Fluoride removal from drinking water (Metlaoui, Tunisia) using untreated and treated natural clays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract BACKGROUND Fluorosis is an endemic disease due to an excess of fluoride intake via drinking water. In some regions of the world, removing fluoride from drinking water is a severe problem that is still to be solved. The present study focuses on the use of a natural clay to reduce fluoride concentration in Tunisian contaminated drinking water under relevant working conditions. RESULTS Adsorption experiments were performed in batches using a fluoride aqueous solution. The Box–Behnken model design was used to define the working conditions in which three factors were controlled: clay dosage, contact time and agitation speed. The fixed parameters were the initial fluoride concentration and water pH as observed in Metlaoui, Tunisia in 2021, and experiments were performed at room temperature. Results show that 4 g(50 mL) −1 of clay dosage, 10 min of contact time and 280 rpm of agitation speed could provide 51% fluoride removal using an untreated natural clay. Then, various adsorbents based on this clay were synthesized (chitosan–clay, C 6 H 17 NO 3 Si–clay and thermally treated clays purified using different methods) and tested using the same approach. Among the adsorbents tested, the thermally treated purified clays were the most effective in removing fluoride under ambient conditions with a fluoride removal of 97.5%. Tests performed on drinking water showed that the safety fluoride concentration could be achieved without modifications of the water pH. CONCLUSIONS The thermally treated clays investigated in this study were effective for fluoride removal under relevant conditions, which can pave the way for future field applications. © 2023 Society of Chemical Industry (SCI).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it