Separation of Refrigerant Gas Mixtures Containing\nR32, R134a, and R1234yf through Poly(ether-<i>block</i>-amide)\nMembranes
Why this work is in the frame
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Bibliographic record
Abstract
Hydrofluorocarbons\n(HFCs) are powerful greenhouse gases whose production\nand consumption must be phased down in order to reach the reduction\ngoals established by the Kigali Amendment to the Montreal Protocol.\nHowever, the share of recycled refrigerant gases remains very low\nowing to the extremely inefficient separation of refrigerant mixtures\nby cryogenic distillation. In this sense, the HFCs, difluoromethane\n(R32, GWP = 675) and 1,1,1,2-tetrafluoroethane (R134a, GWP = 1430),\ntogether with the hydrofluoroolefin (HFO) 2,3,3,3-tetrafluoropropene\n(R1234yf, GWP = 4), are among the most common constituents of HFC/HFO\nrefrigerant mixtures currently employed in the refrigeration and air-conditioning\nsector. Therefore, the feasibility of using membrane technology for\nthe selective separation of these compounds is assessed in this work\nfor the first time. A comprehensive study of their gas permeation\nthrough several poly(ether-<i>block</i>-amide) (PEBA) membranes\nthat differ on the content and type of backbone segments is performed.\nResults show that PEBA membranes exhibit superior permeability of\nR32 (up to 305 barrer) and R134a (up to 230 barrer) coupled with reasonably\nhigh selectivity for the gas pairs R32/R1234yf (up to 10) and R134a/R1234yf\n(up to 8). Moreover, for the blends R32/R1234yf and R32/R134a, the\nmembrane separation performance is not significantly affected under\nthe mixed gas conditions tested. Thus, results evidence that consideration\nshould be given to membrane technology for the cost-efficient separation\nof HFC/HFO mixtures in order to boost the recycling of these compounds.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.077 | 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