Selective and efficient removal of phosphate from aqueous solution using activated carbon‐supported Mg–Fe layered double oxide nanocomposites
Bibliographic record
Abstract
Abstract In the face of the continuous development of novel adsorbents, developing robust adsorbents with high efficiency, strong phosphate selectivity, high regenerability, and cost effectiveness is a scientific challenge. In the present study, an activated carbon‐supported MgFe 2 O 4 ‐layered double hydroxide (AC@ MgFe 2 O 4 ‐LDH) derived Mg–Fe layered double oxide (AC@ MgFe 2 O 4 ‐LDO) nanocomposite was synthesized at various temperatures and its potential application for phosphate adsorption was investigated. The nanocomposite exhibited a hierarchical mesoporous structure with a Brunauer–Emmett–Teller (BET) specific surface area of 193 m 2 /g and a narrow per‐size distribution of ~2 nm. AC@MgFe 2 O 4 ‐LDO exhibited a high point of zero charge (pH pzc ) value of 9.8 and robust phosphate adsorption potential over a wide pH range of 4–9 due to its high pH buffering capacity. The effects of adsorbent dose, layered double hydroxides (LDH) calcination temperature, initial phosphate concentration, contact time, and temperature on the phosphate adsorption capacity of the adsorbent were investigated. In the present study, up to 99.0% removal of phosphate was achieved at a 4 g/L adsorbent dosage in 4 h at pH 7 and 30°C. An adsorption kinetics study revealed that the adsorption of phosphate by AC@MgFe 2 O 4 ‐LDO reached equilibrium within 240 min, with the kinetic experimental data fitting well with pseudo‐first‐order kinetics ( r 2 >0.99). The Langmuir adsorption isotherm model fit the experimental data well, with a maximum adsorption capacity of 25.81 mg/g. The adsorbent displayed strong phosphate selectivity in the presence of competing anions, and the study demonstrated that AC@MgFe 2 O 4 ‐LDO has promising potential for efficient phosphate adsorption over a wide pH range.
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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) | 0.000 | 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) | 0.000 | 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".