Targeted Repair of Super‐Lubricating Surfaces via Pairing Click Chemistry
Bibliographic record
Abstract
Abstract Intact advanced lubricating coatings can rival natural hydration lubricating systems. However, once damaged, their lubricity is drastically diminished as the delaminated coating materials are either unable to re‐bond to the original substrate due to the irreversible bond breakage or easily bridge the opposing rubbing surface via non‐specific interactions. Inspired by the reversibility and selectivity of dynamic click chemistry, super‐lubricating surfaces with targeted self‐repairability are developed through a surface‐recognized strategy. The rubbing surfaces exhibit superlubricity with friction coefficient µ ≈0.002 at physiologically high pressure (≈7.5 MPa). When wear‐induced coating‐substrate breakage occurs, the lubricating materials can target and reassociate with their pairing surfaces through specific dynamic covalent linkages, circumventing surface bridging, and recovering high lubricity even upon repeated damage. This study offers an innovative paradigm for developing durable lubricating surfaces with bespoke reparability for biomedical applications.
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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".