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Record W2093830751 · doi:10.1116/1.2148413

Conduction anisotropy in porous thin films with chevron microstructures

2005· article· en· W2093830751 on OpenAlexafffund
D. Vick, Michael J. Brett

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2005
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceAnisotropyChevron (anatomy)MicrostructureThin filmThermal conductionSubstrate (aquarium)Composite materialPorosityConductivityDeposition (geology)Electrical resistivity and conductivityLayer (electronics)TitaniumOpticsNanotechnologyMetallurgyChemistryElectrical engineeringGeology

Abstract

fetched live from OpenAlex

Electrical conductivity measurements were performed on structurally anisotropic thin films deposited using the glancing angle deposition apparatus [K. Robbie and M. J. Brett, J. Vac. Sci. Technol. A 15, 1460 (1997); K. Robbie, J. Sit, and M. J. Brett, J. Vac. Sci. Technol. B 16, 1115 (1998); K. Robbie and M. J. Brett, US Patent No. 5,866,204 (2 February 1999)]. The films were comprised of bilayers of titanium over silica, engineered as a chevron morphology. Samples were evaporated at various incident vapor deposition angles α, in order to investigate the effects of morphology and voiding on the behavior of conductivity. A rapid decline in the conductivity, accompanied by an increase in conduction anisotropy in the plane of the substrate, was observed with increasing α. A random walk model was developed to model the transport properties of the films, and applied to microstructures predicted by a three-dimensional ballistic thin film simulator. In order to generate reasonable agreement between the modeling and measurement, it was necessary to incorporate the effect of native oxide formation on the exposed surfaces of the titanium layer.

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 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 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 score0.073
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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 teacher head, 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

Citations27
Published2005
Admission routes2
Has abstractyes

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