Glycerol‐Based Polyurethane Nanoparticles Reduce Friction and Wear of Lubricant Formulations
Bibliographic record
Abstract
Abstract The relative motion of two surfaces in direct contact results in friction and wear. This affects every moving surface, contributing to a quarter of the worldwide energy consumption. The addition of lubricant can reduce friction by separating the surfaces, making more energy‐efficient systems. Lubricants are composed of a base oil and a series of additives. Molecules like glycerol can improve the efficiency of a lubricant system. However, the direct addition of hydrophilic molecules to hydrophobic lubricant oils is challenging due to their poor miscibility. The encapsulation of glycerol, or other hydrophilic additives, in nanocarriers will enable the design of additive systems delivering poorly miscible molecules to the lubricant. Here, glycerol is encapsulated in cross‐linked glycerol nanocapsules. The nanocarrier is dispersed in a lubricant oil and placed between two metal surfaces. The release of the additive, from the nanocarriers, is triggered by the force applied on the nanocarriers by the metal surfaces in contact. The release observed is dependent on the applied force and mechanical properties of the nanocarrier, which can be controlled during the synthesis. The addition of those mechanoresponsive nanocarriers improved the long‐term performance of the lubricant and represents a step toward the reduction of friction between metal–metal contacts.
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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".