Antibacterial properties of nine pure metals: a laboratory study using<i>Staphylococcus aureus</i>and<i>Escherichia coli</i>
Bibliographic record
Abstract
Bacterial attachment and growth on material surfaces are considered to be the primary steps leading to the formation of biofilm. Biofilms in hospital and food processing settings can result in bacterial infection and food contamination, respectively. Prevention of bacterial attachment, therefore, is considered to be the best strategy for abating these menaces and therefore the development of antibacterial metals becomes important. In this study, nine pure metals, viz. titanium, cobalt, nickel, copper, zinc, zirconium, molybdenum, tin, and lead have been tested for their antibacterial properties against two bacterial strains, Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli. This was accomplished using two assay methods, the film contact method and the shaking flask method. The results show that the antibacterial properties varied significantly with different metals and the effectiveness of metals to resist bacterial attachment varied with the bacterial strain. Among the metals tested, titanium and tin did not exhibit antibacterial properties. TEM images showed that metal accumulation resulted in the disruption of the bacterial cell wall and other cellular components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".