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Record W2043595767 · doi:10.1063/1.2213349

Effect of substrate surface on the structure and electronic properties of cubic boron nitride films

2006· article· en· W2043595767 on OpenAlexafffund
X. T. Zhou, Tsun‐Kong Sham, C. Y. Chan, Wenjun Zhang, I. Bello, S. T. Lee, H. Hofsäß

Bibliographic record

VenueJournal of Applied Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCity University of Hong Kong
KeywordsBoron nitrideMaterials scienceNanocrystalline materialCrystalliteMicrocrystallineSurface roughnessSurface energyCrystallographyCrystal structureComposite materialNanotechnologyMetallurgyChemistry

Abstract

fetched live from OpenAlex

Cubic boron nitride (c-BN) films were prepared by mass-selected ion beam deposition (MSIBD) technique. The effects of substrate surface roughness were investigated by boron and nitrogen k-edge x-ray absorption near-edge structure, x-ray diffraction, and atomic force microscopy. All the films are a mixture of nanocrystalline sp3-bonded c-BN and sp2-bonded BN phases. The substrate with a rough surface causes a decrease of the c-BN phase content of the film on it. A significant large lattice contraction of the c-BN crystallites in the films, relative to the bulk, is observed. It is also found that the electronic structure of the nanocrystalline c-BN films by MSIBD technique is somewhat different from that of microcrystalline c-BN∕h-BN references. We attribute the effect of the nature of the substrate on the morphology and structure of the c-BN films to the orientation of sp2-bonded graphitic BN basal plane on the top surface of the films during their growth, and the lattice contraction and energy band structure modification of c-BN films to the large compressive stress, respectively.

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 score0.008
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.217
Teacher spread0.211 · 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

Citations9
Published2006
Admission routes2
Has abstractyes

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