Mass transport limitations in high-performance water-vapor selective membranes: A multiphysics simulation approach
Bibliographic record
Abstract
• Cell geometry and flow reduce membrane performance by ∼20% compared to intrinsic values. • Boundary layer growth impacts membrane performance evaluation. • Test cells show hotspots and dead zones, limiting active membrane area performance. • Scaling membrane lengths from 1 mm to 40 mm reduces performance by 10–50%. The pursuit of high-performance, water–vapor selective membranes remain a focal point, as membranes often emerge as energy-efficient alternatives for psychrometric processes. Recent literature results on membranes fabricated from specialized polymers, 2D materials, and metal–organic frameworks (MOFs) as selective layers, have demonstrated unprecedented water vapour permeance and selectivity. However, few studies have examined how such high intrinsic permeances interplay with transport limitations governing the overall membrane performance in common test-cell geometries. This study aims to assess the theoretical limits of diffusion-based membranes by studying the impact of various transport resistances on the overall system permeance. Multiphysics simulations are used to model both fluid dynamics and mass transport within a simplistic geometry versus a commercially available sweep cell. A non-uniform flux pattern emerges, highlighting the substantial impact of non-uniform flow fields and fluid boundary layers, which were previously deemed inconsequential in gas separations. This leads to the underutilization of the membrane, yet unexpectedly increases the overall process permeance by approximately 20% when compared to a simple planar geometry. As researchers engineer better selective layers, future advancements could push this difference as high as 36%. By utilizing dimensionless groups and fitting parameters (0.76 Re 0.438 Sc 0.33 ), the true membrane permeance can be extracted from the test configuration within a 5% margin of error. This approach is essential for assessing permeances, scaling membranes, and enabling accurate comparisons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".