Metastable hybridized structure transformation in amorphous carbon films during friction—A study combining experiments and MD simulation
Bibliographic record
Abstract
Abstract Amorphous carbon films have attracted substantial interest due to their exceptional mechanical and tribological properties. Previous studies revealed that the amorphous carbon films exhibited lower coefficient of friction (COF) because of the transformation in bond structure from sp 3 -C to sp 2 -C during friction processes. However, the mechanism for such a transformation during friction is not well understood. This study is conducted to get an insight into the metastable transformation in amorphous carbon film during friction by means of experiments and molecular dynamics (MD) simulation. Relevant wear tests showed that wear of the film changed from an abrasive wear mode to a mixture of abrasion and adhesive wear, resulting in a decrease in growth rate of the wear rate after the running-in stage. It is worth noting that the sp 3 -C atoms were increased during the running-in stage when the films contained lower sp 3 /sp 2 ratios. However, the formed sp 3 -C atoms could only be short-lived and gradually transformed to sp 2 -C atoms with the graphitization generated on the wearing surface of the films. The radial distribution function and translational order parameter indicated that the films’ high sp 3 /sp 2 ratio led to an increased sp 2 -C proportion on the wear scar after friction, which caused an increased structural ordering.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".