Effects of Lubricants on the Friction and Wear Properties of PTFE and POM
Bibliographic record
Abstract
The friction and wear properties of PTFE and POM were investigated using a ball-on-steel ring tester under dry and lubricated conditions by paraffin and 10W–30 oils. SEM, EDAX, FTIR, and surface wettability techniques were used to characterize and assess the morphology and chemical composition of the original surfaces, as well as the wear track, transfer film, and wear debris at different loads and speeds. Although the friction was high, similar behavior to that reported in the literature was observed. The experimentally determined surface temperature of the plastic revealed optimum loading levels, for each sliding speed, at which the friction and wear rate become a minimum and the thermal effect stabilizes. In boundary-like lubrication using both oils, friction and wear were significantly reduced with the exception of an increase in wear rate with load when POM was lubricated by 10W–30 oil. Surface analysis revealed that the formation of lubricative protective layers on the surfaces in contact is crucial to reducing friction, and more importantly, wear. FTIR results confirmed that film transfer occurs in the case of POM.
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 | 0.001 | 0.002 |
| 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".