Environment-Friendly Refrigerants for Sustainable Refrigeration and Air Conditioning: A Review
Bibliographic record
Abstract
Refrigeration and air conditioning systems play a vital role in our modern society, and refrigerants are integral components of these systems. Traditional refrigerants like chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) have caused significant environmental concerns because of their role in ozone depletion and global warming. Consequently, interest has increased in developing and implementing environmentally benign refrigerants possessing minimal global warming potential (GWP) and no ozone depletion potential (ODP). This review explores the emerging field of environment-friendly refrigerants such as natural refrigerants (NH3, CO2, hydrocarbons), hydrofluoroolefins (HFOs), hydrofluorocarbons (HFCs) with ultra-low GWP, hydrofluoroethers (HFEs) and mixtures or blends of these refrigerants. The article also compares their thermophysical, thermodynamic, environmental and safety properties, and their suitability for different applications. The key recommendations encompass the promotion of natural refrigerants, including NH3, CO2, and hydrocarbons, exhibit minimal environmental effects. Additionally, the exploration of HFOs and HFCs with ultra-low GWP and their mixtures as potential substitutes is advised. Transitioning to environment-friendly refrigerants is essential for achieving sustainable refrigeration and air conditioning systems, mitigating climate change, and ensuring the long-term viability of cooling technologies while preserving the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".