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Record W4411354112 · doi:10.3390/molecules30122604

Functionalisation of Lignin-Derived Diols for the Synthesis of Thermoplastic Polyurethanes and Polyester Resins

2025· article· en· W4411354112 on OpenAlexafffund
Rachele N. Carafa, Justin J. S. Kosalka, Daniel A. Foucher, Guerino Sacripante

Bibliographic record

VenueMolecules · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaRyerson University
KeywordsIsoeugenolVanillinChemistryPolyesterOrganic chemistryEthylene carbonateGel permeation chromatographyThermal stabilityDifferential scanning calorimetryThermogravimetric analysisEugenolPolymer chemistrySubstituentGuaiacolPolymer

Abstract

fetched live from OpenAlex

-cresol, catechol, syringol, syringaldehyde, and vanillin) for the synthesis of thermoplastic polyurethanes (PUs) and polyester (PE) resins is herein described. Diols were synthesised from phenolics in a one-step reaction using either glycerol carbonate or ethylene carbonate as a greener, solvent-free synthetic route. Nine of the diols were selected for the synthesis of Pus, and two of the diols were used for the synthesis of PE resins, with their physical and thermal properties characterised. Analysis of the PUs by differential scanning calorimetry (DSC) confirmed their amorphous nature, while thermogravimetric analysis (TGA) suggested improved thermal stability for all PUs with the addition of an alkyl or aldehyde substituent on the benzene ring regardless of the diisocyanate used. However, lower PU thermal stabilities were observed with the use of an aliphatic diisocyanate over an aromatic diisocyanate in the absence of an additional substituent. Analysis of the PEs by DSC also confirmed that the clear resins were all amorphous, and gel permeation chromatography (GPC) revealed significantly higher molecular weights and dispersities when an aliphatic diacid was utilised over an aromatic diacid.

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)0.001
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 score0.181
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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