Rapid Removal of Fluoride from Water Using Core@shell and @Shell Nanoparticles of SiO2@ZrO2 and @ZrO2. Investigation of the Mechanisms Involved and Impact of Elemental Leaching
Bibliographic record
Abstract
<p>Fluoride is a natural contaminant of water that endangers many people worldwide when present in concentrations higher than 2 ppm. Here, fluoride removal by four different nanostructured colloidal particles (SiO<sub>2</sub>@ZrO<sub>2</sub><sup>nc</sup>, SiO<sub>2</sub>@ZrO<sub>2</sub><sup>c</sup>, @ZrO<sub>2</sub><sup>nc</sup>, and @ZrO<sub>2</sub><sup>c</sup>) was measured in batch systems within a period of 24 h. Surprisingly, these materials removed fluoride from the water solutions and reached equilibrium in less than 10 minutes. The combination of high specific surface and fast fluoride removal placed these materials among the top materials currently known in fluoride removal. Also, the impact of element leaching was measured and quantified. The influence of time, pH, and fluoride concentration on leaching of Zr and Si was evaluated with a response surface methodology. Leaching of Zr and Si continued for several hours and depended on first-order, quadratic and cross-product coefficients. Previous studies of fluoride removal with zirconium oxide often assumed that a decrease in fluoride concentration in the solution indicated that fluoride was bound to the surface of the oxide. Zirconium oxide's solubility in water is low, but not zero. Hence, Zr might have formed soluble fluorocomplexes. This is the first report of fluoride removal with zirconium oxide that studied the leaching of the solid to exclude the formation of soluble fluorocomplexes.</p>
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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".