Polymerization of Vinylpyrrolidone to Form a Neutral Coating on Anionic Nanomaterials in Aqueous Suspension for Rapid Sedimentation
Bibliographic record
Abstract
Nanomaterials in water present an array of identifiable potential hazards to ecological and human health. There is no general consensus about the influence of anionic or cationic charge on the toxicity of nanomaterials on environmental ecology. One challenge is the limited number of scalable technologies available for the removal of charged nanomaterials from water. A new method based on polymer coating has been developed in our laboratory for rapid sedimentation of nanomaterials in aqueous suspension. Using colloidal silica as a model inorganic oxide, coating of polyvinylpyrrolidone (PVP) around the SiO2 nanoparticles produced SiO2@PVP particles, as indicated by a linear increase of nephelometric turbidity. Purification of the water sample was afforded by total sedimentation of SiO2@PVP particles when left for 24 h. Characterization by capillary electrophoresis (CE) revealed nearly zero ionic charge on the particles. Further coating of polydopamine (PDA) around those particles in aqueous suspension produced an intense dark color due to the formation of SiO2@PVP@PDA. The SiO2@PVP@PDA peak appeared at a characteristic migration time of 4.2 min that allowed for quantitative CE-UV analysis to determine the original SiO2 concentration with enhanced sensitivity and without any ambiguous identity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".