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Record W2090735812 · doi:10.1063/1.3289732

Structural analysis of silicon carbon nitride films prepared by vapor transport-chemical vapor deposition

2010· article· en· W2090735812 on OpenAlexaff
Y. Awad, My Alı El Khakani, Mihai Scarlete, Ç. Aktik, R. Smirani, N. Camiré, M. Lessard, J. Mouïne

Bibliographic record

VenueJournal of Applied Physics · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsBishop's UniversityInstitut National de la Recherche ScientifiqueUniversité de Sherbrooke
Fundersnot available
KeywordsX-ray photoelectron spectroscopyFourier transform infrared spectroscopyChemical vapor depositionAnalytical Chemistry (journal)Amorphous solidCarbon nitrideSiliconChemical bondMaterials scienceInfrared spectroscopySilicon nitrideCarbon fibersAmorphous carbonChemistryChemical engineeringCrystallographyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Amorphous silicon carbon nitride (a-SiCN:H) films were synthesized using vapor transport-chemical vapor deposition technique. Poly(dimethylsilane) was used as a single source for both Si and C. NH3 gas diluted in Ar is used as a source for nitrogen. The composition and bonding states are uniquely characterized with respect to NH3/Ar ratio by Fourier transform infrared spectroscopy (FTIR) and x-ray photoelectron spectroscopy (XPS). Spectral deconvolution is used to extract the individual components of the FTIR and XPS spectra. For instance, the FTIR spectra show a remarkable drop in the intensity of SiC vibration accompanied by the formation of further bonds including SiN, CN, CN, CN, and NH with increasing NH3/Ar ratio. Moreover, the XPS spectra show the existence of different chemical bonds in the a-SiCN:H films such as SiC, SiN, CN, CN, and CC. Both FTIR and XPS data demonstrate that the chemical bonding in the amorphous matrix is more complicated than a collection of single SiC SiN, or SiH bonds.

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 score0.002
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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