Removal of Phosphate from Eutrophic Lakes through Adsorption by in Situ Formation of Magnesium Hydroxide from Diatomite
Bibliographic record
Abstract
Since in situ formation of Mg(OH)2 can efficiently sorb phosphate (PO4) from low concentrations in the environment, a novel dispersed magnesium oxide nanoflake-modified diatomite adsorbent (MOD) was developed for use in restoration of eutrophic lakes by removal of excess PO4. Various adsorption conditions, such as pH, temperature and contact time were investigated. Overall, sorption capacities increased with increasing temperature and contact time, and decreased with increasing pH. Adsorption of PO4 was well described by both the Langmuir isotherm and pseudo second-order models. Theoretical maximum sorption capacity of MOD for PO4 was 44.44-52.08 mg/g at experimental conditions. Characterization of PO4 adsorbed to MOD by use of X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and solid state (31)P nuclear magnetic resonance revealed that electrostatic attraction, surface complexation and chemical conversion in situ were the major forces in adsorption of PO4. Mg(OH)2 formed in situ had a net positive charge on the surface of the MOD that could adsorb PO4(3-) and HPO4(2-) anion to form surface complex and gradually convert to Mg3(PO4)2 and MgHPO4. Efficiency of removal of PO4 was 90% when 300 mg MOD/L was added to eutrophic lake water. Results presented here demonstrated the potential use of the MOD for restoration of eutrophic lakes by removal of excess PO4.
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".