Interaction of Graphene Oxide with Bacterial Cell Membranes: Insights from Force Spectroscopy
Bibliographic record
Abstract
Abstract Understanding the interactions of graphene oxide (GO) with biological membranes is crucial for the evaluation of GO?s health and environmental impacts, its bactericidal activity, and to advance graphene-based biological and environmental applications. In an effort to understand graphene-induced bacterial inactivation, we studied the interaction of GO with bacterial (Escherichia coli) cell membranes using atomic force microscopy (AFM). Toward this goal, we devised a polydopamine-assisted experimental protocol to functionalize an AFM probe with GO nanosheets, and used AFM-based force spectroscopy to measure cell membrane?GO interaction forces. Our results show that GO?cell interactions are predominantly repulsive, with only sporadic adhesion forces being measured upon probe pull-off, which we attribute to lipopolysaccharide bridging. We provide evidence of the acellular oxidation of glutathione by GO, underscoring the role of oxidative pathways in GO-mediated bacterial cell inactivation. Our force spectroscopy results suggest that physicochemical interactions do not underlie the primary mode of action of GO in bacteria.
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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 | 0.001 | 0.001 |
| 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".