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Record W2953019186 · doi:10.1021/cen-09713-scicon3

Plastic crystals could be solid-state refrigerants

2019· article· en· W2953019186 on OpenAlexaboutno aff
Sam Lemonick

Bibliographic record

VenueC&EN Global Enterprise · 2019
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantFreonChlorofluorocarbonRefrigerator carPlastic crystalGas compressorOzone layerSolid-stateGreenhouse gasMontreal ProtocolWaste managementEnvironmental scienceProcess engineeringMaterials scienceChemistryPhase (matter)EngineeringMechanical engineeringEngineering physicsOzoneOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Regulators and manufacturers started phasing out chlorofluorocarbon gas refrigerants like Freon in the late 1970s after scientists discovered that they could deplete the ozone layer. But their replacements, hydrochlorofluorocarbons and hydrofluorocarbons, are greenhouse gases. Some scientists and engineers have suggested using solid-state refrigerants to avoid those environmental issues, but their use has yet to be realized. Now researchers propose that so-called plastic crystal materials could be more effective refrigerants than previously studied solid-state materials (Nature 2019, DOI: 10.1038/s41586-019-1042-5). However, experts caution that major engineering challenges stand between these crystals and your fridge. In conventional refrigerators, standard refrigerants change from liquid to gas, absorbing energy from the air inside the refrigerator and cooling it. A compressor then increases the pressure and temperature of the gas, dumping that absorbed energy outside the refrigerator as hot air as the refrigerant returns to the liquid phase. Previously proposed solid-state alternatives could go through similar

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.007

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 head0.238
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2019
Admission routes1
Has abstractyes

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