Dissolution behavior of metal oxide nanomaterials in cell culture medium versus distilled water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 TiO 2 ENMs 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 > TiO 2 in 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-TiO 2 were the opposite of those displayed by nano-CuO and nano-NiO. In DMEM, nano-TiO 2 displayed decreased solubility at the higher initial concentration (0.3-fold), whereas in water, nano-TiO 2 displayed 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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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 | 0.002 | 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 it