Influence of cold rolling on in vitro cytotoxicity and electrochemical behaviour of an Fe-Mn-C biodegradable alloy in physiological solutions
Bibliographic record
Abstract
The properties of cold-worked Fe-13Mn-1.2C steel, as candidate material for scaffolding and stenting applications, have been investigated. The study of the electrochemical corrosion susceptibility of Fe-13Mn-1.2C alloy in protein bearing and non-protein bearing physiological solutions, revealed that there were no differences between the as-received, 10% and 20% cold worked Fe-13Mn-1.2C samples. Although protein addition reduces the overall corrosion rate in static immersion degradation tests for both the cold worked and non-cold worked alloys, there were no discernible differences in the corrosion rates of samples with different percentages of cold work deformations. Similarly, potentiodynamic testing showed no differences between the corrosion rates in solutions with and without protein addition. Atomic absorption spectroscopy (AAS) results—post static immersion—showed similar values of Fe and Mn concentrations in the electrolyte for all the investigated conditions. Cold working was found to increase Grain Average Misorientation (GAM) and deformation twins within the steel, and, consequently, this led to an increase in the elastic modulus. Hence, cold-rolling may be used to achieve smaller sections (volumes) in order to support the equivalent load of the non-cold worked counterpart, giving a larger surface area to the volume ratio, thereby increasing the corrosion rate, and, in turn, rendering the degradation process shorter. When considering cytocompatibility in vitro , the collected supernatant particulate free Fe-13Mn-1.2C steel electrolytes were seen to be equally cytocompatible with no differences being observed between the different percentage cold work conditions. The presence of solid 80 μm size particles in the seeded elutions were seen to change the results and render the Fe-13Mn-1.2C steel non-cytocompatible.
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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".