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Preparation and Nanomechanical Characterisation of Metal Containing Amorphous Hydrogenated Carbon Nanocomposite Films

2010· article· en· W2068686815 on OpenAlexaff
P. Vijai Bharathy, D. Nataraj, Q. Yang, D. Mangalaraj, Mangalampalli S. R. N. Kiran

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsNanoindentationMaterials scienceX-ray photoelectron spectroscopyNanocompositeCarbideRaman spectroscopyAmorphous carbonCarbon fibersGraphiteMicrostructureMetalAmorphous solidSputteringCarbon filmChemical engineeringComposite materialThin filmMetallurgyNanotechnologyCrystallographyComposite numberChemistry

Abstract

fetched live from OpenAlex

The relationship between metal-induced chemical bonding and the mechanical properties of Me/a:C-H (Me- Ti and Ni) films are discussed. Nanocomposite films were deposited onto the Si substrates via biased target ion beam sputtering of metal combined with reactive ion beam deposition of a:C-H using CH4/Ar gas mixture. The chemical composition, microstructure and mechanical properties were characterized using X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and nanoindentation. XPS analysis revealed that both Ti and Ni atoms were preferentially bonded to carbon to form its metal carbide phase. Irrespective of its kind, both the metal carbide clusters induces more graphite like bondings in a:C-H matrix. From the nanoindentation analysis it was found that there is no crack propagation and damage around the indent region even at very high indentation loads. Additionally, it was found that a:C-H film shows higher hardness compared to that of Ti/a:C-H and Ni/a:C-H films.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.341
Teacher spread0.318 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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