Aggregation of Titanium Dioxide Nanoparticles: Role of a Fulvic Acid
Bibliographic record
Abstract
The increasing use of nanomaterials in consumer products has led to increased concerns abouttheir potential environmental and health impacts. To better understand the transport, fate, and behavior of nanoparticles in aquatic systems, it is essential to understand their interactions with different components of natural waters including natural organic matter over a broad range of physicochemical conditions. Fluorescence correlation spectroscopy was used to determine the diffusion coefficients of TiO2 nanoparticles having a nominal size of 5 nm. The effects of a various concentrations of the Suwannee River Fulvic Acid (SRFA) and the roles of pH and ionic strength were evaluated. Aggregation of the bare TiO2 nanoparticles increased for pH values near the zero point of charge. At any given pH, an increase in ionic strength generally resulted in increased aggregation. Furthermore, conditions which favored adsorption of the SRFA resulted in less aggregation of the TiO2 nanoparticles, presumably due to increased steric repulsion. Under the conditions studied here, nanoparticle dispersions were often stable for environmentally relevant conditions of SRFA, pH, and ionic strength, suggesting that in the natural environment, TiO2 dispersion might occur to a greater extent than expected.
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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) | 0.000 | 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 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".