Analytical Surface Energy Model of Fine Copper-Graphite Core-Shell Particles in Oil Lubricant
Bibliographic record
Abstract
Surface energy is a key quantity that controls many physical, mechanical, and tribological properties of materials.However, determining their values at various scales remains challenging to evaluate how they affect dispersion behavior and frictional performance.Analytical surface energy modelling for lubricating additives in die lubricant oil is made to evaluate the surface energy as a function of tribological characteristics with complex compositional dispersion in two conditions: (i) separately adding graphite and copper particles at the same time to the oil and (ii) adding the particles to the oil in the form of copper-graphite core-shell composite structure.We have introduced the surface-induced excess energy and discussed this value before and after friction according to the particle diameter and powder content wt.% in the lubricant oil.Results revealed that the adding of graphite and copper particles as a core-shell composite structure can play an excellent role as compared to separately adding the graphite and copper particles to the oil.The ratio of total surface energy after friction process to that of before friction in separately introduced the components, and in the form of core-shell composite was equal to 7 and 5, respectively.Meanwhile, the ratio of the total surface energy of the core-shell composite structure to that of separately introduced the components before and after the friction process is equal to 2 and 1.4, respectively.However, the composite with (2.5-3) wt.% content and a 10 µm diameter of particles indicates 3 and 3.4 values of the total surface energy ratio in cases of separate and core-shell structure conditions, respectively.The results indicate that the total surface energy for the core-shell composite is more effective than separate powders of graphite and copper added in oil.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".