Graphene as an additive in complex lithium grease: A comprehensive analysis of friction, wear and thermal behaviour
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the tribological performance of graphene-enhanced complex lithium greases, focusing on friction reduction, wear resistance, and thermal stability. Various weight percentages of graphene (0.5 wt%, 0.75 wt%, 1 wt% 2 wt%) were added into the grease matrix, and their effects were evaluated through multiple experimental tests, including the four-ball wear test, thermal stability assessments and water resistance tests. The results demonstrated that lower graphene concentrations, particularly 0.5 wt%, offered the best balance of performance, providing significant reductions in friction and wear while improving thermal stability and water resistance. Higher concentrations, while improving thermal stability, exhibited diminishing returns in tribological performance due to agglomeration. This research highlights the potential of graphene as a lubricant additive for industrial applications, especially in environments requiring high thermal resistance and mechanical stability. Future work should focus on opti-mizing dispersion techniques and exploring the synergy between graphene and other nanomaterials to further enhance grease performance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it