MétaCan
Menu
Back to cohort
Record W4390172297 · doi:10.2320/jinstmet.j2023017

Deposition of TiC Film by Microwave Sheath-Voltage Combination Plasma

2023· article· en· W4390172297 on OpenAlexaff
Yusuke Ushiro, Ippei Tanaka, Yasunori Harada, Takashi Ogisu

Bibliographic record

VenueJournal of the Japan Institute of Metals and Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsX-ray photoelectron spectroscopyMaterials scienceDeposition (geology)Analytical Chemistry (journal)Chemical vapor depositionVolumetric flow rateCoatingPlasma-enhanced chemical vapor depositionThin filmTitaniumTitanium carbideComposite materialChemical engineeringMetallurgyNanotechnologyChemistry

Abstract

fetched live from OpenAlex

To improve wear resistance and adhesion, a hard film of titanium carbide (TiC) is usually prepared by a plasma chemical vapor deposition technique. Microwave sheath-voltage combination plasma (MVP) is a method to generate high-density plasma. In the present study, TiC coatings were prepared by MVP and the processing conditions were examined to reveal the effect on film deposition speed. The TiC coatings were deposited in a reactor using a TiCl4-CH4-H2-Ar gas mixture. The phase identifications, binding energy analysis, coating composition, and friction coefficient were investigated by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDX), and ball-on-disk friction tester, respectively. TiC was detected by XRD. As the flow rate of the raw material gas increased, the film deposition rate increased. The maximum deposition rate of the films was 9.0 µm/h. By changing the film deposition conditions, it is considered that higher speed film deposition of TiC will be possible.

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)0.002
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 score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.014
GPT teacher head0.249
Teacher spread0.235 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Japan Institute of Metals and MaterialsSame topicDiamond and Carbon-based Materials ResearchFrench-language works237,207