Effect of Surface Fluorination on Diffusion through a High Density Polyethylene Geomembrane
Bibliographic record
Abstract
The relative improvement of the diffusive barrier function of high density polyethylene (HDPE) geomembranes to volatile organic compounds (VOCs) when subjected to surface fluorination is experimentally examined. The surface fluorination consisted of applying elemental fluorine, which exchanged with hydrogen along polymer chains at the surface of a polyolefin substrate. Sorption and diffusion tests were performed on both traditional “untreated” and “fluorinated” 1.5mm HDPE geomembranes using dilute aqueous organic contaminants commonly found in municipal solid waste leachate. The partitioning coefficient is shown to remain essential the same after the surface fluorination; however, the surface fluorination resulted in a reduction in both the diffusion and the permeation coefficients by factors ranging between 1.5 and 4.5, depending on the hydrocarbon examined. Modeling of VOC diffusion through a geomembrane/compacted clay composite liner indicated that contaminant impacts were about 1.7–2.9 times lower when a fluorinated geomembrane is used. To achieve the same level of protection as provided by the fluorinated geomembrane underlain by 0.60m of compacted clay, one would need an additional 0.4–0.9m of compacted clay in conjunction with a conventional (untreated) geomembrane. The importance of the thickness of the treated layer is highlighted.
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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 | 0.001 |
| 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 | 0.000 |
| 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".