Glycerol‐Based Polyurethane Nanoparticles Reduce Friction and Wear of Lubricant Formulations
Why this work is in the frame
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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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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) | 0.000 | 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 it