X-Ray Photoelectron Spectroscopy Analysis of Antiwear Tribofilms Produced on Boundary-Lubricated Steel Surfaces from Sulfur- and Phosphorus-Containing Additives and Metal Deactivator Additive
Bibliographic record
Abstract
X-ray photoelectron spectroscopy (XPS) was performed on AISI 52100 steel surfaces subjected to sliding in the boundary lubrication regime at 32 and 100°C. The specimens were lubricated with base oil blended with individual additives containing sulfur (S), phosphorus (P), or metal deactivator, as well as base oil with all the previous additives in the same amounts as in the single blends. XPS spectra were analyzed to confirm the formation and determine the chemical composition of the antiwear tribofilms produced on the steel surfaces during sliding. The use of S- and P-containing additives on the tested disk surfaces revealed that tribochemical reactions resulted in the formation of antiwear tribofilms containing S- and P-rich components. Results for the multi-additive blend provided evidence for two components in the produced tribofilm, appearing to consist primarily of sulfide and phosphate. This investigation provides new insight into the competing roles of these compounds on the tribological properties of the antiwear tribofilms. The significance of the sulfide components is demonstrated by the more pronounced antiwear effect of the S-containing additive in the multi-additive formulation.
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".