Spectroscopic Investigation of the Interaction of Silicate Ions with Lead Carbonates Under Drinking Water Conditions
Bibliographic record
Abstract
The presence of lead has been identified as a critical health risk in drinking water systems serviced by Pb-bearing plumbing. Among several corrosion control strategies, the use of sodium silicates has attracted interest due to the advantages it offers compared to other approaches, such as phosphate dosage. However, the interaction of silicate ions with lead corrosion scales and other ubiquitous dissolved species such as Al ions in drinking water is not well understood. In this work, surface and bulk spectroscopic analysis of the solid scale is combined with quantitative analysis of the aqueous phase. A detailed spectroscopic probing of the transformations taking place on the solid phase enables us to develop a mechanistic framework for reports published in the last four years in the open literature, suggesting that silicates may not be an adequate corrosion control option in drinking water systems rich in solid lead carbonates. The spectroscopic data obtained demonstrate that in the presence of chlorine residual, silicates inhibit Pb(II) carbonates from oxidizing into less soluble Pb(IV) oxides thus, negatively impacting water quality. Furthermore, aluminum ions interact with silicates resulting in the formation of solid allophane phase over the lead scale surface, extending into the bulk. However, the formation of this new solid allophane phase does not protect against lead dissolution.
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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) | 0.002 | 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".