A peptide-based biological coating for enhanced corrosion resistance of titanium alloy biomaterials in chloride-containing fluids
Bibliographic record
Abstract
Titanium alloys are common materials in the manufacturing of dental and orthopedic implants. Although these materials exhibit excellent biocompatibility, corrosion in response to biological fluids can impact prosthesis performance and longevity. In this work, a PEGylated metal binding peptide (D-K122-4-PEG), derived from bacteria Pseudomonas aeruginosa, was applied on a titanium (Ti) alloy, and the corrosion resistance of the coated alloy specimen was investigated in simulated chloride-containing physiological fluids by electrochemical impedance spectroscopy and micro-electrochemical measurements, surface characterization, and biocompatibility testing. Compared to uncoated specimen, the D-K122-4-PEG-coated Ti alloy demonstrates decreased corrosion current density without affecting the natural passivity. Morphological analysis using atomic force microscopy and scanning electron microscopy confirms a reduction in surface roughness of the coated specimens in the fluids. The D-K122-4-PEG does not affect the binding of HEK-293T cells to the surface of unpolished Ti alloy, nor does it increase the leukocyte activation properties of the metal. D-K122-4-PEG represents a promising coating to enhance the corrosion resistance of Ti alloys in physiological fluids, while maintaining an excellent biocompatibility.
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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) | 0.000 | 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 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".