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Record W4290805782 · doi:10.1002/app.53011

Flexible polymeric biomaterials from epoxidized soybean oil, epoxidized oleic acid, and citric acid as both a hardener and acid catalyst

2022· article· en· W4290805782 on OpenAlexafffund
Christine Hood, Saeed M. Ghazani, Alejandro G. Marangoni, Erica Pensini

Bibliographic record

VenueJournal of Applied Polymer Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsCitric acidEpoxidized soybean oilOleic acidDifferential scanning calorimetryMaterials scienceFourier transform infrared spectroscopyNuclear chemistryCatalysisDynamic mechanical analysisGlass transitionEpoxyPolymerChemistryChemical engineeringOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Flexible bio‐based polymeric materials were produced by combining epoxidized soybean oil (ESO), aqueous citric acid solutions, and varying amounts of epoxidized oleic acid (EOA), followed by heating at 95°C for 24 h. Starting materials were analyzed by way of proton nuclear magnetic resonance (H 1 NMR), to confirm the conversion of double bonds in soybean oil or oleic acid to epoxides. Attenuated total reflectance Fourier‐transform infrared spectroscopy (ATR‐FTIR) was used to confirm the reaction of epoxide groups with citric acid and/or EOA. Tensile testing was done to determine the differences in Young's modulus between samples with varying amounts of EOA. Stiffness increased with decreasing EOA content. The stiffest sample (0% EOA) and most elastic sample (30% EOA) had a Young's modulus of 1.43 ± 0.19 MPa and 0.064 ± 0.004 MPa, respectively. Differential scanning calorimetry (DSC) showed that the glass transition temperature was below room temperature for all samples, and decreased with increasing EOA content.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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