Surface-Modified Sepiolite Nanofibers as a Novel Lubricant Additive
Bibliographic record
Abstract
Abstract Lubricants are an essential component in high-performance mechanical equipment, but traditional lubricants are becoming inadequate for meeting the increasing demands for anti-friction and anti-wear properties and they discharge harmful chemicals to the environment. The purpose of the present study was to explore the use of sepiolite as a novel oil additive to extend the performance of lubricants. Sepiolite nanofibers were first treated by acid followed by a dry air flow, aimed at increasing the pore volume and decreasing the particle size. Then the nanofibers were further modified by an organosilane coupling agent to reduce the surface free energy and to improve the dispersion stability in lubricant. A significant improvement in the performance of the lubricant was achieved by using the modified sepiolite nanofibers as an additive. When the amount of modified sepiolite nanofibers added was 1.5 wt.%, the best performance was demonstrated by the lubricant, showing a viscosity increase at 40°C and 100°C, and an increase in resistance to oxidation. Moreover, the acid value and pour point decreased, and the copper sheet corrosion level dropped to its lowest value.
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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) | 0.000 | 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".