Preparation of Breathable Cellulose Based Polymeric Membranes with Enhanced Water Resistance for the Building Industry
Bibliographic record
Abstract
This study focuses on the development of advanced water-resistant bio-based membranes with enhanced vapour permeability for use within building envelopes. Building walls are vulnerable to moisture damage and mold growth due to water penetration, built-in moisture, and interstitial condensation. In this work, breathable composite membranes were prepared using micro-fibrillated cellulose fiber (CF) and polylactic acid (PLA). The chemical composition and physical structure of CF is responsible for its hydrophilic nature, which affects its compatibility with polymers and consequently its performance in the presence of excessive moisture conditions. To enhance the dispersibility of CF in the PLA polymer, the fibers were treated with an organic phosphoric acid ester-based surfactant. The hygroscopic properties of the PLA-CF composites were improved after surfactant treatment and the membranes were resistant to water yet permeable to vapor. Morphological examination of the surface showed better interfacial adhesion and enhanced dispersion of CF in the PLA matrix. Thermal analysis revealed that the surfactant treatment of CF enhanced the glass transition temperature and thermal stability of the composite samples. These bio-based membranes have immense potential as durable, eco-friendly, weather resistant barriers for the building industry as they can adapt to varying humidity conditions, thus allowing entrapped water vapor to pass through and escape the building, eventually prolonging the building life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".