Role of Niobium on the Passivation Mechanisms of TiHfZrNb High-Entropy Alloys in Hanks’ Simulated Body Fluid
Bibliographic record
Abstract
A family of TiHfZrNb high-entropy alloys has been considered novel biomaterials for high-performance, small-sized implants. The present work evaluates the role of niobium on passivation kinetics and electrochemical characteristics of passive film on TiHfZrNb alloys formed in Hanks' simulated body fluid by analyzing electrochemical data with three analytical models. Results confirm that higher niobium content in the alloys reinforces the compactness of the passive film by favoring the dominance of film formation and thickening mechanism over the dissolution mechanism. Higher niobium content enhances the passivation kinetics to rapidly form the first layer, and total surface coverage reinforces the capacitive-resistant behavior of the film by enrichment with niobium oxides and reduces the point defect density and their mobility across the film, lowering pitting initiation susceptibility. With the high resistance to dissolution and rapid repassivation ability in the aggressive Hanks' simulated body fluid, the TiHfZrNb alloys confirm their great potential as new materials for biomedical implants and warrant further biocompatibility testing.
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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) | 0.000 | 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".