Natural Organic Matter Concentration and Hydrochemistry Influence Aggregation Kinetics of Functionalized Engineered Nanoparticles
Bibliographic record
Abstract
Understanding the colloidal stability of functionalized engineered nanoparticles (FENPs) in aquatic environments is of paramount importance in order to assess the risk related to FENPs. In this study, gold nanoparticles (GNPs) of 68 and 43 nm diameter, coated with citrate and 11-mercaptoundecanoic acid (MUA) respectively, were used as models of FENPs. Time-resolved dynamic light scattering was employed to investigate the aggregation kinetics of two types of GNPs. The results show that without Suwannee river natural organic matter (SRNOM), MUA coating resulted in greater stability than citrate coating for GNPs. Cations have a destabilizing effect on both GNPs following the order Ca(2+) ≈ Mg(2+) >> Na(+); different anions (Cl(-) and SO4(2-)) showed no difference in effects. In the fast aggregation regime, adding SRNOM enhanced the stability of MUA-coated GNPs in both Ca(2+) and Mg(2+) solutions. However citrate-coated GNPs were only stabilized in Mg(2+) solution but enhanced aggregation occurred in high Ca(2+) concentration due to interparticle bridging. For the investigated GNPs and in the presence of SRNOM, Ca(2+) does not always act as a strong coagulant. This indicates that for the new materials emerging from the application of nanotechnology the well-described aggregation mechanisms of colloids in the environment require a detailed re-examination.
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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 | 0.001 |
| 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".