Mobility of Functionalized Quantum Dots and a Model Polystyrene Nanoparticle in Saturated Quartz Sand and Loamy Sand
Bibliographic record
Abstract
Quantum dots (QDs) are one example of engineered nanoparticles (ENPs) with demonstrated toxic effects. Yet, little is known about the behavior of QDs in the natural environment. This study assessed the transport of two commercial carboxylated QDs (CdTe and CdSe) and carboxylated polystyrene latex (nPL) as a model nanoparticle using saturated laboratory-scale columns. The influence of solution ionic strength (IS) and cation type (K(+) or Ca(2+)) on the transport potential of these ENPs was examined in two granular matrices - quartz sand and loamy sand. The retention of all three particles was generally low in the quartz sand columns within the range of studied IS (0.1-100 mM) for the monovalent salt (KCl). In contrast, the retention of the three ENPs in the quartz sand was significant in the presence of 10 mM Ca(2+). Moreover, ENP attachment efficiencies (α) were enhanced by at least 1 order of magnitude in columns packed with loamy sand (for IS between 0.1-10 mM KCl). Although all three ENPs used here are carboxylated, they differ in the type of surface coating (e.g., choice of polymers or polyelectrolytes). Regardless of the surface coatings, the three ENPs exhibit comparable mobility in the quartz sand. However, the ENPs demonstrate variable transport potential in loamy sand suggesting that differences in the binding affinities of surface-modified ENPs for specific soil constituents can play a key role in the fate of ENPs in soils.
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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".