Dual‐Protection Inorganic‐Protein Coating on Mg‐Based Biomaterials through Tooth‐Enamel‐Inspired Biomineralization
Bibliographic record
Abstract
Abstract Biocompatible magnesium alloys represent revolutionary implantable materials in dentistry and orthopedics but face challenges due to rapid biocorrosion, necessitating protective coatings to mitigate dysfunction. Directly integrating durable protective coatings onto Mg surfaces is challenging because of intrinsic low coating compactness. Herein, inspired by tooth enamel, a novel highly compact dual‐protection inorganic‐protein (inorganic Pro ) coating is in situ constructed on Mg surfaces through bovine serum albumin (BSA) protein‐boosted reaction between sodium fluoride (NaF) and Mg substrates. The association of Mg ions and BSA establishes a local hydrophobic domain that lowers the formation enthalpy of NaMgF 3 nanoparticles. This process generates finer nanoparticles that function as “bricks,” facilitating denser packing, consequently reducing voidage inside coatings by over 50% and reinforcing mechanical durability. Moreover, the incorporation of BSA in and on the coatings plays two synergistic roles: 1) acting as “mortar” to seal residual cracks within coatings, thereby promoting coating compactness and tripling anticorrosion performance, and 2) mitigating fouling‐accelerated biocorrosion in complex biosystems via tenfold resistance against biofoulant attachments, including biofluids, proteins, and metabolites. This innovative strategy, leveraging proteins to alter inorganic reactions, benefits the future coating design for Mg‐based and other metallic materials with tailored anticorrosion and antifouling performances.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".