Solubility of metal oxide nanomaterials: cautionary notes on sample preparation
Bibliographic record
Abstract
Abstract Eight metal oxides were obtained to investigate the dissolution behaviour of engineered nanomaterials (ENMs) dispersed in biologically relevant media. Identities of the metal oxide compounds, and their crystal form and size were checked using powder X-ray diffraction (XRD). Methods for sonication of metal oxide nanoparticles were optimized to achieve stable stock dispersions, and methods for separation of dissolved metal ions from dispersed nanoparticles were evaluated. The results of the optimization experiments showed that each metal oxide ENM required a different combination of sonication time and power (% amplitude). Optimized values for delivered sonication energy (J/mL) ranged from 24 for CuO to 833 for ɣ-Al2O3. Centrifugation at 20000G was found to be more effective and less prone to artefacts than using commercially available ultrafiltration devices for separation of dissolved metal fraction, under these experimental conditions. XRD results indicated that the composition of two metal oxide nanopowders (Mn2O3 and Fe2O3) did not meet the manufacturers’ claims, underscoring the importance of double-checking physical-chemical properties of commercial ENMs purchased for research.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".