Chemical and physical properties of tribofilms formed by the interaction of ashless dithiophosphate anti‐wear additives
Bibliographic record
Abstract
Abstract Physiochemical properties of tribofilms generated from octadecylthioperoxydiphosphate (SSC18) and octadecylphosphorofluoridothioate (PFC18) were compared with zinc dialkyl dithiophosphates (ZDDP) in this study. PFC18 and SSC18 were chemically synthesised and mixed in mineral base oil to prepare test oil formulations. Additionally, PFC18 and SSC18 were mixed at different concentrations and evaluated for their compatibility and tribological performance using high frequency reciprocating rig (HFRR) tribometer. All lubricant formulations with ashless additives exhibited superior anti‐wear characteristics compared to ZDDP. Comprehensive surface analysis of tribofilms was conducted using scanning electron microscopy, nano‐indentation and X‐ray absorption near edge spectroscopy. Formulations containing only SSC18 and PFC18 exhibited a uniform and continuous tribofilm formation, while formulations with a combination of SSC18 and PFC18 had a stable pad‐like structure. X‐ray absorption near edge structure (XANES) results indicate ashless PFC18 and SSC18 by themselves and in combination form iron phosphate enriched tribofilms with a mixture of iron sulfides and/or sulfates.
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".