Adsorptive Removal of Phosphate and Nitrate Anions from Aqueous Solutions Using Ammonium-Functionalized Mesoporous Silica
Bibliographic record
Abstract
Adsorption of nitrate and monovalent phosphate anions from aqueous solutions on ammonium-functionalized mesoporous MCM-48 silica was investigated. The adsorbent was prepared via a post-synthesis grafting method, using aminopropyltriethoxysilane, followed by acidification in HCl solution to convert the attached surface amino groups to ammonium moieties. The adsorbent was determined to be effective for the removal of both anions. The effects of pH, temperature, initial concentration of anions, and adsorbent loading on both anions adsorption were examined. At ambient temperature, the removal of nitrate was maximum at pH <8, whereas phosphate removal was maximized at 4 < pH < 6. At a given initial concentration, the percentage anions removal increased as the adsorbent loading increased. For instance, maximum removals of 71% and 88% were obtained for nitrate and phosphate solutions, respectively, using an adsorbent loading of 10 g/L. The results also showed that the adsorption capacity decreased as the temperature increased. The adsorption isotherms were approached by the standard adsorption model equations. Based on a model discrimination study, only the Freundlich model resulted in physicochemically sound adsorption enthalpies and entropies for both anions. Desorption of both anions was rapidly achieved within 10 min, using 0.01 M NaOH. Regeneration tests showed that the adsorbent retained its capacity after five adsorption−desorption cycles.
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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 | 0.000 |
| 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".