Tribological Assessment of Synthetic Grease (PDPLG-2) Derived from Partially Degraded Low-Density Polyethylene Waste
Bibliographic record
Abstract
This study focuses on addressing the pressing challenge of reusing plastic in an eco-friendly manner. This research aimed to produce synthetic grease through an environmentally friendly pyrolysis technique, utilizing 69% predegraded low-density polyethylene (LDPE) combined with visible-light-working TiO2 thin film, protein-coated TiO2 NPs, and Lactobacillus plantarum bacteria in a batch reactor. The optimized conditions of temperature (500 °C) and heating time (2 h) resulted in the creation of 166 gm of partially degraded polyethylene grease 2 (PDPLG2) with National Lubricating Grease Institute (NLGI 2) grade consistency. PDPLG2 grease exhibits a wide-range dropping point of 280 °C and effectively maintains lubrication under high friction and stress loads, thereby preventing wear. Thermal analysis using TG and DSC validated the grease’s stability up to 280 °C, with minimal degradation beyond this point. Taguchi analysis using substance, sliding speed, and load as factors identified the ideal process parameters as aluminum, 1500 rpm, and 150 N, respectively. The present study revealed that sliding speed has the greatest impact, contributing 31.74% to the coefficient of friction (COF) and 11.28% to wear, followed by material and load. Comparative tribological analysis with commercially available grease (NLGI2) demonstrated that PDPLG2 grease outperforms NLGI2 grease. Overall, this innovative eco-friendly approach presents PDPLG2 as a promising alternative lubricant with improved anti-wear and friction properties, while also contributing significantly to plastic waste reduction.
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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".