Antimicrobial Electrospun Biopolymer Nanofiber Mats Functionalized with Graphene Oxide–Silver Nanocomposites
Bibliographic record
Abstract
Functionalization of electrospun mats with antimicrobial nanomaterials is an attractive strategy to develop polymer coating materials to prevent bacterial colonization on surfaces. In this study we demonstrated a feasible approach to produce antimicrobial electrospun mats through a postfabrication binding of graphene-based nanocomposites to the nanofibers' surface. A mixture of poly(lactide-co-glycolide) (PLGA) and chitosan was electrospun to yield cylindrical and narrow-diameter (356 nm) polymeric fibers. To achieve a robust antimicrobial property, the PLGA-chitosan mats were functionalized with graphene oxide decorated with silver nanoparticles (GO-Ag) via a chemical reaction between the carboxyl groups of graphene and the primary amine functional groups on the PLGA-chitosan fibers using 3-(dimethylamino)propyl-N'-ethylcarbodiimide hydrochloride and N-hydroxysuccinimide as cross-linking agents. The attachment of GO-Ag sheets to the surface of PLGA-chitosan fibers was successfully revealed by scanning and transmission electron images. Upon direct contact with bacterial cells, the PLGA-chitosan mats functionalized with GO-Ag nanocomposites were able to effectively inactivate both Gram-negative (Escherichia coli and Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. Our results suggest that covalent binding of GO-Ag nanocomposites to the surface of PLGA-chitosan mats opens up new opportunities for the production of cost-effective, scalable, and biodegradable coating materials with the ability to hinder microbial proliferation on solid surfaces.
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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) | 0.001 | 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".