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Record W2413230688 · doi:10.1021/jacs.6b04686

On- and Off-Cycle Catalyst Cooperativity in Anion-Binding Catalysis

2016· article· en· W2413230688 on OpenAlexfundno aff
David D. Ford, Dan Lehnherr, C. Rose Kennedy, Eric N. Jacobsen

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaHarvard UniversityDivision of Graduate EducationEli Lilly and Company
KeywordsChemistryCooperativityCatalysisCatalytic cycleIonCooperative bindingCombinatorial chemistryOrganic chemistryBinding siteBiochemistry

Abstract

fetched live from OpenAlex

Chiral, neutral H-bond donors have found widespread use as catalysts in enantioselective reactions involving ion-pair intermediates. Herein, a systematic mechanistic study of a prototypical anion-binding reaction, the thiourea-catalyzed enantioselective alkylation of α-chloroethers, is detailed. This study reveals that the catalyst resting state is an inactive dimeric aggregate that must dissociate and then reassemble to form a 2:1 catalyst-substrate complex in the rate-determining transition structure. Insight into this mode of catalyst cooperativity sheds light on the practical limitations that have plagued many of the H-bond donor-catalyzed reactions developed to date and suggests design strategies for new, highly efficient catalyst structures.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations98
Published2016
Admission routes1
Has abstractyes

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