Degradation behavior of austenite, ferrite, and martensite present in biodegradable Fe-based alloys in three protein-rich pseudo-physiological solutions
Bibliographic record
Abstract
This study investigates the degradation behavior of three distinct Fe-based alloys immersed in three pseudo-physiological solutions. These alloys, which have varied Mn and C contents, include a commercially available Fe-0.15C alloy, namely Fe–C, and two newly developed alloys, that is Fe–5Mn-0.2C (namely Fe–5Mn) and Fe–18Mn-0.6C (namely Fe–18Mn). The aim was to understand the effect of alloying elements and the testing solution on the in - vitro degradation behavior of these Fe-based materials. Static immersion degradation and potentiodynamic corrosion tests were carried out using three pseudo-physiological solutions with albumin supply, that is modified Hanks’ saline solution (MHSS), phosphate buffered saline solution (PBS), and sodium chloride solution (NaCl). After two weeks of static immersion, the results revealed that Fe–5Mn, characterized by a mixture of ferrite and martensite, showed the highest degradation rate, while Fe–C, composed solely of ferrite, showed the lowest rate of degradation. The predominant degradation products in MHSS and PBS were phosphates and carbonates. In PBS, these products formed a remarkably stable protective layer on the surface, contributing to the lowest degradation rate. In contrast, porous hydroxides appeared as the main degradation products for samples immersed in NaCl solution, leading to the highest degradation rate. These results provided important insights into the customization of Fe–Mn–C alloys for a range of biomedical applications, meeting a variety of clinical requirements, and highlighting the considerable potential of Fe–Mn–C alloys for biomedical applications. • Fe–5Mn-0.2C showed the presence of ferrite and martensite, showing a duplex microstructure. • The static Fe–5Mn-0.2C corrosion rate was ∼0.19 mm/y in Hanks' solution and ∼0.28 mm/y in NaCl one. • Ferrite (AISI 1018) and austenite (Fe–18Mn-0.7C) showed similar static degradation rates. • Degradation products showed the presence of phosphates and carbonates, in the form of loose precipitates. • Crystallized Ca 2 P 2 O 7 was found on degraded surfaces of Fe–18Mn-0.7C immersed in Hanks' solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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 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".