Dissolution behavior of metal oxide nanomaterials in cell culture medium versus distilled water
Bibliographic record
Abstract
Abstract Solubility is a key criterion used in the hazard assessment of metal oxide–engineered nanomaterials (ENMs). The present study investigated solubility of CuO, NiO, and TiO2ENMs compared with their bulk analogues in two aqueous media: water and Dulbecco’s modified Eagle’s medium (DMEM). Particle size distributions were characterized using dynamic light scattering (DLS) and tunable resistive pulse sensing (TRPS). After centrifugal separation, the dissolved metal fraction was quantified using inductively coupled plasma optical emission spectroscopy (ICP-OES). Overall, solubility of the metal oxides decreased in the order CuO ≥ NiO > TiO2in both media, with each ENM displaying higher solubility than its bulk analogue. However, the metal oxide ENMs responded differently to the two aqueous media, when comparing their solubility using a low initial concentration (10 mg/L) versus a high initial concentration (100 mg/L). In DMEM, both nano-CuO and nano-NiO displayed increased solubility at the higher initial concentration by 3.8-fold and 1.4-fold, respectively. In water, this trend was reversed, with both nano-CuO and nano-NiO displaying increased solubility at the lower initial concentration by 3.3-fold and 1.2-fold, respectively. Interestingly, solubility trends displayed by nano-TiO2were the opposite of those displayed by nano-CuO and nano-NiO. In DMEM, nano-TiO2displayed decreased solubility at the higher initial concentration (0.3-fold), whereas in water, nano-TiO2displayed increased solubility at the higher initial concentration (5.5-fold). These results show the importance of evaluating the solubility of ENMs in biologically relevant fluids at concentrations that correspond to toxicity assays, for the purposes of read-across and grouping ENMs.
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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) | 0.001 | 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".