Surface Charge Affecting Fluid–Fluid Displacement at Pore Scale
Bibliographic record
Abstract
Abstract Efficiency in fluid–fluid displacement is drastically reduced by viscous fingering, limiting the overall effectiveness in enhanced oil recovery, membrane science, and lateral flow devices used in biomedical applications. Local instabilities at the fluid–fluid interface lead to finger‐like patterns when a less viscous fluid displaces an immiscible fluid of higher viscosity. This widely observed phenomenon in multiphase flow inside porous media is infamously intricate to control, especially for given geometry and viscosity ratio. The presented study uses a highly controlled microfluidic porous network structure with tailored ionic surface strength. The direct correlation of viscous fingering evolution on the porous structure's zeta potential at a pore‐scale level is demonstrated via polyelectrolyte coatings using a layer‐by‐layer technique. Displacement patterns are tuned from vigorous viscous fingering over stable displacement to corner flow events across a broad range of capillary numbers depending on the applied coatings. The experimental data show an increasing trend of oil recovery with increasing surface wettability, consistent with several previous findings. Furthermore, the results reveal that surface zeta potential correlates positively with recovery rate but negatively with the displacement stability quantified by the fractal dimension. These insights enable a more targeted porous media design to obtain optimal multiphase flow control.
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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) | 0.001 | 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".