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Record W7014966012

Properties of Polyol Esters for Low GWP Refrigerants

2021· article· en· W7014966012 on OpenAlexaboutno aff

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2021
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationGlobal-warming potentialDissolutionLubricityBase oilUltra-low-sulfur dieselViscosity
DOInot available

Abstract

fetched live from OpenAlex

HFC (Hydrofluorocarbon) refrigerants, whose ODP (Ozone Depletion Potential) is 0, are used in refrigeration and air conditioning equipments recently. It is known that HFCs are high GWP (Global Warming Potential) refrigerants. In October 2016, Kigali Amendment to the Montreal Protocol was stated to achieve the phase-down of HFC refrigerants to prevent global warming. Transition of current HFCs to low GWP refrigerants would be accelerated around the world. HFO (Hydrofluoroolefin) refrigerants such as R1234yf, R1234ze etc. are low GWP refrigerants. Refrigerant manufacturers are introducing not only HFO itself, but also mixture of HFC and HFO. HFO is known to be less chemically stable compared to HFC since there is double bond in its molecular structure. Also, HFOs tend to dissolve well with refrigeration oil compared to HFCs. As a result, amount of refrigerant dissolving in the oil increase and the kinematic viscosity of the oil decrease. We investigated ways to solve problems which may occur by using HFOs. In particular, it is important to maintain the stability and lubricity of oil. To improve stability, we optimized the amount of the stabilizer. To improve lubricity, since the kinematic viscosity of the oil decrease because the refrigerant dissolve in the oil more than necessary, we applied original base oil formulation to prevent the refrigerant from dissolving too much to the oil. In the presentation, properties of POE refrigeration oils which solve problems of low GWP refrigerants by investigating appropriate base oil and additive formulation are introduced.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.821

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 communication0.0000.000
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 head0.009
GPT teacher head0.154
Teacher spread0.144 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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