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Record W2046978965 · doi:10.1143/jjap.40.6574

Substrate Bias Effect on Amorphous Hydrogenated Carbon Films Deposited by Filtered Cathodic Arc Deposition

2001· article· en· W2046978965 on OpenAlexfundno aff
Yan-Way Li, Chia‐Fu Chen, Yew-Bin Shue, Teng-Chien Yu, Jack Chang

Bibliographic record

VenueJapanese Journal of Applied Physics · 2001
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
FundersNational Research Council CanadaNational Science Foundation
KeywordsMaterials scienceAmorphous carbonRaman spectroscopyCarbon filmSubstrate (aquarium)NanoindentationAmorphous solidCarbon fibersNucleationChemical engineeringMicrostructureGraphiteThin filmAnalytical Chemistry (journal)MetallurgyComposite materialNanotechnologyCrystallographyChemistryOrganic chemistryOpticsComposite number

Abstract

fetched live from OpenAlex

In the present study, we briefly describe the 45° angle magnetic filtered arc deposition (FAD) process and investigate the effect of substrate bias on the hardness of amorphous carbon (a-C) films. An attempt is made to correlate the microstructure, chemical composition and chemical bonding states with the hardness of the corresponding films. After deposition, the film properties were analyzed by Raman spectroscopy and nanoindentation system (NIS). It was found that amorphous carbon films possess highest hardness when deposited at substrate biases ranging from -50 V to -100 V. The hardness values do not show good correlation with Raman I (D)/ I (G) ratio. Hydrogen additions to the system help prevent the nucleation of the graphite phase, and stabilize the sp 3 bonding of amorphous hydrogenated carbon films. Hydrogen affected on the small graphitic crystalline growth. Films have higher hardness when they have higher fraction of sp 3 content.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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.

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

Citations2
Published2001
Admission routes1
Has abstractyes

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