Size and Zeta Potential of Oxidized Iron and Manganese in Water Treatment: Influence of pH, Ionic Strength, and Hardness
Bibliographic record
Abstract
Iron and manganese are commonly found in natural waters, particularly in groundwater. Because of the importance of particle size distribution (PSD) on the performance of removal processes, this research focuses on understanding the PSD and ζ-potentials of oxidized iron/manganese in water, as a function of pH, ionic strength, and hardness. After rapid oxidation of dissolved iron/manganese, laser diffraction (LD), dynamic light scattering (DLS), and fractionation through serial membrane filtration techniques were used to define the PSD. For manganese dioxide, the ζ-potential was found to decrease as the pH decreased and as the ionic strength and hardness increased, resulting in a higher aggregate size. The aggregation rate of manganese dioxide strongly increased with hardness. On the other hand, ferric hydroxide PSD was not significantly altered by ionic strength or hardness at pH values relevant to typical natural waters. A combination of several techniques was found to be essential for providing a complete picture of the PSD. The DLS and LD techniques were generally well adapted for nano-scale and micron-scale particles, respectively. The serial membrane filtration technique was suggested for water practitioners working toward the selection of an appropriate process for iron/manganese control in drinking waters.
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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) | 0.000 | 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".