Using viscosity as an index for polymer loading of bentonite-polymer composite geosynthetic clay liners
Bibliographic record
Abstract
Bentonite-polymer composite (BPC) geosynthetic clay liners (GCLs) containing a mixture of air-dry granules of bentonite and polymer have been developed for containment of wastes that generate leachates that are too aggressive for conventional sodium bentonite GCLs. Sufficient polymer loading is essential for BPC GCLs to maintain low hydraulic conductivity, and expedient methods are needed for manufacturing quality control and construction quality control to confirm that BPC GCLs contain sufficient polymer. In this study, a methodology for developed to estimate the polymer loading based on viscosity testing of slurries prepared with the BPC. A simplified version of the method can be used to determine if polymer is present in a BPC. Factors influencing the viscosity measurement were evaluated systematically, including water-to-BPC ratio, tempering time, and mixing method. The method that was developed consists of (1) adding deionized water to dry BPCs to achieve a water-to-BPC ratio of 30, (2) blending the BPC-water mixture with an overhead stirrer at 5000 rpm for 30 min to create a homogeneous slurry, (3) tempering the slurry in a zip-top bag for 24 hr, and (4) measuring the viscosity of the slurry in a viscometer at 300 and 600 rpm. Linear relationships were developed between polymer loading and viscosity for two BPC GCLs. Independent validation confirmed that the polymer loading estimated with the method is ±0.25% of the actual polymer loading.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".