Impacts of orthophosphate–polyphosphate blends on the dissolution and transformation of lead (II) carbonate
Bibliographic record
Abstract
Abstract Orthophosphate–polyphosphate blends are commonly used to control lead release into drinking water, but little is known about how they interact with lead corrosion scale. Conventional corrosion control practice assumes that orthophosphate controls lead release by forming insoluble Pb-phosphate minerals, but this does not always occur, and under certain conditions, phosphate blends may increase lead release. Here, we used continuously-stirred tank reactors to compare orthophosphate–polyphosphate blends with orthophosphate on the basis of lead (II) carbonate dissolution and transformation at environmentally relevant phosphate concentrations. Three model polyphosphates—tripoly-, trimeta- and hexametaphosphate—were used. Hexametaphosphate was the strongest complexing agent (1.60–2.10 mol Pb /mol Polyphosphate ), followed by tripolyphosphate and trimetaphosphate (1.00 and 0.07 mol Pb /mol Polyphosphate , respectively. At equivalent orthophosphate and polyphosphate concentrations (as P), orthophosphate-trimetaphosphate had minimal impact on lead release, while orthophosphate-tripolyphosphate increased dissolved lead. Orthophosphate-hexametaphosphate also increased dissolved lead, but only over a 24-h stagnation. Both orthophosphate-tripolyphosphate and orthophosphate-hexametaphosphate increased colloidal lead after 24-h. Increasing the concentrations of hexameta- and tripoly-phosphate increased dissolved lead release, while all three polyphosphates inhibited the formation of hydroxypyromorphite and reduced the phosphorus content of the resulting lead solids. We attributed the impacts of orthophosphate–polyphosphates to a combination of complexation, adsorption, colloidal dispersion, polyphosphate hydrolysis, and lead mineral precipitation.
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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 | 0.001 | 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".