Capillary Electrophoresis Analysis of Metal/Metalloid Oxide Nanoparticles in Water: Method Development for the Enhancement of UV Detection Sensitivity
Bibliographic record
Abstract
Increasing production and applications of metal/metalloid oxide nanoparticles (NPs) have greatly raised the demand for new analytical techniques capable for trace quantification in water to assess their environmental impacts and health risks.A new analytical method was developed for the sensitive detection of silica (SiO2), titania (TiO2) and zinc oxide (ZnO) as model metal/metalloid oxide NPs.This method was based on the formation of molecular layers and polymeric coatings on the NPs directly in water to selectively add chromophores to their surface for enhanced ultraviolet (UV) light absorption in capillary electrophoresis (CE) analysis.One unique advantage is the ability to identify nanoparticles by observing a stronger peak and/or a shifted migration time. Controlled polymerization of 2-hydroxypropyl methacrylate (HPMA) on SiO2NPs added a coating of poly-2-hydroxypropyl methacrylate (PHPMA) that increased their UV detection sensitivity by 6±1 folds initially.A second coating with polydopamine produced a larger size of PHPMA-SiO2 NPs, as confirmed by dynamic light scattering (DLS) and transmission electron microscopy, further enhancing their UV detection sensitivity by 12±2 folds.Chitosan coating and HPMA binding on SiO2 NPs produced a significant enhancement of UV detection sensitivity by 50±1 folds.This method was selective for SiO2 in the presence of TiO2 NPs in 10 mM Na2HPO4, a background electrolyte used for CE analysis.Selective enhancement of UV detection sensitivity of TiO2 in the presence of alumina (Al2O3), SiO2, and ZnO NPs in 100 mM Tris was achieved using deoxyribonucleic acid (DNA) and polyethylene glycol (PEG).Single-stranded DNA (ssDNA) exhibited better performance than double-stranded DNA in enhancing the v
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".