The mechanism of extraction of organic compounds using polyurethane membranes
Bibliographic record
Abstract
The main focus of this research has been to investigate the process by which organic compounds in solution are taken up by polyurethane membrane under various chemical and physical conditions. This extensive mechanistic study involved examining the effect of solution conditions on extraction (presence of salts, pH), the size and polarity of the organic species, the type and position of substituents on the molecule, membrane properties (active surface area, thickness), and temperature on the sorption of pollutants and industrially important compounds such as various phenols, benzoic acids, organic dyes, organometallic ion-association complexes, and organic solvents. It was found that the formation of a neutral species in solution and the ability to engage in hydrogen-bonding with the membrane is essential for extraction into polyurethane to occur. The size of the organic species is not as important as its overall polarity and the relative solubility in the solvent and in the membrane. Increased removal of species from solution can be attained by providing either a larger surface area exposed to the sample solution, a thicker membrane, or a receiving solution into which the compound can be desorbed from the membrane. Higher temperature can be used to accelerate the entire sorption process. The mechanism by which organic compounds are removed from solution by the polyurethane membrane can be subdivided into three separate phenomena, namely, the transfer of species from bulk solution to the solution-membrane interface and the reverse, adsorption onto the membrane surface, and transport into the bulk of the polymer.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".