Morphological and Surface Potential Characterization of Protein Nanobiofilm Formation on Magnesium Alloy Oxide: Their Role in Biodegradation
Bibliographic record
Abstract
The formation of a protein nanobiofilm on the surface of degradable biomaterials such as magnesium (Mg) and its alloys influences metal ion release, cell adhesion/spreading, and biocompatibility. During the early stage of human body implantation, competition and interaction between inorganic species and protein molecules result in a complex film containing Mg oxide and a protein layer. This film affects the electrochemical properties of the metal surface, the protein conformational arrangement, and the electronic properties of the protein/Mg oxide interface. In this study, we discuss the impact of various simulated body fluids, including sodium chloride (NaCl), phosphate-buffered saline (PBS), and Hanks' solutions on protein adsorption, electrochemical interactions, and electrical surface potential (ESP) distribution at the adsorbed protein/Mg oxide interface. After 10 min of immersion in NaCl, atomic force microscopy (AFM) and scanning Kelvin probe force microscopy (SKPFM) showed a higher surface roughness related to enhanced degradation and lower ESP distribution on a Mg-based alloy than those in other solutions. Furthermore, adding bovine serum albumin (BSA) to all solutions caused a decline in the total surface roughness and ESP magnitude on the Mg alloy surface, particularly in the NaCl electrolyte. Using SKPFM surface analysis, we detected a protein nanobiofilm (∼10-20 nm) with an aggregated and/or fibrillary morphology only on the Mg surface exposed in Hanks' and PBS solutions; these surfaces had a lower ESP value than the oxide layer.
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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 | 0.000 | 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 teacher head, 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".