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Record W2592333346 · doi:10.1002/cssc.201601824

Remarkably Efficient Microwave‐Assisted Cross‐Metathesis of Lipids under Solvent‐Free Conditions

2017· article· en· W2592333346 on OpenAlexafffund
Aman Ullah, Muhammad Arshad

Bibliographic record

VenueChemSusChem · 2017
Typearticle
Languageen
FieldChemistry
TopicSynthetic Organic Chemistry Methods
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates Bio Solutions
KeywordsCatalysisCanolaRutheniumOlefin metathesisMetathesisChemistryBiomass (ecology)Salt metathesis reactionRenewable energyVegetable oilOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Catalytic transformation of renewable feedstocks into fine chemicals is in high demands and olefin metathesis is a sophisticated tool for biomass conversion. Nevertheless, the large‐scale viability of such processes depends on the conversion efficiency, energy efficiency, catalytic activity, selective conversion into desired products, and environmental footprint of the process. Therefore, conversions of renewables by using simple, swift, and efficient methods are desirable. A microwave‐assisted ethenolysis and alkenolysis (using 1,5‐hexadiene) of canola oil and methyl esters derived from canola oil (COME) and waste/recycled cooking oil (WOME) was carried out by using ruthenium‐based catalytic systems. A systematic study using 1st and 2nd generation Grubbs and Hoveyda–Grubbs catalysts was carried out. Among all ruthenium catalysts, 2nd generation Hoveyda–Grubbs catalyst was found to be highly active in the range of 0.002–0.1 mol % loading. The conversions proved to be rapid providing unprecedented turnover frequencies (TOFs). High TOFs were achieved for ethenolysis of COME (21 450 min −1 ), direct ethenolysis of canola oil (19 110 min −1 ), for WOME (15 840 min −1 ) and for cross‐metathesis of 1,5‐hexadiene with COME (10 920 min −1 ). The ethenolysis of commercial methyl oleate was also performed with a TOF of 8000 min −1 under microwave conditions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.325
Teacher spread0.288 · 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

Citations26
Published2017
Admission routes2
Has abstractyes

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