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Record W4309129510 · doi:10.48550/arxiv.2211.06554

Contact Angle Studies on Porous Silicon: Evidence for Heterogeneous Wetting and Implications of Oxidation

2022· preprint· en· W4309129510 on OpenAlexfundno aff
S. J. Spencer, C. G. Deacon, G. T. Andrews

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingMicroporous materialContact angleMaterials sciencePorosityOxideLayer (electronics)Surface roughnessSurface finishComposite materialPorous mediumSiliconPorous siliconChemical engineeringMetallurgy

Abstract

fetched live from OpenAlex

A study of wetting was carried out on porous silicon films with pore diameters spanning three orders of magnitude. Water contact angle measurements on adjoining porous and nonporous regions yielded Wenzel roughness ratios that were either unphysical (less than unity) or unrealistically low when compared to those expected from specific surface area considerations. Moreover, results obtained from a sample consisting of a microporous film on a macroporous layer gave contact angles that were very similar to those found on films consisting of only a single microporous layer, contrary to what would be expected for complete filling of the pores by liquid. Values for wetted surface fractions calculated from the Cassie-Baxter model are unreasonably high for micro- , meso- and oxidized macroporous films considering their porosities, while relatively oxide-free macroporous films give wetted surface fractions in accord with those expected based on film porosity. Collectively, these results show that the predominant mode of wetting on these films is heterogeneous.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.154
GPT teacher head0.273
Teacher spread0.119 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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