlexaff
Karina Grundke, T. Bogumil, Thomas Gietzelt, H.‐J. Jacobasch, Daniel Y. Kwok, A. W. Neumann

Bibliographic record

VenueSteinkopff eBooks · 2007
Typebook-chapter
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsCanada Research ChairsUniversity of Toronto
Fundersnot available
KeywordsContact angleWettingMaterials scienceCapillary actionSessile drop techniquePorositySurface tensionPenetration (warfare)Solid surfaceSurface roughnessComposite materialInertSurface finishThermodynamicsChemistry

Abstract

fetched live from OpenAlex

The solid-vapour surface tension has been determined by contact angle measurements with polar and non-polar liquids on flat solid surfaces using Axisymmetric Drop Shape Analysis (ADSA) and by capillary penetration experiments on rough and porous solids. For smooth and inert, well prepared solid surfaces (PTFE, FC 721 on mica, FEP, PET) the plot of γ lv cos Θ versus γ lv yields smooth curves which are consistent with the equation of state approach to calculate solid-vapour and solid-liquid interfacial tensions. Other experimental patterns of contact angle data are caused by surface roughness and non-inert solids which may result in contact angles incompatible to Young’s equation. An alternative way to obtain the solidvapour surface tension of rough and porous solids are capillary penetration experiments. The determination of the penetration velocity of liquids into rough and porous solids yields Kγ lv coΘ versus γ lv plots, which provide γ sv values for these systems; K is an unknown parameter of the constant geometry of the porous solid. The application of this concept was demonstrated for a hydrophobic PTFE powder and for hydrophilic Cellulose membranes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.290
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations100
Published2007
Admission routes1
Has abstractyes

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