Study of the effect of magnetic fields on static degradation of Fe and Fe-12Mn-1.2C in balanced salts modified Hanks’ solution
Bibliographic record
Abstract
Iron and its alloys are attractive as biodegradable materials because of their low toxicity and suitable mechanical properties; however, they generally have a slow degradation rate. Given that corrosion is an electrochemical phenomenon where an exchange of electrons takes place, the application of magnetic fields from outside the body may accelerate the degradation of a ferrous temporary implant. In the present study, we have investigated the effect of alternating and direct low magnetic field (H = 6.5 kA/m) on the corrosion process of pure iron (Fe) and an iron-manganese alloy (FeMnC) in modified Hanks' solution. A 14-day static immersion test was performed on the materials. The corrosion rate was assessed by mass and cross-sectional loss measurements, scanning electron microscopy, X-ray diffractometry, Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy before and after degradation. The results show that the presence of magnetic fields significantly accelerates the degradation rate of both materials, with the corrosion rate being twice as high in the case of Fe and almost three times as high for FeMnC. In addition, a homogenous degradation layer is formed over the entire surface and the chemical composition of the degradation products is the same regardless of the presence of a magnetic field.
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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".