The role of hydrophobic properties in ion exchange removal of organic compounds from water
Bibliographic record
Abstract
The removal of organic compounds by ion exchange resins is a complex process due to the involvement of several interacting phenomena during the course of removal. The objective of this research is to clarify the contribution of these interactions to the removal of different organic compounds under various solution conditions. This study investigates the sorption of three ionizable organic compounds, which are identical in terms of ionic charges and different in their non‐polar moieties, in order to better evaluate the contribution of solute‐solvent interactions to the affinity of these compounds for ion exchange. The higher uptake of hydrophobic compounds and lower competition effect from inorganic anions on the removal of these compounds provide evidence for the importance of the hydrophobic effect. The hydrophobic characteristics of organic compounds and the favourable entropy change that they impose during de‐solvation contribute greatly to the ion exchange selectivity of these compounds. Furthermore, increasing the ionic strength of solution by adding high charge density anions, such as sulphate, to the water diminishes the contribution of electrostatic interaction, and hence the potential physical adsorption between resin and organic molecule predominates the removal mechanism. No change in removal mechanism is observed in the presence of inorganic anions with low charge density such as nitrate.
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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 | 0.001 |
| 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".