Gaining insights into capillary barrier effects through microfluidic experiments incorporating gravity
Bibliographic record
Abstract
This study performed microfluidic experiments of water displacing air at a low injection rate to provide deeper insights into capillary barrier effects (CBEs), which are encountered in numerous geotechnical and geoenvironmental applications. Four microfluidic chips with varying pore networks, including one single-layer and three double-layer configurations, were used to examine how pore characteristics impact CBEs. Tilt angles were systematically applied to the chips to explore the influence of gravity on CBEs. The results revealed that the introduction of gravity induced directional water invasion, producing downward preferential flows. CBEs were clearly observed when water moved from fine to coarse layers, causing the water motion direction to shift from downward to lateral or backward, and resulting in perched water above the fine–coarse interfaces. Notably, the pause duration of the advancing water front proved to be a more reliable indicator of CBEs’ effectiveness at the microscale than the water storage capacity of fine layers. The formation of CBEs required that the maximum driving pressure on the water–air interface be provided by fine layers after the water front reached the fine–coarse interfaces. A smaller gravitational force and larger pore sizes in coarse layers reduced the driving pressure provided by coarse layers, thereby enhancing the effectiveness of CBEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".