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Record W2531137624 · doi:10.1021/jacs.6b09205

Mechanism-Guided Development of a Highly Active Bis-thiourea Catalyst for Anion-Abstraction Catalysis

2016· article· en· W2531137624 on OpenAlexfundno aff
C. Rose Kennedy, Dan Lehnherr, Naomi S. Rajapaksa, David D. Ford, Yongho Park, Eric N. Jacobsen

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaDivision of Graduate EducationBoehringer IngelheimEli Lilly and Company
KeywordsChemistryThioureaCatalysisIntramolecular forceRational designEnantioselective synthesisCombinatorial chemistryHydrogen bondHydrogen atom abstractionOrganocatalysisAbstractionOrganic chemistryMoleculeHydrogenNanotechnology

Abstract

fetched live from OpenAlex

We describe the rational design of a linked, bis-thiourea catalyst with enhanced activity relative to monomeric analogues in a representative enantioselective anion-abstraction reaction. Mechanistic insights guide development of this linking strategy to favor substrate activation though the intramolecular cooperation of two thiourea subunits while avoiding nonproductive aggregation. The resulting catalyst platform overcomes many of the practical limitations that have plagued hydrogen-bond-donor catalysis and enables use of catalyst loadings as low as 0.05 mol %. Computational analyses of possible anion-binding modes provide detailed insight into the precise mechanism of anion-abstraction catalysis with this pseudo-dimeric thiourea.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.001
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 head0.265
Teacher spread0.252 · 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

Citations86
Published2016
Admission routes1
Has abstractyes

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