Effect of moisture condensation on vapour transmission through porous membranes
Bibliographic record
Abstract
Porous membranes find natural application in various fields and industries. Water condensation on membranes can block pores, reduce vapour transmissibility, and diminish the porous membranes' performance. This research investigates the rate of water vapour transmission through microporous nylon and nanofibrous Gore-Tex membranes. Testing consisted of placing the membrane at the intersection of two chambers with varied initial humidity conditions. One compartment is initially set to a high ([Formula: see text]water vapour concentration and the other low ([Formula: see text], with changes in humidity recorded as a function of time. The impact of pore blockage was explored by pre-wetting the membranes with water or interposing glycerine onto the membrane pores before testing. Pore blockage was measured using image analysis for the nylon membrane. The mass flow rate of water vapour ( ṁ v ) diffusing through a porous membrane is proportional to both its area (A) and the difference in vapour concentration across its two faces ([Formula: see text], such that [Formula: see text] where K is defined as the moisture diffusion coefficient. Correlations are presented for the variation of K as a function of [Formula: see text]. Liquid contamination on the porous membrane has been shown to reduce the moisture diffusion rate through the membrane due to pore blockage and the subsequent reduced open area available for vapour diffusion. Water evaporation from the membrane's surface was observed to add to the mass of vapour diffusing through the membrane. A model was developed to predict the effect of membrane wetting on vapour diffusion and showed good agreement with experimental data.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".