Heteroagglomeration of nanosilver with colloidal SiO2 and clay
Bibliographic record
Abstract
Environmental context The fate of nanomaterials in the environment is related to their colloidal stability. Although numerous studies have examined their homoagglomeration, their low concentration and the presence of high concentrations of natural particles implies that heteroagglomeration rather than homoagglomeration is likely to occur under natural conditions. In this paper, two state-of-the art analytical techniques were used to identify the conditions under which nanosilver was most likely to form heteroagglomerates in natural waters. Abstract The environmental risk of nanomaterials will depend on their persistence, mobility, toxicity and bioaccumulation. Each of these parameters is related to their fate (especially dissolution, agglomeration). The goal of this paper was to understand the heteroagglomeration of silver nanoparticles in natural waters. Two small silver nanoparticles (nAg, ~3 nm; polyacrylic acid- and citrate-stabilised) were covalently labelled with a fluorescent dye and then mixed with colloidal silicon oxides (SiO2, ~18.5 nm) or clays (~550 nm SWy-2 montmorillonite). Homo- and heteroagglomeration of the nAg were first studied in controlled synthetic waters that were representative of natural fresh waters (50 µg Ag L–1; pH 7.0; ionic strength 10–7 to 10–1 M Ca) by following the sizes of the nAg by fluorescence correlation spectroscopy. The polyacrylic acid-coated nanosilver was extremely stable under all conditions, including in the presence of other colloids and at high ionic strengths. However, the citrate-coated nanosilver formed heteroaggregates in presence of both colloidal SiO2 and clay particles. Nanoparticle surface properties appeared to play a key role in controlling the physicochemical stability of the nAg. For example, the polyacrylic acid stabilized nAg-remained extremely stable in the water column, even under conditions for which surrounding colloidal particles were agglomerating. Finally, enhanced dark-field microscopy was then used to further characterise the heteroagglomeration of a citrate-coated nAg with suspensions of colloidal clay, colloidal SiO2 or natural (river) water.
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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) | 0.001 | 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 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".