XANES Study of Tribofilm Formation With Low Phosphorus Additive Mixtures of Phosphonium Ionic Liquid and Borate Ester
Bibliographic record
Abstract
The development of low phosphorus engine oils is important to minimize phosphorus-induced exhaust catalyst poisoning and resulting in harmful emissions. In this study, low phosphorus oil formulations were prepared by using an ashless additive mixture of borate ester (SB) with ionic liquid composed of a phosphonium cation and phosphate anion (P_DEHP) at 350 and 700 ppm phosphorus. Tribological properties of this binary additive system were evaluated using a reciprocating cylinder on a flat test configuration. Favorable interaction between P_DEHP and SB resulted in a significant reduction in friction coefficient and wear volume, in particular for P_DEHP(700P) + SB oil blend. Time-scale analysis of tribofilm formation was determined by running the tribological experiments for 5, 15, and 60 min duration. Electrical contact resistance (ECR) results revealed that the addition of P_DEHP at 350 ppm of phosphorus to SB at 500 ppm of boron can reduce the incubation time from 300 to 100 s for stable tribofilm formation. X-ray absorption near-edge spectroscopy (XANES) analysis of tribofilms indicates that the tribofilm mechanism for additive mixtures of P_DEHP and SB initially involves the formation of boron oxide-based films, which later interact with phosphorus to form boron phosphates in addition to iron phosphates. Incorporation of the high amount of boron phosphates in addition to boron oxide/acid and iron phosphates in the tribofilms contributed to the improved tribological performance of P_DEHP(700P) + SB oil. XANES results reveal that tribofilms formed due to the interaction of SB and P_DEHP evolve to a cross-linked structure, wherein the chain length of polyphosphates is increased with the increase in rubbing time.
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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".