Lab-on-chip methodologies for the study of transport in porous media: energy applications
Why this work is in the frame
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Bibliographic record
Abstract
We present a lab-on-chip approach to the study of multiphase transport in porous media. The applicability of microfluidics to biological and chemical analysis has motivated much development in lab-on-chip methodologies. Several of these methodologies are also well suited to the study of transport in porous media. We demonstrate the application of rapid prototyping of microfluidic networks with approximately 5000 channels, controllable wettability, and fluorescence-based analysis to the study of multiphase transport phenomena in porous media. The method is applied to measure the influence of wettability relative to network regularity, and to differentiate initial percolation patterns from active flow paths. Transport phenomena in porous media are of critical importance to many fields and particularly in many energy-related applications including liquid water transport in fuel cells, oil recovery, and CO(2) sequestration.
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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 it