Assessing the Antimicrobial Activity of Polyisoprene Based Surfaces
Bibliographic record
Abstract
There has been an intense research effort in the last decades in the field of biofouling prevention as it concerns many aspects of everyday life and causes problems to devices, the environment, and human health. Many different antifouling and antimicrobial materials have been developed to struggle against bacteria and other micro- and macro-organism attachment to different surfaces. However the "miracle solution" has still to be found. The research presented here concerns the synthesis of bio-based polymeric materials and the biological tests that showed their antifouling and, at the same time, antibacterial activity. The raw material used for the coating synthesis was natural rubber. The polyisoprene chains were fragmented to obtain oligomers, which had reactive chemical groups at their chain ends, therefore they could be modified to insert polymerizable and biocidal groups. Films were obtained by radical photopolymerization of the natural rubber derived oligomers and their structure was altered, in order to understand the mechanism of attachment inhibition and to increase the efficiency of the anti-biofouling action. The adhesion of three species of pathogenic bacteria and six strains of marine bacteria was studied. The coatings were able to inhibit bacterial attachment by contact, as it was verified that no detectable leaching of toxic molecules occurred.
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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".