Surface nanocrystallization of stainless steel for reduced biofilm adherence
Bibliographic record
Abstract
Stainless steel is one of the most common metallic biomedical materials. For medical applications, its resistance to the adherence of biofilms is of importance to the elimination or minimization of bacterial infections. In this study, we demonstrate the effectiveness of a process combining surface nanocrystallization and thermal oxidation (or a recovery heat treatment in air) for reducing the biofilm's adherence to stainless steel. During this treatment, a target surface was sandblasted and the resultant dislocation cells in the surface layer were turned into nanosized grains by a subsequent recovery treatment in air. This process generated a more protective oxide film that blocked the electron exchange or reduced the surface activity more effectively. As a result, the biofilm's adherence to the treated surface was markedly minimized. A synthetic peptide was utilized as a substitute of biofilms to evaluate the adhesion between a treated steel surface and biofilms using an atomic force microscope (AFM) through measuring the adhesive force between the target surface and a peptide-coated AFM tip. It was shown that the adhesive force decreased with a decrease in the grain size of the steel. The corresponding surface electron work function (EWF) of the steel was also measured, which showed a trend of variation in EWF with the grain size, consistent with corresponding changes in the adhesive force.
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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) | 0.001 | 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".