Sodium silicate and hexametaphosphate promote the release of (oxyhydr)oxide nanoparticles from corroding iron
Bibliographic record
Abstract
Sequestrants such as polyphosphate and sodium silicate are used widely to control iron precipitation in drinking water, but less is known about their impacts on iron corrosion scale. Here we characterize the nanoparticulate iron (oxyhydr)oxide suspensions that result from corroding cast iron coupons in solutions containing either sodium hexametaphosphate (3 mg P L-1) or sodium silicate (100 mg SiO2 L-1). We determined the elemental composition and size distribution of these suspensions using flow field-flow fractionation with ultraviolet, multielement, and multiangle light scattering detection (FFF-UV-MALS-ICP-MS). Both sequestrants yielded stable iron suspensions, and the pooled median radius of gyration at peak 57Fe intensity was 22 nm, corresponding to a sphere-equivalent geometric diameter of 57 nm. The median Feret diameter and ASTM roundness were 50 nm and 0.4, respectively, as determined by transmission electron microscopy. Lead associated readily with iron nanoparticles, and in the hexametaphosphate suspension it associated preferentially with iron over free hexametaphosphate. Sequestrants then, may interact with iron corrosion scale to yield effective transport vectors for lead in drinking water systems, even when complexation of free lead by the sequestrant is negligible.
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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".