Systematic toxicity investigation of graphene oxide: evaluation of assay selection, cell type, exposure period and flake size
Bibliographic record
Abstract
Understanding the toxicity of nanomaterials is essential for the safe and sustainable development of new applications. This is particularly true for a nanomaterial as widely used as graphene oxide (GO), which is utilized as films for electronics, membranes for filtration, drug carriers and more. Despite this, the current literature presents conflicting results on the overall toxicity of GO. Here, the cytotoxicity of three sizes of commercially available GO was investigated on six cell lines, as values of NOAEL/LOAEL. The effectiveness of four viability assays was also evaluated. The overall toxicity of GO greatly varied between cell lines; the suspension cells showed a greater response to the GO treatment compared to the adherent cell lines. Time dependent cytotoxicity was also cell line dependent, with only one cell line demonstrating obvious dependence. The six cell lines were also tested to evaluate their response to varying GO flake sizes: the suspension/phagocytic cells showed little variation in viability, while a difference was observed for the adherent/non-phagocytic cell lines. By systematically studying the effect of dose, GO size and treatment time for the six cell lines by using commercially available GO samples, we eliminate many of the variables which may result in the conflicting reports on the cytotoxicity of GO in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".