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Record W2802353135 · doi:10.1002/app.46616

Polyurethane foams from chlorinated and non‐chlorinated metathesis modified canola oil polyols

2018· article· en· W2802353135 on OpenAlexafffund
Prasanth K.S. Pillai, Shaojun Li, Laziz Bouzidi, Suresh S. Narine

Bibliographic record

VenueJournal of Applied Polymer Science · 2018
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsTrent University
FundersOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsPolyurethanePolyolTrimerMaterials sciencePolymer chemistryChemical engineeringDimerOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

ABSTRACT The product of 1‐butene metathesis of canola triacylglycerol (CMTAG), with shortened structures, terminal double bonds (50% of the total), and oligomers (40% dimer and trimer, and 10% higher oligomers) was used to synthesize novel polyols and polyurethane foams. A non‐chlorinated (Pol‐1) and a chlorinated polyol (Pol‐2) having OH value (170 and 190 mg KOH/g, respectively) were synthesized from CMTAG by epoxidation followed by hydroxylation, and epoxidation followed by hydrogenation, respectively. Both polyols remained liquid below ambient temperature and demonstrated physical characteristics such as viscosity which allowed for the facile preparation of polyurethane foams. The foam obtained with Pol‐1 was relatively soft (∼0.32 MPa at 10% strain) and very flexible (recovery ∼90%); whereas, the foam obtained with Pol‐2 was semi‐rigid (∼1.1 MPa at 10% strain and recovery of 64%). The higher strength and rigidity of Pol‐2 foam compared to Pol‐1 foam is chiefly attributable to the effect of the bulky chlorines on the crosslink density. Importantly, this work highlights that one can improve and control jointly the mechanical properties and deformation recovery ability of bio‐based foams by combining primary functional groups, oligomers, and high molar volume molecules in the polyols. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135 , 46616.

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 categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.260
Teacher spread0.245 · 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.

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

Citations16
Published2018
Admission routes2
Has abstractyes

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