Impacts of Multilayer Hybrid Coating on PSF Hollow Fiber Membrane for Enhanced Gas Separation
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Bibliographic record
Abstract
One of the most critical issues encountered by polymeric membranes for the gas separation process is the trade-off effect between gas permeability and selectivity. The aim of this work is to develop a simple yet effective coating technique to modify the surface properties of commonly used polysulfone (PSF) hollow fiber membranes to address the trade-off effect for CO2/CH4 and O2/N2 separation. In this study, multilayer coated PSF hollow fibers were fabricated by incorporating a graphene oxide (GO) nanosheet into the selective coating layer made of polyether block amide (Pebax). In order to prevent the penetration of Pebax coating solution into the membrane substrate, a gutter layer of polydimethylsiloxane (PDMS) was formed between the substrate and Pebax layer. The impacts of GO loadings (0.0–1.0 wt%) on the Pebax layer properties and the membrane performances were then investigated. XPS data clearly showed the existence of GO in the membrane selective layer, and the higher the amount of GO incorporated the greater the sp2 hybridization state of carbon detected. In terms of coating layer morphology, increasing the GO amount only affected the membrane surface roughness without altering the entire coating layer thickness. Our findings indicated that the addition of 0.8 wt% GO into the Pebax coating layer could produce the best performing multilayer coated membrane, showing 56.1% and 20.9% enhancements in the CO2/CH4 and O2/N2 gas pair selectivities, respectively, in comparison to the membrane without GO incorporation. The improvement is due to the increased tortuous path in the selective layer, which created a higher resistance to the larger gas molecules (CH4 and N2) compared to the smaller gas molecules (CO2 and O2). The best performing membrane also demonstrated a lower degree of plasticization and a very stable performance over the entire 50-h operation, recording CO2/CH4 and O2/N2 gas pair selectivities of 52.57 (CO2 permeance: 28.08 GPU) and 8.05 (O2 permeance: 5.32 GPU), respectively.
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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) | 0.000 | 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 it