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 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".