Influence of natural organic matter on the transformation of metal and metal oxide nanoparticles and their ecotoxic potency in vitro
Bibliographic record
Abstract
Increased use and production of engineered nanoparticles (NPs) lead to an elevated risk of their diffuse dispersion into the aquatic environment and increased concern on unknown effects induced by their release into the aquatic ecosystem. An improved understanding of the environmental transformation processes of NPs of various surface characteristics is hence imperative for risk assessment and management. This study presents results on effects of natural organic matter (NOM) on the environmental transformation and dissolution of metal and metal oxide NPs of different surface and solubility properties in synthetic freshwater (FW) with and without NOM. Adsorption of NOM was evident on most of the studied NPs, except Sb and Sb2O3, which resulted in the formation of negatively charged colloids of higher stability and smaller size distribution compared with the same NPs in FW only. The dissolution rate of the NPs in the presence of NOM correlated with the strength of interactions between the carboxylate group of NOM and the particle surface, and resulted in either no (Mn, Sb, ZnO NPs), increased (Co, Sn NPs) and decreased (Ni, NiO, Sb2O3, Y2O3 NPs) levels of dissolution. One type of metal NP from each group (Mn, Ni, Sn) were investigated to assess whether observed differences in adsorption of NOM and dissolution would influence their ecotoxic potency. The results showed Mn, Ni, and Sn NPs to generate intracellular reactive oxygen species (ROS) in a time and dose-dependent manner. The extent of ROS generation in FW was similar for both Mn and Ni NPs but higher for Sn NPs. These findings are possibly related to interactions and infiltration of the NPs with the cells, which lead to redox imbalances which could induce oxidative stress and cell damage. At the same time, the presence of NOM generally reduced the intracellular ROS generation by 20–40% for the investigated NPs and also reduced cytotoxicity of Sn NPs, which can be attributed to the stronger interaction of carboxylate groups of NOM with the surface of the NPs.
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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 | 0.001 | 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".