Pore-scale study on the effect of heterogeneity on evaporation in porous media
Bibliographic record
Abstract
The evaporation process in porous media typically experiences three main periods, among which the first period, named the constant rate period (CRP), performs most efficiently in removing liquid. We aim to prolong the CRP to very low degrees of saturation ( S ) and increase its evaporation rate by playing with heterogeneity in wettability and pore size. First, we show that a porous medium with a smaller contact angle at the surface and increasing contact angle towards the inside generally dries out faster compared with that with uniform contact angle. Second, a constant contact angle porous medium with smaller/larger pores in the surface/inside part dries out faster than a medium with uniform pore size. The underlying mechanism is the occurrence of a capillary pressure jump at the border between the two layers accompanied by enhanced capillary pumping, increasing/maintaining the interfacial area in the surface pores. Harnessing the potential of this mechanism, we propose an optimized strategy by combining two heterogeneity effects: increasing contact angle and pore size towards the inside. This strategy is found to be robust both for multilayer and larger systems. In this case, a small drying front first penetrates fast towards the inside and then expands, followed by a horizontal drying front moving back layer by layer to the surface. Quantitatively, compared with evaporation from a homogeneously porous medium with uniform contact angle where CRP stops at $S=0.64$ , our optimized design can extend the CRP down to $S=0.12$ , and decrease five-fold the drying time needed to reach $S=0.05$ .
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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".