Effect of Amino Acids on the Corrosion and Metal Release from Copper and Stainless Steel
Bibliographic record
Abstract
Copper (Cu) and stainless steel 316 L are widely used for biomedical applications, such as intrauterine devices and orthopedic/dental implants. Amino acids are abundantly present in biological environments. We investigated the influence of select amino acids on the corrosion of Cu under naturally aerated and deaerated conditions using a phosphate-free buffer. Amino acids increased the corrosion of Cu under both aeration conditions at pH 7.4. Cu release was also significantly (up to 18-fold) increased in the presence of amino acids, investigated at pH 7.4 and 37 °C for 24 h under naturally aerated conditions. Speciation modelling predicted a generally increased solubility of Cu in the presence of amino acids at pH 7.4. 316 L, investigated for metal release under similar conditions for comparison, released about 1,000-fold lower amounts of metals than did Cu and remained passive with no change in surface oxide composition or thickness. However, amino acids also increased the chromium release (up to 52-fold), significantly for lysine, and the iron release for cysteine, while nickel and molybdenum release remained unaffected. This was not predicted by solution speciation modelling. The surface analysis confirmed the adsorption of amino acids on 316 L and, to a lower extent, Cu coupons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".