Sustainable Conversion of Agriculture and Food Waste into Activated Carbons Devoted to Fluoride Removal from Drinking Water in Senegal
Bibliographic record
Abstract
The objective of this study was to investigate the production of activated carbons (AC) from cashew shells, and millet stalks and their efficiency in fluoride retention. These agricultural residues are collected from Senegal. It is known that some regions of Sénégal, commonly called the groundnut basin, are affected by a public health problem caused by an excess of fluoride in drinking water used by these populations. The activated carbons were produced by a combined pyrolysis and activation with water steam; no other chemical compounds were added. Then, activated carbonaceous materials obtained from cashew shells and millet stalks were called CS-H2O and MS-H2O respectively. CS-H2O and MS-H2O show very good adsorbent features, and present carbon content ranges between 71 % and 86 %. The BET surface areas are 942 m² g-1 and 1234 m².g-1 for CS-H2O and MS-H2O respectively. A third activated carbon produced from food wastes and coagulation-flocculation sludge (FW/CFS-H2O) was produced in the same conditions. Carbon and calcium content of FW/CFS-H2O are 32.6 and 39.3 % respectively. The kinetics sorption were performed with all these activated carbons, then the pseudo-first equation was used to describe the kinetics sorption. Fluoride adsorption isotherms were performed with synthetic and natural water with the best activated carbon from kinetics sorption, Langmuir and Freundlich models were used to describe the experimental data. The results showed that carbonaceous materials obtained from CS-H2O and MS-H2O were weakly efficient for fluoride removal. With FW/CFS-H2O, the adsorption capacity is 28.48 mg.g-1 with r² = 0.99 with synthetic water.
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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 | 0.001 | 0.001 |
| 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".