Novel Reduced GWP Refrigerant Compositions To Replace R-134a in Stationary Air-conditioning and Refrigeration
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) have replaced chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) as non-ozone depleting fluids in many applications, including as refrigerants, solvents, aerosols, and blowing agents for insulating foams. However, some HFCs have relatively high Global Warming Potential (GWP) and are coming under closer scrutiny due to the increasing concern over global climate change. The focus now is on the search for the next generation of environmentally sustainable working fluids with negligible direct environmental impact in terms of both ozone depletion and global warming potential. Development of low-GWP options should be balanced with respect to safety, performance, ease of use, and energy efficiency. Indeed, greenhouse gas emissions come not only from direct emissions but also largely from indirect sources based on energy consumption. It is therefore important that energy efficiency remain a primary consideration when implementing low-GWP solutions, as replacing a high-GWP fluid with a lower GWP, but less efficient option may actually increase greenhouse gas emissions, thereby degrading the overall Life Cycle Climate Performance (LCCP). This paper introduces a novel low GWP refrigerant composition to replace R-134a. Thermodynamic properties as well as theoretical and experimental evaluation of this refrigerant is being discussed. Results are compared to baseline
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".