Specific polarizability of sand–clay mixtures with varying ethanol concentration
Bibliographic record
Abstract
ABSTRACT We utilise a concept of specific polarizability , represented as the ratio of mineral‐fluid interface polarization per pore‐normalised surface area , to demonstrate the influence of clay‐organic interaction on complex conductivity measurements. Complex conductivity measurements were performed on kaolinite‐ and illite‐sand mixtures as a function of varying ethanol (EtOH) concentration (10% and 20% v/v). The specific surface area of each clay type and Ottawa sand was determined by nitrogen‐gas‐adsorption Brunauer‐Emmett‐Teller method. We also calculated the porosity and saturation of each mixture based on weight loss of dried samples. Debye decomposition, a phenom‐enological model, was applied to the complex conductivity data to determine normalised chargea‐bility . Specific polarizability estimates from previous complex conductivity measurements for bentonite‐sand mixtures were compared with our dataset. The for all sand–clay mixtures decreased as the EtOH concentration increased from 0% to 10% to 20% v/v. We observe similar responses to EtOH concentration for all sand–clay mixtures. Analysis of variance with a level of significance suggests that the suppression in responses with increasing EtOH concentration was statistically significant for all sand–clay mixtures. On the other hand, real conductivity showed only 10% to 20% v/v changes with increasing EtOH concentration. The estimates reflect the sensitivity of complex conductivity measurements to alteration in surface chemistry at available surface adsorption sites for different clay types, likely resulting from ion exchange at the clay surface and associated with kinetic reactions in the electrical double layer of the clay‐water‐EtOH media. Our results indicate a much larger influence of specific surface area and ethanol concentration on clay‐driven polarization relative to changes in clay mineralogy.
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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".