Aggregation of titanium dioxide nanoparticles: role of calcium and phosphate
Bibliographic record
Abstract
Environmental context. The increasing use of nanomaterials in consumer products has led to increased concerns about their potential environmental and health impacts. TiO2 is a widely used nanoparticle found in sunscreens and electronic products. In order to understand and predict the mobility of TiO2 in the natural environment, it is essential to determine its state of aggregation under environmentally relevant conditions of pH, ionic strength, ion and natural organic matter content. Aggregation is likely to lead to both reduced mobility and bioavailability in soils and natural waters. Abstract. The physicochemical characterisation of nanomaterials is crucial to predict their environmental and health impacts. Ion adsorption is known to influence the surface properties of nano-metal oxides in natural systems. The role of calcium and phosphate adsorption on aggregation was examined in the presence and absence of natural organic matter. Fluorescence correlation spectroscopy (FCS) was performed in order to determine the diffusion coefficients of TiO2 nanoparticles having a nominal size between 3 to 5 nm. Based upon FCS and electrophoretic mobility measurements, the presence of calcium resulted in a destabilisation and aggregation of the TiO2 nanoparticles, even in presence of Suwannee River fulvic acid (SRFA). Conditions which favoured phosphate adsorption also resulted in a destabilisation of the TiO2 sample but for low SRFA concentrations only.
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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 | 0.000 |
| 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 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".