Boosting the Friction Performance of Amine Friction Modifiers with MoDTC
Bibliographic record
Abstract
For years amine surfactants, such as primary amines, ethoxylated amines and polyamines, have been used as friction modifiers in lubricating oils in order to improve fuel economy. This paper describes how the friction performance of amine containing lubricating oils can be improved with the addition of a small amount of molybdenum dithiocarbamate (MoDTC). Three fatty amines, tallow amine (Armeen® T), tallow propanediamine (Duomeen® T) and tallow dipropylenetriamine (Triameen® T), have been tested with Zinc Dialkyldithiophosphate (ZDDP) and with and without MoDTC in the Minitraction machine (MTM). It is shown that MoDTC improves the friction of Duomeen T and Triameen T while not for Armeen T. It is argued that the packing of Armeen T does not allow MoDTC to reach the surface and to create molybdenum disulphide (MoS2) sheets. Duomeen T and Triameen T have more nitrogen atoms and cannot pack as closely at the surface as Armeen T which allow MoS2 sheets to form. To validate the theory, Scanning Electron Microscopy combined with Energy Dispersive Xray Analysis (SEM-EDX) has been used to determine the amount of sulphur in the tribofilm. The SEM-EDX data show more sulphur in the tribofilm of Duomeen T compared to Armeen T which could be due to the formation of more MoS2.
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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) | 0.002 | 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".