Solubilization of volatile organic compounds in edible nonionic surfactants
Bibliographic record
Abstract
Volatile Organic Compounds (VOCs) are major subsurface contaminants and exist as Non-Aqueous Phase Liquids (NAPLs) in the subsurface.Pump-and-treat remediation is the most common technology used to remediate contaminated groundwater.However, experience has revealed that the traditional pump-and-treat remediation is impractical for treating NAPLs in the subsurface, especially for dense NAPLs (DNAPLs).The difficult desorption of highly hydrophobic compounds and the inability to dissolve residual saturation are the major limiting factors for traditional pump-and-treat remediation.Different chemicals have been used to enhance traditional pump-and-treat remediation.Surfactant enhanced subsurface remediation is identified as a promising technology.In situ Surfactant-Enhanced Subsurface Remediation (SESR) was developed to improve removal efficiency by surfactant solubilization (formation of micelles) and mobilization (reduction in the interfacial tension between the NAPLs and groundwater).The solubilization efficiency of VOCs in an edible surfactant system is very important for the implementation of SESR.The micelle-water partition coefficient (Km) is one of the key parameters to describe the solubilization efficiency of insoluble or sparingly soluble organic compounds in the micelles.A headspace auto-sampler and gas chromatography system was used to test the solubilization of benzene, toluene and TCE in edible nonionic surfactants (Tween 20 and Tween 80 which are mono-fatty acid esters of polyoxyethylene sorbitan) by a modification of the EPICS (Equilibrium Partitioning In Closed Systems) method.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".