Influence of pH, particle size and crystal form on dissolution behaviour of engineered nanomaterials
Bibliographic record
Abstract
Solubility is a critical component of physicochemical characterisation of engineered nanomaterials (ENMs) and an important parameter in their risk assessments. Standard testing methodologies are needed to estimate the dissolution behaviour and biodurability (half-life) of ENMs in biological fluids. The effect of pH, particle size and crystal form on dissolution behaviour of zinc metal, ZnO and TiO 2 was investigated using a simple 2 h solubility assay at body temperature (37 °C) and two pH conditions (1.5 and 7) to approximately frame the pH range found in human body fluids. Time series dissolution experiments were then conducted to determine rate constants and half-lives. Dissolution characteristics of investigated ENMs were compared with those of their bulk analogues for both pH conditions. Two crystal forms of TiO 2 were considered: anatase and rutile. For all compounds studied, and at both pH conditions, the short solubility assays and the time series experiments consistently showed that biodurability of the bulk analogues was equal to or greater than biodurability of the corresponding nanomaterials. The results showed that particle size and crystal form of inorganic ENMs were important properties that influenced dissolution behaviour and biodurability. All ENMs and bulk analogues displayed significantly higher solubility at low pH than at neutral pH. In the context of classification and read-across approaches, the pH of the dissolution medium was the key parameter. The main implication is that pH and temperature should be specified in solubility testing when evaluating ENM dissolution in human body fluids, even for preliminary (tier 1) screening.
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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 | 0.001 |
| 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".