The Influence of Paper Surface Chemistry on Bacteriophage Activity
Bibliographic record
Abstract
Bacteriophages are promising biosensing systems in bioactive paper application due to their specific detection of bacteria. Different chemicals including wet strength resins were used to improve paper properties. This work investigated the influence of wet strength resins (PAE and PVAm) on bacteriophage activity, and proposed another method of using Poly NIP AM microgel to separate bacteriophage from paper surface. Compared with filter paper, the cationic polymer PAE and PVAm treated paper exhibited high phage binding efficiency but low phage activity due to the electrostatic interaction. PVAm had strong phage adsorption and almost completely deactivated the phage particle. Streptavidin was coupled to PolyNIPAM microgel in the presence of EDC, and T4 bacteriophage genetically modified with biotin was immobilized to microgel particle which resulted in a 10-fold improvement in attachment when compared with T4 wild-type phage. The microgel-phage coupling efficiency was very low, there were more than 10^6 micro gel particle for every active phage. And micro gel supported phages were deactivated after coating on the PAE/PVAm treated paper.
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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 | 0.001 |
| 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 | 0.001 | 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".