Tribological Performance of a Plasma Electrolytic Oxidation-Coated Mg Alloy in Graphene-Incorporated Ethanol
Bibliographic record
Abstract
This study investigated the friction and wear characteristics of a plasma electrolytic oxidation (PEO)-coated Mg–Al alloy (AZ31) in sliding contact against steel using graphene nanoplatelets (GNPs) containing ethanol as a lubricant. The results revealed that the typically high coefficient of friction (COF) of PEO-coated surfaces under dry sliding (0.74) was notably reduced to 0.18 during the sliding tests conducted in GNP-free ethanol. When the ethanol contained 5 × 10−4 wt.% GNPs, the COF of the uncoated AZ31 alloy further dropped to 0.17. The PEO-coated surfaces achieved a significantly lower COF of 0.07 and demonstrated a marked reduction in wear rate, attributed to the formation of a tribolayer incorporating graphene. These findings highlight the significant potential of GNP-incorporated ethanol to improve the tribological performance of PEO-coated AZ31, presenting a promising avenue for advancing lightweight, sustainable, and efficient automotive technologies.
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 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".