Phosphate Removal from Wastewater by Magnetic Amorphous Lanthanum Silicate Alginate Hydrogel Beads
Bibliographic record
Abstract
It is of both fundamental and practical importance to develop effective adsorbents for removing phosphate from aqueous solutions continuously. In this study, magnetic amorphous lanthanum silicate alginate hydrogel beads (MALS-B) were prepared and used for phosphate removal. Mesoporous silica materials with highly ordered and hexagonal channel structures were synthesized from natural mineral rectorite (REC) at room temperature. On this basis, amorphous lanthanum silicate (ALS) was synthesized by theone-pot method using a silicon source from REC and a commercial lanthanum source. Further, MALS-B were synthesized from sodium alginate (SA) with ALS and Fe3O4 as the incorporated adsorbable and magnetic nanoparticles via a simple cross-linking method in CaCl2 solution. The synthesized hydrogel beads were characterized by various techniques. ALS and Fe3O4 existed relatively independently in MALS-B, where ALS provided adsorption sites and Fe3O4 provided magnetism. They played a synergistic role in phosphate removal. The saturation magnetization value of MALS-B was 17.38 emu/g, enabling theirfacile separation from aqueous solutions after phosphate adsorption. MALS-B exhibited a preferable adsorption capacity of 40.14 mg P/g for phosphorus compared to other hydrogel beads based on adsorption experiments. More significantly, MALS-B exhibited excellent selectivity for phosphate in aqueous solutions with various interfering ions and possessed a high affinity to phosphate in a wide pH range. MALS-B showed the treatment volume of 480 BV when effluent phosphate concentration was below 0.5 mg/L in fixed-bed column adsorption. The adsorption mechanism was also revealed. Our work demonstrates that MALS-B can serve as a promising adsorbent for continuous phosphate adsorption.
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) | 0.000 | 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".