The use of multiple pseudo-physiological solutions to simulate the degradation behavior of pure iron as a metallic resorbable implant: a surface-characterization study
Bibliographic record
Abstract
Understanding the interactions of a pure iron surface with biological elements, such as ions and proteins in an aqueous medium, is essential for an accurate in vitro assessment of corrosion patterns. In fact, the synergy of chlorides, carbonates, phosphates and complex organic molecules present in the body environment is a key factor affecting both in vivo and in vitro degradation of materials, especially iron and its alloys. The aim of this work was the assessment of degradation patterns of pure iron in 5 commercial pseudo-physiological solutions by a thorough study of degraded surface chemistry and morphology. It also provides a methodological basis to understand the short-term degradation mechanism of degradable iron depending on the surrounding physiological media. The standard static immersion corrosion test was modified to adapt the procedure to pseudo-physiological solutions. After a 14-day static immersion test, the surfaces of samples were investigated by scanning electron microscopy, stylus profilometry and atomic force microscopy techniques. The chemistry and phase composition of the degraded layers were evaluated, respectively, by X-ray photoelectron spectrometry and X-ray diffractometry. The morphology and composition of the degradation layers were found to be different for the test-solutions: for phosphate-rich solutions, the formation of an adherent passive layer was found; degradation mechanisms related to general corrosion were predominant for all the other solutions. In conclusion, the chemical composition of the used medium plays a fundamental role in the degradation pattern of pure iron, so that direct comparisons of solutions with different ion concentrations, as reported in the literature, need to be carefully assessed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".