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Microstructure and blowing agent concentration analysis in accelerated aged polyurethane & polyisocyanurate insulation

2019· article· en· W2982236972 on OpenAlexaff
Jelena Madzarevic, Umberto Berardi

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBlowing agentMaterials sciencePolyurethaneMicrostructureComposite materialPolymerThermal conductivityElongationThermal insulationUltimate tensile strengthLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Some closed cell foam insulation products show an increase in thermal conductivity at low temperatures. This reduction in thermal performance has been attributed to the diffusion of air and blowing agent through the foam and to the condensation of the blowing agent. Aging processes and polymer degradation further increase the thermal conductivity of foams. The initial cell structure plays a role in dictating the thermal performance and changes with foam aging which is rarely investigated. To understand the loss of thermal performance in closed cell foams, a microstructure and chemical characterization of pristine and aged samples was performed in this study. The aging behaviour was analyzed by SEM imaging and by measuring the blowing agent concentration in the foam. Changes in the polymer physical attributes were identified. This study also used gas chromatography and quantified changes in pentane concentration in aged polyisocyanurate foams. Results show that aged foams undergo change in their polymer appearance and cellular elongation. Gas chromatography quantified a decrease in blowing agent in the range of 11-85% for polyisocyanurate foams after aging.

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 score0.198
Threshold uncertainty score0.583

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 head0.016
GPT teacher head0.228
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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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