Full-Factorial Experimental Design to Determine the Impacts of Influential Parameters on the Porosity and Mechanical Strength of LLDEP Microporous Membrane Fabricated via Thermally Induced Phase Separation Method
Bibliographic record
Abstract
Membrane separation processes have a wide application in liquid and gas purification industries. They enjoy advantages such as convenient processibility, easy and lower production and operational costs. Thermally induced phase separation (TIPS) process, due to its wide advantages, has won special attention in recent decades. In this process, a homogenous solution of polymer-diluent at a temperature above the polymer melting point is formed and the solution is then cast in the favorite shape. In order to create a porous structure, the diluent is extracted. In this work, microporous LLDPE membrane is fabricated and full factorial experimental design is used to evaluate the individual as well as mutual impacts of polymer concentration, membrane thickness and cooling bath temperature on the porosity and mechanical strength of the membrane. The results obtained from the analysis of variance of membrane porosity and mechanical strength, showed that the impact of cooling bath temperature is much more important than polymer concentration and membrane thickness. Higher cooling bath temperature, lower polymer concentration and membrane thickness result in higher porosity.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".