Blended polyolefin geomembrane degradation in water and extreme pH mining environments
Bibliographic record
Abstract
Three blended polyolefin (BPO) geomembranes (GMBs) with nominal thicknesses of 1.0 mm (BzSw10), 1.5 mm (BzSw15), and 2.0 mm (BzS20) were immersed for 9.3 years in highly acidic (L1-pH 0.5) and highly alkaline (L8-pH 13.5) heap leaching solutions, and water (pH 6.5–7.6), at 85, 75, and 65°C. The solutions simulated pregnant liquors from metal recovery processes. BzSw10 and BzSw15 shared the same resin and antioxidant package, while BzS20 had a different formulation. All three comprised around 90% linear low-density polyethylene (LLDPE) and 10% high-density polyethylene (HDPE) resin. Times to antioxidant depletion and nominal tensile failure generally increased from BzSw10 to BzSw15 to BzS20 across all media. Among the solutions, pH 13.5 was the most aggressive, causing faster antioxidant depletion and onset of degradation, followed by water (pH 6.5–7.6) and pH 0.5. In pH 13.5, melt index and break strength initially decreased but later stabilized at low values, reflecting the strong influence of solution chemistry. Compared to a HDPE GMB previously immersed in the same pH 13.5 and pH 0.5 solutions, the BPOs exhibited faster antioxidant depletion but generally outperformed the HDPE in terms of mechanical property degradation, including break strength and stress-crack resistance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".