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Record W4408026619 · doi:10.3390/polym17050643

Functionalization of Phenolic Aldehydes for the Preparation of Sustainable Polyesters and Polyurethanes

2025· article· en· W4408026619 on OpenAlexafffund
Rachele N. Carafa, Clara Repiquet, S. K. Rana, Daniel A. Foucher, Guerino Sacripante

Bibliographic record

VenuePolymers · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityRyerson University
KeywordsPolyesterSurface modificationPolymer scienceMaterials sciencePolyurethaneOrganic chemistryPolymer chemistryChemistryChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Biobased organic diols derived from the phenolic aldehyde by-products in the depolymerization of lignin (4-hydroxybenzaldehyde, vanillin, and syringaldehyde) for the synthesis of polyesters and polyurethanes is described. Methods to prepare lignin-based diols involved a two-step synthetic route using either a hydroxy alkylation and aldehyde reduction or an aldehyde reduction and Williamson-Ether substitution. The preparation of five polyesters (PEs) and ten polyurethanes (PUs) from lignin-based diols was also performed and their physical and thermal properties were analyzed. DSC analysis confirmed the amorphous nature of all synthesized polymers, and GPC analysis revealed broad dispersities and high molecular weights. Two PE polyols were also derived from a vanillin-based diol at concentrations of 10 and 25 wt% for their usage in sustainable PU foams. PU foams were prepared from these polyols, where it was found that only the foam containing the 10 wt% formulation was suitable for mechanical testing. The PU foam samples were found to have good hardness and tensile strengths compared to both control foams, showing potential for the incorporation of biobased polyols for PU foam formation.

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 score0.066
Threshold uncertainty score0.206

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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