Synthesis, performance, and mechanism of magnesium‐iron‐aluminum trimetal composite as an adsorbent for fluoride removal in water treatment
Bibliographic record
Abstract
Abstract In this study the Mg‐Fe‐Al trimetal composite was successfully synthesized by an easy co‐precipitation method and used as the adsorbent for fluoride removal from aqueous solutions. Synthetic conditions such as Mg/Fe/Al molar ratio and calcination temperature were studied to optimize the adsorbent. The adsorbents were characterized by X‐ray diffraction (XRD), scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDS), and Fourier transform infrared (FTIR). Batch adsorption studies were conducted under various conditions, such as different fluoride concentration, contact time, temperature, initial solution pH, and coexisting anions. Results indicated that the adsorbent obtained the maximum adsorption capacity of 92.85 mg/g for fluoride with a Mg/Fe/Al molar ratio of 30:1:4, calcined at 500 °C, at near‐neutral pH and room temperature (25 °C). The adsorption data were fitted well with the Langmuir isotherm model and the pseudo‐first order kinetic model. The adsorption mechanism involved electrostatic interaction on the surface, ion exchange interaction, and reconstruction of original layered structure by rehydration of mixed metal oxides. All results indicated that the Mg‐Fe‐Al trimetal composite can be a very promising material, has potential application in the removal of fluoride in water treatment, and has positive effects on human health.
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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) | 0.001 | 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".