Adsorptive removal of nitrate and phosphate anions from aqueous solutions using functionalised SBA‐15: Effects of the organic functional group
Bibliographic record
Abstract
Abstract Adsorption of nitrate and monovalent phosphate anions from aqueous solutions on mono, di‐ and tri‐ammonium‐functionalised mesoporous SBA‐15 silica was investigated. The adsorbents were prepared via a post‐synthesis grafting method, using either 3‐aminopropyltrimethoxysilane (N‐silane) or [1‐(2‐aminoethyl)‐3‐aminopropyl]trimethoxysilane (NN‐silane) or 1‐[3‐(trimethoxysilyl)‐propyl]‐diethylenetriamine (NNN‐silane), followed by acidification in HCl solution to convert the attached surface amino groups to positively charged ammonium moieties. The nominal loading of amino moieties on the SBA‐15 surface was varied from 5% to 20% as organoalkoxysilane/silica molar ratio. The adsorption experiments were conducted batchwise at room temperature. Results showed that adsorption capacity increased with increasing the concentration of monoammonium groups on the SBA‐15 adsorbent. Nitrate adsorption capacity increased from 0.34 to 0.66 mmol ${\rm NO}_{3}^{{-} } /{\rm g}$ adsorbent while phosphate adsorption capacity increased from 0.34 to 0.63 mmol ${\rm H}_{2} {\rm PO}_{4}^{{-} } /{\rm g}$ adsorbent when the molar ratio organoalkoxysilane/silica was varied from 5% to 20%, respectively. Also, for the same organoalkoxysilane/silica molar ratio of 10%, the adsorption capacity increased with the increase of the number of protonated amines in the functional groups. Therefore, maximum adsorption capacities of 0.80, 1.16 and 1.38 mmol ${\rm NO}_{3}^{{-} } /{\rm g}$ adsorbent and 0.72, 0.82 and 1.17 mmol ${\rm H}_{2} {\rm PO}_{4}^{{-} } /{\rm g}$ adsorbent were obtained using mono‐, di‐ and triammonium functionalised SBA‐15 adsorbents, respectively. © 2011 Canadian Society for Chemical Engineering
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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) | 0.001 | 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".