Agglomeration and dissolution of zinc oxide nanoparticles: role of pH, ionic strength and fulvic acid
Bibliographic record
Abstract
Environmental context The number of nano-enabled products reaching consumers is growing exponentially, inevitably resulting in their release to the environment. The environmental fate and mobility of nanomaterials will depend on their physicochemical form(s) under natural conditions. For ZnO nanoparticles, determinations of agglomeration and dissolution under environmentally relevant conditions of pH, ionic strength and natural organic matter content will provide insight into the potential environmental risk of these novel products. Abstract The increasing use of engineered nanoparticles (ENPs) in industrial and household applications has led to their release into the environment and increasing concern about their effects. Proper assessment of the ecological risks of ENPs will require data on their bioavailability, persistence and mobility over a broad range of physicochemical conditions, including environmentally relevant pH, ionic strength and concentrations of natural organic matter (NOM). In this study, fluorescence correlation spectroscopy was used to determine the agglomeration of a ZnO ENP (nZnO) with a nominal size of 20 nm. Particle dissolution was followed using scanned stripping chronopotentiometry. The effects of Suwannee River fulvic acid (SRFA, 0–60 mg L–1) and the roles of pH (4–10) and ionic strength (0.005–0.1 M) were carefully evaluated. Agglomeration of the bare nZnO increased for pH values near the zero point of charge, whereas the dissolution of the particles decreased. At any given pH, an increase in ionic strength generally resulted in a less stable colloidal system. The role of SRFA was highly dependent upon its concentration with increased agglomeration observed at low SRFA : nZnO mass ratios and decreased agglomeration observed at higher SRFA : nZnO mass ratios. The results indicated that in natural systems, both nZnO dispersion and dissolution will be important and highly dependent upon the precise conditions of pH and ionic strength.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".