Investigation of Ammonium Ion Removal from Aqueous Solutions Using Arene- and Propylsulfonic Acid Functionalized Mesoporous Silica Adsorbents
Bibliographic record
Abstract
To counter environmental threats to the water resources polluted by NH, which is common in wastewaters and agricultural runoff, adsorption using mesoporous functional materials represents a promising alternative to existing treatment methods. In this study, adsorption of NH ions from aqueous solutions was investigated on arene- and propylsulfonic acid functionalized SBA-15 mesoporous silica materials. The adsorbents were synthesized via co-condensation and post-synthesis grafting procedures. Adsorbents were characterized by means of X-ray diffraction, N physisorption, titration, and elemental analyses. The effects of pH, NH initial concentration, temperature, adsorbent loading, organosilane molar ratio, and presence of competitive species on the performance of the adsorbent materials were examined. All the adsorbents having an organosilane/silica molar ratio of 1:5 displayed maximum adsorption capacity around approximately 25 mg g NH at the lowest temperature investigated, 5°C. This capacity decreased with increasing temperature. For a given initial NH concentration, the removal efficiency () increased with increasing adsorbent loading. For instance, increased from 24 to 59% when the adsorbent loading was increased from 2 to 10 g L at 25°C. The adsorption isotherms were well described by a Langmuir model equation. Adsorption capacity improved with increasing organosilane/silica molar ratio, reaching 42 mg g NH with a ratio of 2:5 at 25°C. Arene- and propylsulfonic acid functionalized SBA-15 materials synthesized via co-condensation and post-synthesis grafting proved to be effective high-capacity adsorbents for the removal of NH ions from aqueous solutions.
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How this classification was reachedexpand
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 | 0.002 | 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) | 0.001 | 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 teacher head, 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".