Characterization of Polymeric Nanomaterials Using Analytical Ultracentrifugation
Bibliographic record
Abstract
The characterization of nanomaterials represents a complex analytical challenge due to their dynamic nature (small size, high reactivity, and instability) and the low concentrations in the environment, often below typical analytical detection limits. Analytical ultracentrifugation (AUC) is especially useful for the characterization of small nanoparticles (1-10 nm), which are often the most problematic for the commonly used techniques such as electron microscopy or dynamic light scattering. In this study, small polymeric nanomaterials (allospheres) that are used commercially to facilitate the distribution of pesticides in agricultural fields were characterized under a number of environmentally relevant conditions. Under most of the studied conditions, the allospheres were shown to have a constant hydrodynamic diameter (dH) of about 7.0 nm. Only small increases in diameter were observed, either at low pH or very high ionic strength or hardness, demonstrating their high physicochemical stability (and thus high mobility in soils). Furthermore, natural organic matter had little effect on the hydrodynamic diameters of the allospheres. The concentration of the nanoparticles was an important parameter influencing their agglomeration-results obtained using dynamic light scattering at high particle concentrations showed large agglomerate sizes and significant particle losses through sedimentation, clearly indicating the importance of characterizing the nanomaterials under environmentally relevant conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".