Removal of ammonium cations from aqueous solution using arene‐sulphonic acid functionalised SBA‐15 as adsorbent
Bibliographic record
Abstract
Abstract Removal of ammonium cations from aqueous solution was investigated using an arene‐sulphonic acid functionalised mesostructured SBA‐15 material as adsorbent. Arene‐sulphonic acid‐SBA‐15 (AS‐SBA‐15) was prepared via a co‐condensation strategy using tetraethylorthosilicate (TEOS) and 2‐(4‐chlorosulphonylphenyl)ethyltrimethoxysilane (CSPTMS) as framework precursors under acidic conditions. The material exhibited high surface area (680 m2/g) and total pore volume (0.84 mL/g). The effects of adsorbent loading, initial ammonium concentration, temperature, pH and the presence of competitive ions on the adsorption performances were investigated. The ammonium removal increased with the increase of the adsorbent loading and the decrease of the initial concentration. The adsorption capacity decreased with increasing the temperature. Maximum adsorption capacity obtained at 5°C was ca. 19 mg NH /g adsorbent. The isotherms data were studied using different adsorption models and thermodynamic parameters were calculated. Competitive ions such K+ and Na+ slightly affected the ammonium adsorption. After six adsorption–desorption cycles, the adsorbent retained its adsorption capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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".