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Record W2601527730 · doi:10.3390/ma10030315

Microwave-Assisted Catalytic Synthesis of Bio-Based Copolymers from Waste Cooking Oil

2017· article· en· W2601527730 on OpenAlexafffund
Mahrzadi Noureen Shahi, Muhammad Arshad, Aman Ullah

Bibliographic record

VenueMaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisCopolymerMaterials scienceCooking oilMicrowaveWaste managementChemical engineeringOrganic chemistryBiodieselChemistryComposite materialComputer sciencePolymerEngineering

Abstract

fetched live from OpenAlex

Solvent-free copolymerization of epoxides derived from fatty esters of waste cooking oil with phthalic anhydride using (salen)CrIIICl as catalyst and n-Bu4NCl/DMAP (tetrabutylammonium chloride/4-(dimethylamino)pyridine) as co-catalysts was carried out for the first time under microwave irradiation, where reaction time was reduced from a number of hours to minutes. The polyesters were obtained with molecular weight (Mw = 3100–6750 g/mol) and dispersity values (D = 1.18–1.92) when (salen)CrIIICl/n-Bu4NCl was used as catalysts. Moreover, in the case of DMAP as a co-catalyst, polyesters with improved molecular weight (Mw = 5500–6950 g/mol) and narrow dispersity values (D = 1.07–1.28) were obtained even at reduced concentrations of (salen)CrIIICl and DMAP. The obtained products were characterized and evaluated by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), proton nuclear magnetic resonance (1H-NMR) spectroscopy, gel permeation chromatography (GPC), thermogravimetric analysis (TGA) and differential scanning calorimetric (DSC) Techniques.

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 score0.006
Threshold uncertainty score0.753

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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