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Record W2043329493 · doi:10.1021/bm0704018

Physical Properties of Canola Oil Based Polyurethane Networks

2007· article· en· W2043329493 on OpenAlexaff
Xiaohua Kong, Jin Yue, Suresh S. Narine

Bibliographic record

VenueBiomacromolecules · 2007
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsAlberta Ministry of Agriculture and ForestryAgriculture Food and Rural DevelopmentUniversity of Alberta
Fundersnot available
KeywordsCanolaPolyurethanePolymer scienceChemical engineeringChemistryMaterials sciencePolymer chemistryOrganic chemistryFood scienceEngineering

Abstract

fetched live from OpenAlex

A new generation polyol (generation-II) with significantly higher triol content and higher hydroxyl value was synthesized from canola oil by introducing a mild solvent (ethyl acetate) and a more efficient reductive reagent (zinc) to the previous synthetic procedure (Narine, S. S.; Yue, J.; Kong, X. J. Am. Oil Chem. Soc. 2007, 84, 173-179). Polyurethane (PUR) elastomers were prepared by reacting this type of polyol with aliphatic diisocyanates. The physical and thermal properties of the PUR elastomers were studied using dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC) and compared to the elastomers made from the old generation polyol (generation-I). The concentration of elastically active network chains (nue) of the polymer networks was calculated based on rubber elasticity theory. Larger nue and narrower distribution of nue was observed in the case of the PURs prepared from the generation-II polyol. The relatively faster relaxation at higher temperature for this type of PUR elastomer, suggests a tighter cross-linked network structure by reducing the dangling chains effect. With the same OH/NCO molar ratio, the PURs prepared from the generation-II polyol showed higher glass transition temperatures (Tg), higher Young's modulus and tensile strength, and longer elongation at break.

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 score0.008
Threshold uncertainty score0.483

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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

Citations54
Published2007
Admission routes1
Has abstractyes

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