Chemical and Toxicological Evolution of Carbon Nanotubes During Atmospherically Relevant Aging Processes
Bibliographic record
Abstract
The toxicity of carbon nanotubes (CNTs) has received significant attention due to their usage in a wide range of commercial applications. While numerous studies exist on their impacts in water and soil ecosystems, there is a lack of information on the exposure to CNTs from the atmosphere. The transformation of CNTs in the atmosphere, resulting in their functionalization, may significantly alter their toxicity. In the current study, the chemical modification of single wall carbon nanotubes (SWCNTs) via ozone and OH radical oxidation is investigated through studies that simulate a range of expected tropospheric particulate matter (PM) lifetimes, in order to link their chemical evolution to toxicological changes. The results indicate that the oxidation favors carboxylic acid functionalization, but significantly less than other studies performed under nonatmospheric conditions. Despite evidence of functionalization, neither O3 nor OH radical oxidation resulted in a change in redox activity (potentially giving rise to oxidative stress) or in cytotoxic end points. Conversely, both the redox activity and cytotoxicity of SWCNTs significantly decreased when exposed to ambient urban air, likely due to the adsorption of organic carbon vapors. These results suggest that the effect of gas-particle partitioning of organics in the atmosphere on the toxicity of SWCNTs should be investigated further.
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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) | 0.000 | 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 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".