Fractal Flow Patterns in Hydrophobic Microfluidic Pore Networks: Experimental Modeling of Two-Phase Flow in Porous Electrodes
Bibliographic record
Abstract
Experimental two-phase invasion percolation flow patterns were observed in hydrophobic microporous networks designed to model the operational conditions encountered in the porous electrodes of polymer electrolyte membrane fuel cells. The microporous networks were fabricated mimicking the small thickness of porous electrodes and their pore distributions. The inlet channels were invaded homogeneously with flow rates corresponding to fuel cell current densities of 1.0– ( ). A variety of fractal breakthrough patterns were observed and analyzed to quantify flooding density and geometrical diversity in terms of the total saturation, , local saturations, , and fractal dimension, . increased monotonically during the invasion process until the breakthrough point was reached, and profiles indicated the dynamic distribution of the liquid phase during the process. Fractal analysis confirmed that the experiments fall within the flow regime of invasion percolation with trapping (IPT). Fractal dimensions of different IPT flow patterns spanned an interval of 1.6–1.8 depending on the flow rate of invasion. In this work, we proposed to correlate the fractal dimension to the total saturation and use this map as a parameter for modeling liquid water transport in porous electrodes.
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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) | 0.000 | 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".