Synthetic Lubricants Derived from Plastic Waste and their Tribological Performance
Bibliographic record
Abstract
Abstract The energy efficiency, mechanical durability, and environmental compatibility of all moving machine components rely heavily on advanced lubricants for smooth and safe operation. Herein an alternative family of high‐quality liquid (HQL) lubricants was derived by the catalytic conversion of pre‐ and post‐consumer polyolefin waste. The plastic‐derived lubricants performed comparably to synthetic base oils such as polyalphaolefins (PAOs), both with a wear scar volume (WSV) of 7.5×10 −5 mm −3 . HQLs also performed superior to petroleum‐based lubricants such as Group III mineral oil with a WSV of 1.7×10 −4 mm −3 , showcasing a 44 % reduction in wear. Furthermore, a synergistic reduction in friction and wear was observed when combining the upcycled plastic lubricant with synthetic oils. Life cycle and techno‐economic analyses also showed this process to be energetically efficient and economically feasible. This novel technology offers a cost‐effective opportunity to reduce the harmful environmental impact of plastic waste on our planet and to save energy through reduction of friction and wear‐related degradations in transportation applications akin to synthetic oils.
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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".