Comparative Study of Physicochemical Properties and Antibiofilm Activity of Graphene Oxide Nanoribbons
Bibliographic record
Abstract
In this article, the antibiofilm activity and physicochemical properties of graphene oxide (GO) nanoribbons, which have been among the most exciting materials, were studied by measuring the ratio of killed to alive bacteria incubated with these nanomaterials. Our objective was to determine the related physicochemical and antibiofilm properties of graphene oxide nanoribbons. We hypothesized that the physicochemical properties of graphene oxide nanoribbons could affect their antibiofilm activity. A combination of spectroscopic and microscopic measurements of the samples allowed us to determine their physicochemical properties affecting the biofilms. Our work includes information on the surface properties of these materials related to their incubation with the biofilms. The Fourier transform infrared spectroscopy showed the vibrations of OH groups of water molecules adsorbed on graphene oxide nanoribbons. The results show the high antibiofilm activity of the graphene oxide nanoribbons. The fluorescence confocal microscopy revealed that 50 % ± 3 % of the total number of bacteria were killed with these nanomaterials. The incubation of graphene oxide nanoribbons with bacterial biofilms resulted in the appearance of the NO2–, NO3– peaks in the negative mode mass spectrum. The attenuation of the O– and OH– peaks were attributed to the interactions of the samples with the biofilms. Our study gives more evidence of the practical value of graphene oxide nanoribbons in killing bacteria related to their surface physical properties and the potential of these nanomaterials for materials science and biomedical applications. Keywords: nanomaterials, bacterial biofilm, Fourier transform, infrared spectroscopy, transmission electron microscopy, time-of-flight secondary ion mass spectrometry, confocal microscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".