Structural, Thermal, and Surface Properties of PVDF/Silica Aerogel Nanocomposite Membranes for Membrane Distillation Application
Bibliographic record
Abstract
This study addresses membrane distillation's key challenges - wetting and thermal inefficiency - by developing PVDF/silica aerogel nanocomposite membranes with optimized sublayer properties. We fabricated membranes with systematic variations in PVDF concentration (12-21%) and silica aerogel loading (1-3%), characterizing their structural and surface properties. FTIR analysis confirmed successful nanoparticle incorporation without altering PVDF chemistry. Porosity exhibited concentration-dependent behavior: increasing with silica at 12% PVDF, stable at 18%, and decreasing at 21% due to viscosity effects on phase separation. All nanocomposites showed reduced thermal conductivity, enhancing insulation. While skin layer hydrophobicity remained constant, silica migration significantly increased sublayer contacts angles (peak 130.6° for 18% PVDF/3% silica, 20% improvement over control). The 18% PVDF formulation demonstrated optimal balance, maintaining structural integrity while achieving high porosity (78.3%) and low thermal conductivity (0.048 W/mK). These results highlight two critical findings: (1) PVDF concentration dictates nanoparticle effects on membrane morphology, and (2) strategic silica incorporation simultaneously enhances sublayer hydrophobicity and thermal resistance without compromising mechanical stability. The study provides a design framework for MD membranes, demonstrating how sublayer engineering can mitigate wetting while improving thermal efficiency - crucial advancements for practical MD implementation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".