Dissolution Behaviour of Metal-Oxide Nanomaterials in Various Biological Media
Bibliographic record
Abstract
Toxicological effects of metal-oxide-engineered nanomaterials (ENMs) are closely related to their distinct physical–chemical properties, especially solubility and surface reactivity. The present study used five metal-oxide ENMs (ZnO, MnO2, CeO2, Al2O3, and Fe2O3) to investigate how various biologically relevant media influenced dissolution behaviour. In both water and cell culture medium (DMEM), the metal-oxide ENMs were more soluble than their bulk analogues, with the exception that bulk-MnO2 was slightly more soluble in water than nano-MnO2 and Fe2O3 displayed negligible solubility across all tested media (regardless of particle size). Lowering the initial concentration (10 mg/L vs. 100 mg/L) significantly increased the relative solubility (% of total concentration) of nano-ZnO and nano-MnO2 in both water and DMEM. Nano-Al2O3 and nano-CeO2 were impacted differently by the two media (significantly higher % solubility at 10 mg/L in DMEM vs. water). Further evaluation of simulated interstitial lung fluid (Gamble’s solution) and phagolysosomal simulant fluid (PSF) showed that the selection of aqueous media significantly affected agglomeration and dissolution behaviour. The solubility of all investigated ENMs was significantly higher in DMEM (pH = 7.4) compared to Gamble’s (pH 7.4), attributable to the presence of amino acids and proteins in DMEM. All ENMs showed low solubility in Gamble’s (pH = 7.4) compared with PSF (pH = 4.5), attributable to the difference in pH. These observations are relevant to nanotoxicology as increased nanomaterial solubility also affects toxicity. The results demonstrated that, for the purpose of grouping and read-across efforts, the dissolution behaviour of metal-oxide ENMs should be evaluated using aqueous media representative of the exposure pathway being considered.
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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".