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Record W2050584086 · doi:10.1088/0957-4484/16/10/002

Control of power law scaling in the growth of silicon nanocolumn pseudo-regular arrays deposited by glancing angle deposition

2005· article· en· W2050584086 on OpenAlex
Cristina Buzea, Gisia Beydaghyan, Chelsea Elliott, Kevin Robbie

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNanotechnology · 2005
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSiliconAspect ratio (aeronautics)EvaporationOpticsSubstrate (aquarium)ScalingDeposition (geology)ExponentChemical vapor depositionPower lawMorphology (biology)OptoelectronicsGeometryPhysics

Abstract

fetched live from OpenAlex

Nanocolumn pseudo-regular arrays of silicon with controlled aspect ratio and porosity are fabricated by electron-beam evaporation using the glancing angle deposition (GLAD) method with vapour impinging at oblique incidence onto rapidly rotating substrates. The width W at positions y along the height of one individual column scales with y following a power law dependence W approximately y(p). We demonstrate that the scaling exponent value, p, can be modified from 0.6 to 0.3 by varying the vapour incidence angle from 75 degrees to a glancing 89 degrees from the substrate normal. This exponent is an important morphological factor for thin films, as it determines the morphological correlation length, nanocolumn profile, size, and spacing. The nanocolumn mean diameter can be varied between 12 and 40 nm, while the intercolumnar spacing can be adjusted between 37 and 85 nm via changing the incidence angle. The growth mechanism and film morphology are explored in detail.

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 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 score0.019
Threshold uncertainty score0.386

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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