In Vivo Self‐Growing Mineral Coating Enables Durable Protection on Mg Biometals for Bone Regeneration
Bibliographic record
Abstract
Abstract Magnesium represents the revolutionary biometal for bone regeneration but necessitates protective coatings to mitigate its rapid biodegradation and promote osteogenesis. However, conventional coatings inevitably deteriorate in vivo due to the mechanical damage during implant fixation surgery and continuous exposure to corrosive biofluids. Herein, a coating strategy that utilizes biofluid components to dynamically ‘self‐grow’ a mineral coating instead of deterioration, mimicking tooth enamel growth through in vivo biomineralization, is proposed to overturn the instability of coatings. A dual‐layer system, consisting of a surface‐parallel fluoroapatite (FAP)‐crystal network layer and a fluoride‐releasing MgF 2 layer, is constructed on the Mg surface to catalyze the energetically favorable conversion of Ca 2+ and PO 4 3− ions from biofluids into new FAPs via fluoride‐combined FAP template‐boosted biomineralization. This dynamically grown FAP continuously densifies the coating by sealing internal voids and autonomously adapts to the highly corrosive environment. Consequently, the self‐growing coating effectively mitigates biocorrosion, maintains magnesium substrate integrity over two months in vivo, significantly improves osteointegration and accelerates bone regeneration by enhancing osteoblast adhesion, differentiation, and fostering endogenous mineralization. This innovative strategy leverages unlimited corrosive biofluids to dynamically create an anti‐corrosion coating in vivo, revolutionizing the development of durable and bioactive coatings for implanted biomaterials.
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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".