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Record W4414779288 · doi:10.1680/jener.25.00050

Assessing sustainable energy options: R466A vs. R410A for air conditioning and refrigeration

2025· article· en· W4414779288 on OpenAlexaboutno aff
Sanjeev Singh, Pardeep Kumar, Vipin Sharma, Dinesh Kumar, Rajeev Kukreja, Vikas Kaushik

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Energy · 2025
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsChillerRefrigerationAir conditioningMontreal ProtocolRefrigerantGlobal-warming potential

Abstract

fetched live from OpenAlex

The widespread use of R410A is under mounting pressure to gradually phase out due to its high global warming potential (GWP). The Kigali amendment to the Montreal Protocol mandates developed nations to eliminate R410A from air conditioning systems by the late 2020s, necessitating the adoption of alternatives by the mid-2020s. One such alternative, R466A, has emerged as a promising refrigerant mixture with thermodynamic properties that could make it a suitable replacement for R410A. R466A boasts a remarkable 65% reduction in GWP compared to R410A and is classified as non-flammable (Class A1). Previous evaluations in air-cooled chillers and residential split-system heat pumps showed performance mostly in line with expectations, with slight variations in capacity and efficiency compared to R410A. Additional experiments conducted in residential heat pumps and transport refrigeration units further supported R466A's viability as an alternative. Capacities varied by 5% decrease to 2% increase, while efficiencies ranged from 3% decrease to 4% increase compared to R410A. These findings underscore R466A's suitability for various HVAC&R applications, offering both reduced GWP and non-flammability, facilitating its integration into existing equipment designs or as a substitute for R410A in practical use cases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.218
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - EnergySame topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207