Modelling pH-Dependent and Microstructure-Dependent Streaming Potential Coefficient and Zeta Potential of Porous Sandstones
Bibliographic record
Abstract
In this paper, we have significantly modified an existing model for calculating the zeta potential and streaming potential coefficient of porous media and tested it with a large, recently published, high-quality experimental dataset. The newly modified model does not require the imposition of a zeta potential offset but derives its high salinity zeta potential behaviour from Stern plane saturation considerations. The newly modified model has been implemented as a function of temperature, salinity, pH, and rock microstructure both for facies-specific aggregations of the new data and for individual samples. Since the experimental data include measurements on samples of both detrital and authigenic overgrowth sandstones, it was possible to model and test the effect of widely varying microstructural properties while keeping lithology constant. The results show that the theoretical model represents the experimental data very well when applied to model data for a particular lithofacies over the whole salinity, from 10−5 to 6.3 mol/dm3, and extremely well when modelling individual samples and taking individual sample microstructure into account. The new model reproduces and explains the extreme sensitivity of zeta and streaming potential coefficient to pore fluid pH. The low salinity control of streaming potential coefficient by rock microstructure is described well by the modified model. The model also behaves at high salinities, showing that the constant zeta potential observed at high salinities arises from the development of a maximum charge density in the diffuse layer as it is compressed to the thickness of one hydrated metal ion.
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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 | 0.000 |
| 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 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".