Oxidative and Toxicological Evolution of Engineered Nanoparticles with Atmospherically Relevant Coatings
Bibliographic record
Abstract
The health impacts associated with engineered nanoparticles (ENPs) released into the atmosphere have not been adequately assessed. Such impacts could potentially arise from the toxicity associated with condensable atmospheric secondary organic material (SOM), or changes in the SOM composition induced by ENPs. Here, these possibilities are evaluated by investigating the oxidative and toxicological evolution of TiO 2 and SiO 2 nanoparticles which have been coated with SOM from the O 3 or OH initiated oxidation of α -pinene. It was found that pristine SiO 2 particles were significantly more cytotoxic compared to pristine TiO 2 particles. TiO 2 in the dark or under UV irradiation catalytically reacted with the SOM, increasing its O/C by up to 55% over photochemically inert SiO 2 while having negligible effects on the overall cytotoxicity. Conversely, the cytotoxicity associated with SiO 2 coated with SOM was markedly suppressed (by a factor of 9, at the highest exposure dose) with both increased SOM coating thickness and increased photochemical aging. These suppressing effects (organic coating and photo-oxidation of organics) were attributed to a physical hindrance of SiO 2 -cell interactions by the SOM and enhanced SOM viscosity and hydrophilicity with continued photo-oxidation, respectively. These findings highlight the importance of atmospheric processes in altering the cytotoxicity of ENPs.
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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".