Optical transparency of electrospun nanofibrous membranes: A study on influencing parameters
Bibliographic record
Abstract
Abstract This paper studies the effects of five parameters, including fiber diameter, crystallinity, thickness, packing density, and surface roughness, on the transparency of electrospun polymethyl methacrylate/polydimethylsiloxane (PMMA/PDMS) nanofibrous membranes after heat treatment. The PMMA/PDMS membranes produced at an electrospinning voltage of 20 kV reaches 54.85% transparency with the diameter 0.759 μm, crystallinity 14.81%, thickness 9.2 μm, roughness 0.659 μm, and packing density α 0.0081% after the heat treatment from 80 to 200°C. In general, the PMMA/PDMS membranes present higher transparency with the decrease of diameter, crystallinity, thickness, roughness, and packing density. The influencing parameters on transparency are first quantified by multiple nonlinear regressions with a R 2 of the model equals 0.869. A composite scaled sensitivity (CSS j ) analysis shows that the most influential factor is the nanofibrous membrane thickness with a CSS j value of 1, followed by fiber diameter with a CSS j value of 0.515. The CSS j values for packing density, membrane roughness and crystallinity are 0.385, 0.361, and 0.173, respectively. Finally, the nanofibrous membrane transparency is a function of nanofiber material density, fiber diameter, and nanofibrous membrane thickness based on the Chandrasekhar radiative transfer equation and the Rayleigh's Scattering Theory.
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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 | 0.001 |
| 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 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".