Morphology and Composition of Brake Wear Particles Ameliorated by an Alumina Coating Approach
Bibliographic record
Abstract
A plasma-assisted electrochemical deposition (PAECD) technology was introduced to coat a cast iron brake disc for the possible reduction of brake wear and brake wear particle (BWP) emission. The majority of the coating consisted of alumina (Al2O3), determined by energy dispersive X-ray (EDX) analysis and X-ray diffraction (XRD) analysis. To validate the above strategy of the coating technology for automotive brake corners, one brake stock rotor was replaced by a PAECD-coated rotor for a vehicle road test. After the road test, weight loss of the brake components (rotors and pads) was measured, showing that the alumina coating can reduce the brake wear by more than 70%. BWPs were also collected from wheel barrels, spokes, and brake friction rings of the coated and uncoated rotors during the road test. A morphology and chemical composition analysis of the collected BWPs indicated that the coating could reduce BWP generation from the original sources and avoid a metal pick-up (MPU) issue, leading to less metallic content in BWPs. This alumina coating may provide the auto sector with a sustainable approach to overcome the brake dust emission problem, evidenced by less wear of the brake pads, minimal wear of the coated brake rotor, less MPUs, and a clean wheel rim on the coated brake corner.
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".