Surface activation of graphene oxide nanosheets by ultraviolet irradiation for highly efficient anti-bacterials
Bibliographic record
Abstract
A comprehensive investigation of anti-bacterial properties of graphene oxide (GO) and ultraviolet (UV) irradiated GO nanosheets was carried out. Microscopic characterization revealed that the GO nanosheet-like structures had wavy features and wrinkles or thin grooves. Fundamental surface chemical states of GO nanosheets (before and after UV irradiation) were investigated using x-ray photoelectron spectroscopy and ultraviolet photoelectron spectroscopy. Minimum inhibitory concentration (MIC) results revealed that UV irradiated GO nanosheets have more pronounced anti-bacterial behavior than GO nanosheets and standard antibiotic, kanamycin. The MIC of UV irradiated GO nanosheets was 0.125 μg ml⁻¹ for Escherichia coli and Salmonella typhimurium, 0.25 μg ml⁻¹ for Bacillus subtilis and 0.5 μg ml⁻¹ for Enterococcus faecalis, ensuring its potential as an anti-infective agent for controlling the growth of pathogenic bacteria. The minimum bactericidal concentration of normal GO nanosheets was determined to be two-fold higher than its corresponding MIC value, indicating promising bactericidal activity. The mechanism of anti-bacterial action was evaluated by measuring the enzymatic activity of β-D-galactosidase for the hydrolysis of o-nitrophenol-β-D-galactopyranoside.
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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".