Antimicrobial Properties of Graphene Oxide Nanosheets: Why Size Matters
Bibliographic record
Abstract
Graphene oxide (GO) is a promising material for the development of antimicrobial surfaces due to its contact-based antimicrobial activity. However, the relationship between GO physicochemical properties and its antimicrobial activity has yet to be elucidated. In this study, we investigated the size-dependency of GO antimicrobial activity using the Gram-negative bacteria Escherichia coli. GO suspensions of average sheet area ranging from 0.01 to 0.65 μm(2) were produced and their antimicrobial activity evaluated in cell suspensions or as a model GO surface coating. The antimicrobial activity of GO surface coatings increased 4-fold when GO sheet area decreased from 0.65 to 0.01 μm(2). The higher antimicrobial effect of smaller GO sheets is attributed to oxidative mechanisms associated with the higher defect density of smaller sheets. In contrast, in suspension assays, GO interacted with bacteria in a cell entrapment mechanism; in this case, the antimicrobial effect of GO increased with increasing sheet area, with apparent complete inactivation observed for the 0.65 μm(2) sheets after a 3 h exposure. However, cell inactivation by GO entrapment was reversible and all initially viable cells could be recovered when separated from GO sheets by sonication. These findings provide useful guidelines for future development of graphene-based antimicrobial surface coatings, where smaller sheet sizes can increase the antimicrobial activity of the material. Our study further emphasizes the importance of an accurate assessment of the antimicrobial effect of nanomaterials when used for antimicrobial surface design.
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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 | 0.000 | 0.001 |
| 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".