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Record W2787496553 · doi:10.1021/acsomega.7b01962

Synthesis and Characterization of Biguanide and Biguanidium Surfactants for Efficient and Recyclable Application in the Suzuki–Miyaura Reaction

2018· article· en· W2787496553 on OpenAlexafffund
Solène Fortun, Andreea R. Schmitzer

Bibliographic record

VenueACS Omega · 2018
Typearticle
Languageen
FieldChemistry
TopicCatalytic Cross-Coupling Reactions
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaUniversité de Montréal
KeywordsReactivity (psychology)Pulmonary surfactantMicelleBiguanideCoupling reactionCritical micelle concentrationCatalysisChemistryTransmission electron microscopySurface tensionCharacterization (materials science)Chemical engineeringNuclear chemistryOrganic chemistryInorganic chemistryCombinatorial chemistryMaterials scienceNanotechnologyAqueous solution

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide We report here the synthesis and thorough characterization of a new family of alkylbiguanides and alkylbiguanidium chlorides by 1 H and 13 C NMR and X-ray diffraction. Their critical micelle concentration was first determined by surface tension measurements. Hexylbiguanide was then studied as a surfactant in the micellar Suzuki–Miyaura cross-coupling reaction. The unexpected low reactivity of the system at high Pd/hexylbiguanide ratios was due to the change of the size and the shape of the aggregates, observed by transmission electron microscopy. The best catalytic activity was obtained for a 1:1 Pd/hexylbiguanide ratio for which the micellar conditions were conserved. Better results were obtained for several substrates, when compared to those previously obtained with metformin under the same reaction conditions. Higher yields and a better recyclability were obtained under micellar conditions with hexylbiguanide.

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 score0.054
Threshold uncertainty score0.357

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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