The chemical characterization of tribofilms using XANES — Interaction of nanosize calcium-containing detergents with zinc dialkyldithiophosphate
Bibliographic record
Abstract
A Plint friction and wear tester was used to investigate the effect of several calcium-containing detergents on the tribological and tribochemical performance of a zinc dialkyldithiophosphate (ZDDP) lubricating oil additive in a low-sulfur base stock, 100N. Thus, the friction and wear behavior of a steel-on-steel contact lubricated by 100N oil containing ZDDP alone and ZDDP–detergent mixtures at 100 °C was evaluated in a pin-on-disc configuration. The wear scar width of the upper steel pins was determined using an optical microscope, while the tribofilms formed on the lower steel discs were analyzed using X-ray absorption near edge structure (XANES) spectroscopy and X-ray photoelectron spectroscopy (XPS). At the same time, the thermal-oxidation films of the oil blends containing different additives were also prepared on the same steel discs and analyzed using XANES spectroscopy for comparative studies. It was found that in simple formulations the three kinds of calcium-containing detergents improved the friction-reducing and antiwear abilities of the 100N base stock. This was related to the individual tribochemical reactions and the deposition (in one case) of nanosized CaCO3 on the rubbing steel surface, indicating that the calcium-containing detergent had a synergistic antiwear performance with the ZDDP tested in the present work. Moreover, the calcium-containing detergents contributed to retarding the thermo-oxidation and friction-induced decomposition of ZDDP in 100N oil and influenced the composition and thickness of the tribofilms, which could be dependent on the molecular structures of the detergents and directly related to the tribochemistry of ZDDPs in mineral oil. The rubbing of the steel–steel pair at 100 °C was more beneficial for the deposition of nanosized calcium carbonate on the steel surface than heating at 150 °C. The calcium-containing detergents alone in the base stock also experienced tribochemical reactions, leading to obvious changes in the oxidation state of S in the corresponding tribofilms. Therefore, it was supposed that the tribochemical reactions of the ZDDP and detergents together with the deposition of nanosized CaCO3 on the rubbing steel surfaces accounted for the good antiwear performance of the blended oils.Key words: ZDDP, detergent, thermal film, tribofilm, tribochemistry, XANES, XPS.
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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".