Development of a dual-functional adsorbent for simultaneous removal of cationic and anionic nutrients from contaminated waste streams
Bibliographic record
Abstract
This work investigated the potential of various natural, modified, and synthetic adsorbent materials for the removal of both cationic (e.g., NH4+) and anionic (e.g., PO43-) nutrients from contaminated waste streams. Among all the studied adsorbents, a commercially available synthetic adsorbent, namely Umix, with 101.6 mg total-P/g, and natural clinoptilolite (NZ) with 17.9 mg total-N/g adsorption capacities demonstrated the best performances in capturing phosphorous-P and nitrogen-N species from aqueous solutions. Zirconium-modified clay (ZrC) also showed a high capacity in adsorbing P species, and eventually, two adsorbent mixtures, including ‘ZrC+NZ’ and ‘Umix+NZ’ were selected for exploring the simultaneous removal of N and P species from bisolute solutions. The effect of various operational parameters, including adsorbent dosage, solution pH, initial nutrient concentrations, and contact time were studied. In an optimum adsorbent (g): solution (ml) loading ratio of 1:300 for ZrC and Umix, and 1:60 for NZ, with 24 h contact time, and using a synthetic NH4-PO4 solution with concentration and pH similar to the targeted wastewater (i.e., dairy farm wastewater), we could achieve 96% total-P and 26% NH4-N removals using ‘ZrC+NZ’, and 94% total-P and 29% NH4-N removals using ‘Umix+NZ’. The pseudosecond-order kinetic model best fitted with experimental data, indicating that chemisorption was the predominant mechanism in bi-solute adsorption processes. A cross-linking reaction between sodium alginate and barium chloride fabricated granules of the powdered adsorbent mixtures. Applying 0.5 g of each of the ‘ZrC/NZ’ and ‘Umix/NZ’ granules in 30 mL of dairy farm wastewater resulted in 20% and 60% total-P and 1.3% and 13% NH4-N removals, respectively. The obtained results showed that the developed granules could be used as dual-functional adsorbent materials to remove cationic and anionic nutrients from contaminated waste streams.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".