INFLUENCE OF TUNGSTEN CONTENT IN W-DLC NANOCOMPOSITE THIN FILMS PREPARED BY HYBRID TARGET BIASED ION BEAM ASSISTED DEPOSITION TECHNIQUE
Bibliographic record
Abstract
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 sp2 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".