Oxidation and Antiwear Retention Capability of Low-Phosphorus Engine oils
Bibliographic record
Abstract
Future vehicle emission regulations both in the US and Europe will require maintaining catalyst efficiency for longer mileage intervals. In order to achieve this requirement, chemical restrictions are being placed on elements in engine oil that can poison catalysts. Most of phosphorus and a significant amount of sulfur in current engine oils come from zinc dialkyldithiophosphates, ZDDPs, which are a class of cost-effective multifunctional additives providing wear, oxidation and corrosion protection. Reducing ZDDP concentrations raises oxidation and wear concerns. The overall purpose of this research is to look at the antioxidation and antiwear capability of low phosphorus engine oils containing 0.05 wt% phosphorus and the potential of engine oils formulated without phosphorus. In addition to fresh oils, used oils drained from fleet vehicles were also analyzed and evaluated. The results indicate that by appropriately selecting and balancing supplemental phosphorus-free antioxidation and antiwear additives the antioxidation capability can be improved for low phosphorus and even non-phosphorus oils, and the antiwear performance of low phosphorus oils could be maintained or even improved.
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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".