Assessment of Low Global Warming Potential Refrigerants\nfor Drop-In Replacement by Connecting their Molecular Features to\nTheir Performance
Bibliographic record
Abstract
The use of hydrofluorocarbons\n(HFCs) as an alternative for refrigeration\nunits has grown over the past decades as a replacement to chlorofluorocarbons\n(CFCs), banned by the Montreal’s Protocol because of their\neffect on the depletion of the ozone layer. However, HFCs are known\nto be greenhouse gases with considerable global warming potential\n(GWP), thousands of times higher than carbon dioxide. The Kigali Amendment\nto the Montreal Protocol has promoted an active area of research toward\nthe development of low GWP refrigerants to replace the ones in current\nuse, and it is expected to significantly contribute to the Paris Agreement\nby avoiding nearly half a degree Celsius of temperature increase by\nthe end of this century. We present here a molecular-based evaluation\ntool aiming at finding optimal refrigerants with the requirements\nimposed by current environmental legislations in order to mitigate\ntheir impact on climate change. The proposed approach relies on the\nrobust polar soft-SAFT equation of state to predict thermodynamic\nproperties required for their technical evaluation at conditions relevant\nfor cooling applications. Additionally, the thermodynamic model integrated\nwith technical criteria enable the search for compatibility of currently\nused third generation compounds with more eco-friendly refrigerants\nas drop-in replacements. The criteria include volumetric cooling capacity,\ncoefficient of performance, and other physicochemical properties with\ndirect impact on the technical performance of the cooling cycle. As\nsuch, R1123, R1224yd(Z), R1234ze(E), and R1225ye(Z) demonstrate high\naptitude toward replacing R134a, R32, R152a, and R245fa with minimal\n retrofitting to the existing system. The current modeling platform\nfor the rapid screening of emerging refrigerants offers a guide for\nfuture efforts on the design of alternative working fluids.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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 teacher head, 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".