Combining molecular modelling approaches for a holistic thermophysical characterisation of fluorinated refrigerant blends
Bibliographic record
Abstract
After Montreal Protocol, hydrofluorocarbons (HFCs) appeared to be a permanent solution for replacing previous ozone-depleting substances. However, their utilisation has now progressively decreased following the Kigali Amendment application in 2016 due to their high global warming potential (GWP). Unsaturated HFCs, such as hydrofluoroolefins (HFOs), are considered feasible alternatives due to their high reaction rates and low atmospheric lifetimes, resulting in very low GWP. However, available data on their physicochemical behaviour still needs to be improved, even with the recent increase in the amount of new experimental data for these systems. In this direction, computational tools provide a quick pathway to screen their properties and complete the information obtained from experimental work. In this contribution, two different molecular modelling tools, molecular dynamics (MD) simulations and the soft-SAFT equation of state (EOS), are combined to compute the coexistence densities, vapour pressure, heat capacity, interfacial tension, and dynamic viscosity of several refrigerant blends based on 3rd and 4th generation compounds, in order to provide a thermodynamic analysis of the properties of these mixtures, addressing them for drop-in replacement purposes. Results from MD are compared with REFPROP data and those from soft-SAFT, where the capacities of both modelling methods are addressed. In general, quantitative agreement is achieved using the two approaches, offering a framework to screen these properties for new mixtures.
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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) | 0.000 | 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 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".