Impact of natural organic matter characteristics and inorganic anions on the performance of ion exchange resins in natural waters
Bibliographic record
Abstract
Abstract Ion exchange (IX) process is increasingly used as a cost-effective treatment for the removal of natural organic matter (NOM) from drinking water. However, fundamental studies under the influence of variable NOM characteristics and inorganic anions have often been overlooked. This is important given NOM characteristics (such as charge density and molecular weight) and inorganic anions concentrations are geographically and seasonally variable. We examined the performance of a strongly basic IX resin for the simultaneous removal of NOM, inorganic ions and micropollutants (Per- and polyfluoroalkyl substances (PFAS) and algal toxins), from different surface and recycled waters. The results indicated >70% removal of NOM for ∼20,000 Bed Volumes (BV) with an uptake of NOM fractions following the order of their respective charge densities. IX pore blockage and consequent site reduction was observed in the presence of higher molecular weight NOM fractions (breakthrough ∼7,000 BV). Moreover, NOM and inorganic ions breakthrough corresponded to ∼85–90% site occupancy (in meq) in the absence of pore blocking compounds. IX also provided simultaneous removal of inorganic ions (>90%) and charged micropollutants. Complete removals of Microcystin-LR and multiple long- and short-chained PFAS were achieved at environmentally relevant concentrations with dosages of 1,000 mg/L (or 4.5 mL/L) or higher.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".