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Record W2047740601 · doi:10.1088/0957-4484/16/11/028

Embedded air and solid defects in periodically structured porous thin films

2005· article· en· W2047740601 on OpenAlexafffund
Martin O. Jensen, Michael J. Brett

Bibliographic record

VenueNanotechnology · 2005
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceThin filmDecoupling (probability)MicrostructureChemical vapor depositionPorosityDeposition (geology)Substrate (aquarium)OptoelectronicsCombustion chemical vapor depositionComposite materialCarbon filmNanotechnology

Abstract

fetched live from OpenAlex

Highly porous thin films with columnar microstructures are applicable to many optical, chemical, and electronic devices, and can be fabricated using the glancing angle deposition method for physical vapour deposition onto tilted substrates. In a recent advancement of this method, it was shown that decoupling of the vapour incidence direction from the column growth direction provides significant flexibility to engineer the pore structure of the films. Here we elaborate on the decoupling principle by applying it to chiral thin films with a periodic microstructure, and demonstrating how it leads to improved film uniformity and the elimination of column broadening. We also show the effects of such depositions onto substrate seed layers with intentional defects. Substrate based defects normally transfer to thin films as air filled defects, but here we present for the first time that a simple adjustment of the deposition parameters can invert the normally air filled defects to become solid, evaporant filled defects. This defect engineering capability provides new opportunities for the deployment of glancing angle deposition thin films to photonic bandgap crystals and microfluidic devices.

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)0.000
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 score0.022
Threshold uncertainty score0.495

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)0.0000.000

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 spread0.234 · 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

Citations7
Published2005
Admission routes2
Has abstractyes

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