Amine-functionalized magnetic bio-nanocomposite for fluoride and chromium removal in water
Why this work is in the frame
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Bibliographic record
Abstract
Hexavalent chromium (Cr(VI)) and fluoride (F − ) are hazardous anionic pollutants, frequently present in industrial effluents and naturally occurring in groundwater within Ethiopia's Rift Valley, respectively. These contaminants pose significant health and ecological threats. In this study, a novel and environmentally friendly nanocomposite adsorbent was developed to effectively remove Cr(VI) and fluoride from aqueous systems. The synthesized material—a coffee husk extract (CHE)-capped, amine-functionalized magnetite–pumice–magnesium silica nanocomposite (Fe₃O₄/PU/Mg@SiO₂-NH₂-NC)—incorporates multiple green synthesis strategies: silica nanoparticles derived from bagasse-extracted sodium silicate and capped with CHE, the inclusion of CHE-stabilized MgO nanoparticles, and surface amination to improve the affinity for negatively charged species. To the best of our knowledge, this is the first study to integrate these components into a unified nanocomposite system specifically designed for targeted water treatement. The composite exhibited excellent adsorption capacity, achieving removal efficiencies of 92 % for fluoride (at 4 g·L −1 ) and 86 % for Cr(VI) (at 10 g·L −1 ), from initial concentrations of 5 mg·L −1 and 30 mg·L −1 , respectively. The adsorption process followed pseudo-second-order kinetics and aligned well with the Langmuir isotherm model, yielding maximum adsorption capacities of 14.79 mg·g −1 for fluoride and 66.8 mg·g −1 for Cr(VI). Furthermore, the nanocomposite demonstrated excellent regeneration potential, retaining over 82 % of its original adsorption capacity after five cycles. These findings highlight the promise of CHE-capped Fe₃O₄/PU/Mg@SiO₂-NH₂-NC as a highly effective and sustainable adsorbent for the removal of fluoride and Cr(VI) from contaminated water.
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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) | 0.000 | 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) | 0.000 | 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