Selective solvent filters for non-aqueous phase liquid separation from water
Bibliographic record
Abstract
Injectable filters permeable to water but impermeable to non-polar solvents were developed to contain non-aqueous phase liquids (NAPL) in contaminated aquifers, hence protecting downstream receptors during NAPL remediation. Filters were produced by injecting aqueous solutions of 0.01% chitosan, hydroxyethylcellulose and quaternized hydroxyethylcellulose into sand columns, followed by rinsing with water. Polymer sorption onto silica was verified using a quartz-crystal microbalance with dissipation monitoring. Fluorescence and gas chromatography mass spectroscopy showed low ppm range concentrations of non-polar solvents (e.g., hexane and toluene) in water eluted from the filters (in the absence of emulsifiers). The contact angles between polymer-coated surfaces and hexane or toluene were > 90°, indicating surface oleophobicity. Organic, polar solvents (e.g. tetrahydrofuran and tetrachloroethylene, TCE) were not separated from water. The contact angles between polymer-coated surfaces and TCE was also > 90°. However, the contact area with polymer coated surfaces was greater for TCE than non-polar solvents, suggesting higher affinity between TCE and the surfaces. Emulsifiers can be used to facilitate NAPL extraction from aquifers. Emulsion separation efficiency depended on the emulsifier used. Emulsions were not separated with classical surfactants (e.g. Tween 20 and oleic acid) or alkaline zein solutions. Partial emulsion separation was achieved with humic acids and zein particles.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".