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Record W2015457935 · doi:10.1021/ie0009738

Modeling the Ultraviolet Photodegradation of Rigid Polyurethane Foams

2001· article· en· W2015457935 on OpenAlexaboutno aff
Christopher R. Newman, Daniel Forciniti

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolyurethanePhotodegradationPolymerUltravioletMaterials scienceDegradation (telecommunications)WeatheringComposite materialChemical engineeringPenetration (warfare)Ultraviolet lightScanning electron microscopeChemistryCatalysisOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Before the Montreal Protocol of 1987 and the subsequent phasing-out of chlorofluorocarbons (CFCs) in industrial applications, rigid polymer foams were made using these compounds as secondary blowing agents. The CFCs remain trapped in the gaseous part of the cellular foam structure, and once discarded these foams constitute a significant reservoir for the environmental release of ozone-depleting chemicals. Environmental degradation of the foam accelerates this process. Of particular interest in this work is the degradative effect of ultraviolet (UV) light on rigid polyurethane foams. Foams were subjected to accelerated weathering conditions and then viewed with a scanning electron microscope. The thin cell membranes near the foam surface degrade when exposed to UV light, leaving only a network of polymer struts that offers negligible resistance to the escape of CFCs or any other gases contained within. This effect has been reproduced qualitatively through simulated weathering of a computer-generated foam structure. If enough is known about the optical properties and photosensitivity of the polymer foam, this simulation technique can be used to estimate the rate of weathering penetration in any situation where photodegradation is the primary concern.

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)0.001
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 score0.091
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.077
GPT teacher head0.301
Teacher spread0.224 · 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

Citations34
Published2001
Admission routes1
Has abstractyes

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