Tailoring H2O2 generation kinetics with magnesium alloys for efficient disinfection on titanium surface
Bibliographic record
Abstract
Abstract A new antibacterial strategy for Ti has been developed without the use of any external antibacterial agents and surface treatments. By combining Mg alloys with Ti, H2O2, which is an oxidizing agent that kills bacteria, was spontaneously generated near the surface of Ti. Importantly, the H2O2 formation kinetics can be precisely controlled by tailoring the degradation rates of Mg alloys connected to Ti. Through microstructural and electrochemical modification of Mg with alloying elements (Ca, Zn), the degradation rates of Mg alloys were controlled, and the H2O2 release kinetics was accelerated when the degradation rate of Mg alloys increased. With the introduction of an in vivo assessment platform comprised of Escherichia coli (E. coli) and transgenic zebrafish embryos, we are able to design optimized antibacterial systems (Ti-Mg and Ti-Mg-3wt% Zn) that can selectively eradicate E. coli while not harming the survival rate, development, and biological functions of zebrafish embryos. We envision that our antibacterial strategy based on utilization of sacrificial Mg alloys could broaden the current palette of antibacterial platforms for metals.
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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".