Optimizing membrane synthesis parameters via Taguchi method: An approach to prepare high performance mixed‐matrix membranes for carbon capture applications
Bibliographic record
Abstract
Abstract Mixed‐matrix membranes (MMMs) comprising polysulfone (PSF) and zeolite 4A (Z4A) were prepared for carbon capture applications. Membranes of varying compositions were fabricated by solution casting technique. Viscous solutions of dissolved ingredients were cast on a clean glass plate followed by evaporation and drying of prepared membrane. Fabricated composite membranes were subjected to morphological, structural, and permeation analyzes. The morphological results showed a uniform dispersal of zeolite nanoparticles with agglomerates formation at higher nanofiller loadings. The structural analysis corroborated the noninteractive behaviour of organic polymer and inorganic zeolite phases. Permeation results suggested that the fabricated membranes were more permeable to CO 2 gas, which can be described by higher diffusivity and structural affinity for CO 2 . Taguchi statistical analysis was employed to optimize carbon capture performance of developed hybrid membranes by carefully controlling the membrane casting parameters such as loading levels of functional nanofiller, sonication time, and drying time of casting solution. The statistical investigation of permeation results suggested the sensitivity of casting parameters on membrane performance in the following manner: zeolite loading > sonication time > drying time. The optimized membrane casting parameters obtained from signal‐to‐noise ratio analysis performed on Minitab led to synthesis of a composite membrane with both high CO 2 /N 2 selectivity and CO 2 permeability. The chosen technique also assisted in justifying the dependence of permeation results on various membrane casting parameters, associating it with the morphological results. The technique also facilitated the optimization of the membrane characteristics and is recommended for future study based on membrane separation processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".