Carbonate Apatite Precipitation from Synthetic Municipal Wastewater
Bibliographic record
Abstract
An important component of phosphorite (phosphate rock) is carbonate apatite, as it is required for phosphorous fertilizer production due to its increased phosphate solubility caused by carbonate substitution in the apatite mineral lattice. High phosphate concentrations in municipal wastewater treatment plants are commonly reduced by precipitating iron phosphate by addition of iron chloride. We investigated the possibility of precipitating carbonate apatite from a potential range of phosphate concentrations that could be available from municipal wastewater treatment plants with anaerobic digestion reactors (5 mM–30 mM). Synthetic phosphate solutions at neutral pH were mixed in batch experiments with a calcium carbonate solution produced by dissolving calcite in contact with carbon dioxide gas, with and without carbonate apatite seed. Batch experiments were used to identify the carbonate apatite supersaturation ranges for homogeneous and heterogeneous nucleation, and the precipitates analyzed with Raman spectroscopy, powder X-ray diffraction, inorganic carbon coulometry, and scanning electron microscopy. Some precipitates contained carbonate weight fractions within the range reported for geological phosphate rock (1.4–6.3 wt %). The precipitates were spherical, poorly crystalline carbonate apatite, suggesting an amorphous precursor transformed to a poorly crystalline carbonate apatite without changing morphology.
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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 | 0.001 |
| 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".