MétaCan
Menu
Back to cohort
Record W2092455896 · doi:10.1002/sia.3800

Bioactivity and mechanical properties of nickel‐incorporated hydrogenated carbon nanocomposite thin films

2011· article· en· W2092455896 on OpenAlexafffund
P. Vijai Bharathy, D. Nataraj, Q. Yang, Mangalampalli S. R. N. Kiran

Bibliographic record

VenueSurface and Interface Analysis · 2011
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Saskatchewan
FundersGovernment of Canada
KeywordsMaterials scienceNickelNanoindentationX-ray photoelectron spectroscopyNanocompositeChemical engineeringThin filmMicrostructureCarbon fibersCarbon filmAmorphous solidAmorphous carbonCarbideComposite materialMetallurgyComposite numberNanotechnologyChemistryCrystallography

Abstract

fetched live from OpenAlex

In this paper, the influence of nickel incorporation on the mechanical properties and the in vitro bioactivity of hydrogenated carbon thin films were investigated in detail. Amorphous hydrogenated carbon (a‐C:H) and nickel‐incorporated hydrogenated carbon (Ni/a‐C:H) thin films were deposited onto the Si substrates by using reactive biased target ion beam deposition technique. The films' chemical composition, surface roughness, microstructure and mechanical properties were investigated by using XPS, AFM, TEM, nanoindentation and nanoscratch test, respectively. XPS results have shown that the film surface is mainly composed of nickel, nickel oxide and nickel hydroxide, whereas at the core is nickel carbide (Ni 3 C) only. The presence of Ni 3 C has increased the sp 2 carbon content and as a result, the mechanical hardness of the film was decreased. However, Ni/a‐C:H films shows very low friction coefficient with higher scratch‐resistance behavior than that of pure a‐C:H film. In addition, in vitro bioactivity study has confirmed that it is possible to grow dense bone‐like apatite layer on Ni/a‐C:H films. Thus, the results have indicated the suitability of the films for bone‐related implant coating applications. Copyright © 2011 John Wiley & Sons, Ltd.

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 score0.287
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.198
Teacher spread0.173 · 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

Citations4
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueSurface and Interface AnalysisSame topicMetal and Thin Film MechanicsFrench-language works237,207