Simultaneous Removal of Nutrients by Geopolymers Made From Industrial By-Products
Bibliographic record
Abstract
An effective way to recover phosphate and ammonium from contaminated waters is of great demand.Nutrients can be reused and applied to land as valuable fertilizers.Composite adsorbents were prepared from industrial waste materials and calcined natural clay.The ability of the new adsorbents to simultaneously remove phosphate and ammonium from diluted solutions was evaluated.Paper mill sludge or blast furnace slag together with kaolinite clay were used as raw materials to produce inorganic polymers by alkaline activation.All raw materials and composites have been characterized by XRF and XRD.The influence of clay and waste material in the adsorbent composition, the adsorbent dose, and time of adsorption characteristics have been investigated at static conditions by bench-top tests.For the best identified composition (metakaolin and blast furnace slag composite), the phosphate adsorption increases from 0.05 mg-P/g for pure clay up to 8.5 mg-P/g for composite with blast furnace slag content of 60 wt.%, while a decrease on the ammonium sorption capacity from 15 mg-N/g to 7 mg-N/g is observed.Phosphate removal was enhanced when ammonium was present, while ammonium removal was slightly varied whether phosphate ions in the system or not.In case of ammonium, ion exchange is the likely mechanism of removal, whereas in the case of phosphate surface precipitation in form of hydroxyapatite appears to occur.
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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) | 0.000 | 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 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".