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Record W2055188632 · doi:10.1142/s0219581x11009295

INFLUENCE OF TUNGSTEN CONTENT IN W-DLC NANOCOMPOSITE THIN FILMS PREPARED BY HYBRID TARGET BIASED ION BEAM ASSISTED DEPOSITION TECHNIQUE

2011· article· en· W2055188632 on OpenAlexaff
P. Vijai Bharathy, D. Nataraj, D. Mangalaraj, Mangalampalli S. R. N. Kiran, Joaquín Silvestre‐Albero, Q. Yang

Bibliographic record

VenueInternational Journal of Nanoscience · 2011
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceX-ray photoelectron spectroscopyTungstenRaman spectroscopyDiamond-like carbonNanocompositeNanoindentationCarbon fibersSubstrate (aquarium)Chemical engineeringIon beam depositionAnalytical Chemistry (journal)Carbon filmDeposition (geology)Thin filmNanotechnologyComposite materialIon beamIonMetallurgyComposite numberOptics

Abstract

fetched live from OpenAlex

Tungsten incorporated diamond like carbon nanocomposite films were deposited onto Si substrate by using target biased ion beam assisted deposition. The effect of W target bias voltage on the chemical bonding, structure, surface morphology and mechanical properties of DLC films were investigated by means of XPS, Raman spectroscopy, AFM and Nanoindentation. It was found that the content of W in the films increased from 6 at.% to 13.7 at.% due to the increase in target bias voltage from -300 V to -700 V. XPS analysis revealed that most of the tungsten starts to react with carbon to form WC nanoparticles. Raman analysis shows that with the increase of W fraction in the DLC matrix, the intensity ratio I D /I G increases and the G band shifts to higher wavenumber. Thus it proves that the incorporation of tungsten leads to increase in sp 2 hybridized carbon content, and hence decrease in the hardness of W-DLC films compared to that of the pure DLC films. The result of AFM indicates that the surface roughness of the DLC gets modified with the incorporation of tungsten.

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 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 score0.010
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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