Effects of sterilization processes on NiTi alloy: Surface characterization
Bibliographic record
Abstract
Sterilization is required for using any device in contact with the human body. Numerous authors have studied device properties after sterilization and reported on bulk and surface modifications of many materials after processing. These surface modifications may in turn influence device biocompatibility. Still, data are missing on the effect of sterilization procedures on new biomaterials such as nickel-titanium (NiTi). Herein we report on the effect of dry heat, steam autoclaving, ethylene oxide, peracetic acid, and plasma-based sterilization techniques on the surface properties of NiTi. After processing electropolished NiTi disks with these techniques, surface analyses were performed by Auger electron spectroscopy (AES), atomic force microscopy (AFM), and contact angle measurements. AES analyses revealed a higher Ni concentration (6-7 vs. 1%) and a slightly thicker oxide layer on the surface for heat and ethylene oxide processed materials. Studies of surface topography by AFM showed up to a threefold increase of the surface roughness when disks were dry heat sterilized. An increase of the surface energy of up to 100% was calculated for plasma treated surfaces. Our results point out that some surface modifications are induced by sterilization procedures. Further work is required to assess the effect of these modifications on biocompatibility, and to determine the most appropriate methods to sterilize NiTi.
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 | 0.001 |
| 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) | 0.001 | 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".