Selective Detection and Removal of Zinc Oxide Nanoparticles in Contaminated Water
Bibliographic record
Abstract
Metal oxide nanoparticles (MONPs) are massively produced for various industrial, environmental and biomedical applications due to their high surface reactivity and unique chemical and physical properties.Among all the manufactured MONPs, human exposure to zinc oxide (ZnO) nanoparticles may cause significant health concerns.This leads to a desire for efficient methods for their facile detection in the aqueous environment.In this work, two detection methods and one removal method were developed to selectively detect or remove ZnO nanoparticles in contaminated water with the presence of other MONPs.A detection method based on the fluorescence quenching of meso-tetra(4carboxyphenyl) porphyrin (TCPP) has been developed to treat water samples containing MONPs.Quenching of the TCPP emission intensity at 650 nm provides a Stern-Volmer plot with adequate sensitivity for the detection of 0.15 mg/mL ZnO nanoparticles.Meanwhile, a unique emission peak at 605 nm is observed and can be used for the identification and quantitation of ZnO nanoparticles down to 0.0015 mg/mL.A removal method using 3-aminopropyltriethoxysilane (APTES) has been proved to sediment ZnO nanoparticles in aqueous suspension with TCPP as a color indicator for ZnO.When 2.0% (v) APTES is applied to treat water samples containing ≥ 0.5 mg/mL of ZnO nanoparticles, a web-like sediment adheres onto the glass vial bottom from APTES-ZnO conjugation.A high removal efficiency of over 99% (w) ZnO nanoparticles was attained using APTES.Another detection method has been developed using electroanalytical analysis by firstly drop-casting MONPs on a screen-printed electrode.When phenol is analyzed as a iii chemical probe by cyclic voltammetry (CV), measurement of the reduction current provides adequate sensitivity for the indirect quantitation of 0.1 mg/mL ZnO nanoparticles.Both the oxidation peak and charge storage capacity measured from the cyclic voltammogram are proportional to the ZnO nanoparticle concentration and can afford a better detection limit of 0.01 mg/mL.Overall, the above methods are labour and cost effective and can afford high selectivity towards ZnO detection or removal in the presence of other MONPs.Additionally, the electrochemical analysis method and the APTES sedimentation method can differentiate ZnO nanoparticles from Zn 2+ and zinc peroxide (ZnO2) nanoparticles.
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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) | 0.001 | 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 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".