Perspectives and design considerations of capillary-driven artificial trees for fast dewatering processes
Bibliographic record
Abstract
Recent progresses on nanocapillary-driven water transport under metastable conditions have substantiated the potential of artificial trees for dewatering applications in a wide pressure range. This paper presents a comprehensive performance analysis of artificial trees encompassing the principle for negative capillary pressure generation; impacts of structural, compositional, and environmental conditions on dewatering performance; and design considerations. It begins by delineating functionalities of artificial trees for evaporation (leaves), conduction (xylem), and filtration (root) of water, in the analogy to natural trees. The analysis revealed that the magnitude of (negative) capillary pressure in the artificial leaves and xylem must be sufficiently large to overcome the osmotic pressure of feed at the root. The required magnitude can be reduced by increasing the osmotic pressure in the artificial xylem conduits, which reduces the risk of cavitation and subsequent blockage of water transport. However, a severe concentration polarization that can occur in long xylem conduits would negate such compensation effect of xylem osmotic pressure, leading to vapor pressure depression at the artificial leaves and therefore reduced dewatering rates. Enhanced Taylor dispersions by increasing xylem conduit diameters are found to alleviate the concentration polarization, allowing for water flux enhancement directly by increasing leaf-to-root membrane area ratio.
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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 | 0.001 | 0.001 |
| 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".