In Situ Synthesized Glycerol‐Releasing Nanocarriers Unlock Superlubricity in Oil‐Based Lubricants
Bibliographic record
Abstract
ABSTRACT Superlubricity, a state where friction is virtually non‐existent, mitigates uneconomical energy losses caused by mechanical motion. Glycerol exhibits excellent superlubrication properties, especially in combination with inert coatings. The addition of glycerol to existing oil‐based lubricants is a promising approach to maximize lubricity. However, miscibility issues hamper the use of hydrophilic glycerol as an additive in hydrophobic lubricant base oils. Therefore, we developed glycerol nanocarriers that are dispersible in hydrophobic oils and contain unreacted glycerol as a payload inside a cross‐linked glycerol‐based polyurethane shell. This in situ synthesis uses inverse miniemulsion polymerization in lubricant base oils as the continuous phase to obtain glycerol nanocarriers. The synthesis in the lubricant base oil significantly increases the glycerol loading capacity with unreacted glycerol compared to previously reported sponge‐like nanoparticles. We demonstrated that varying the amount of comonomer used alters the quantity of encapsulated glycerol. Consequently, the observed lubricity of the formulated lubricants at the tribological contact can be tuned systematically. Ultimately, these novel glycerol‐based nanocarriers provided superlubricity (CoF < 0.01) when combined with diamond‐like carbon–coated surfaces, bringing us closer to ideal, frictionless movements under real‐life operating conditions.
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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".