Tribology improvement of graphene/polyamide-imide composite coating under current-carrying friction
Bibliographic record
Abstract
Graphene is a promising nano-additive in polymer-based coatings to improve their tribology performance under current-carrying conditions due to its unique electrical, thermal and mechanical properties. Here, the current-carrying friction behavior of polyamide-imide (PAI) matrix composite coating incorporated with graphene prepared by high-temperature solicitation is studied using a homemade ball-on-disc tribometer. It is revealed that both coefficient of friction and wear rate of graphene/PAI composite coating are lower than those of pure PAI coating. The reduction in wear rate of PAI coating by the addition of graphene is increased by >7 times when the current intensity rises from 0 to 4.5 A, indicating better tribology enhancement of graphene under current-carrying conditions than under dry conditions. Post-test surface analyses demonstrate that the tribology enhancement mechanism mainly arises from the quick in-situ formation of graphene-rich transfer film promoted by current heat and the highly suppressed arc erosion on worn surface. This work provides a novel method to broaden wear resistant polymer-based coatings in the field of current-carrying lubrication application.
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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".