Polymer based membranes for propylene/propane separation: CMS, MOF and polymer electrolyte membranes
Bibliographic record
Abstract
<abstract> <p>Propylene/propane separations are generally performed by distillation which are energy intensive and costly to build and operate. There is therefore high interest to develop new separation technologies like membrane modules. In our previous paper, we collected, analyzed and reported data for neat polymers and mixed matrix membranes (MMM) based on flat and hollow fiber configurations for propylene/propane separations. In this second part, we collected the data for carbon molecular sieving (CMS) membranes from polymer pyrolysis reaction and metal-organic framework (MOF) membranes from different fabrication methods, as well as data on facilitated transport membrane-polymer electrolyte membranes (PEM). CMS membranes show great potential for C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub> separation with an optimum pyrolysis temperature around 500–600 ℃. However, physical aging is a concern as the micro-pores shrink over time leading to lower permeability. The performance of MOF membranes are above the 2020 upper bound of polymer-based membranes, but have limited commercial application because they are fragile and difficult to produce. Finally, facilitated transport membranes show excellent propylene/propane separation performance, but are less stable compared to commercial polymeric membranes limiting their long-term operation and practical applications. As usual, there is no universal membrane and the selection must be made based on the operating conditions.</p> </abstract>
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".