Shear-dependent tribological behavior of oleic acid as a sustainable lubricant additive in oils and nano-greases
Bibliographic record
Abstract
The depletion of lithium reserves and growing demand for environmentally benign, high-performance lubricants have spurred research into biodegradable additives such as nano-clays and nanocellulose as potential thickener for grease. The integration of environmentally friendly functional additives to prevent nanoparticle agglomeration, is also desired for future lubricant developments. Oleic acid (OA), an amphiphilic molecule, has proven efficacy as functionalizing agent in preventing nanoparticle agglomeration. However, its role as an additive in oils and complex grease colloidal systems extends beyond preventing nanoparticle agglomeration and requires further investigation, specifically its impact on wear and friction reduction on metallic steel-surfaces. This study investigates the tribological properties of OA under different speed/load leading to different lubricant regimes: as an additive in polyalphaolefin (PAO) oil at different concentrations from 1 % to 20 %, as a dispersant for nanoclay-thickened base-oil in nanogreases, and as a dispersant for nanocellulose/nanoclay-thickened base oil. The tribological outcomes reveal that OA significantly reduces friction at low sliding speeds, concurrently mitigating wear compared to PAO oils at respective loads. Conversely, at higher speeds, friction diminishes relative to boundary lubrication , but wear behavior becomes more complex, i.e. increases for lower loads (10N) whereas decreases at 20N compared to PAO. In case of functionalized nanoclay and TOCN greases, the accessibility of OA anchored to nanoclay/TOCN, and residual OA lowers friction at low speeds but exacerbates wear at low loads. The wear response at higher loads is non-monotonic owing to heterogenous microstructure in grease which alters OA and oil accessibility to contact zone upon sliding for film formation. This work elucidates the performance of OA depend upon its accessibility in complex greases and its packing density at metallic interface to form robust boundary films as confirmed from multiple surface analytical techniques (SEM-EDX, AFM) and spectroscopic methods (Raman, FTIR), highlighting the intricate relationship of OA films at different shear conditions, and resultant wear of metallic interfaces.
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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".