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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
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