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Record W2617159552 · doi:10.1021/acs.joc.7b00875

Reaction Progress Kinetics Analysis of 1,3-Disiloxanediols as Hydrogen-Bonding Catalysts

2017· article· en· W2617159552 on OpenAlexaff
Kayla M. Diemoz, Jason E. Hein, Sean O. Wilson, James C. Fettinger, Annaliese K. Franz

Bibliographic record

VenueThe Journal of Organic Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsUniversity of British Columbia
FundersDivision of Chemistry
KeywordsCatalysisChemistrySilanolThioureaKineticsIndole testNitroFriedel–Crafts reactionOrganic chemistryCombinatorial chemistry

Abstract

fetched live from OpenAlex

1,3-Disiloxanediols are effective hydrogen-bonding catalysts that exhibit enhanced activity relative to silanediols and triarylsilanols. The catalytic activity for a series of 1,3-disiloxanediols, including naphthyl-substituted and unsymmetrical siloxanes, has been quantified and compared relative to other silanol and thiourea catalysts using the Friedel Crafts addition of indole to trans-β-nitrostyrene. An in-depth kinetic study using reaction progress kinetic analysis (RPKA) has been performed to probe the catalyst behavior of 1,3-disiloxanediols. The data confirm that the disiloxanediol-catalyzed addition reaction is first order in catalyst over all concentrations studied with no evidence of catalyst self-association. 1,3-Disiloxanediols proved to be robust and recoverable catalysts with no deactivation under reaction conditions. No product inhibition is observed, and competitive binding studies with nitro-containing additives suggest that 1,3-disiloxanediols bind weakly to nitro groups but are strongly activating for catalysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations50
Published2017
Admission routes1
Has abstractyes

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