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Record W2325882946 · doi:10.1021/ic200663v

Metal-Free Catalytic Hydrogenation of Polar Substrates by Frustrated Lewis Pairs

2011· article· en· W2325882946 on OpenAlexaff
Douglas W. Stephan, Sharonna Greenberg, T.W. Graham, P.A. Chase, Jeff Hastie, Stephen J. Geier, Jeffrey M. Farrell, Christopher C. Brown, Zachariah M. Heiden, Gregory C. Welch, Matthias S. Ullrich

Bibliographic record

VenueInorganic Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicOrganoboron and organosilicon chemistry
Canadian institutionsUniversity of WindsorUniversity of Toronto
Fundersnot available
KeywordsFrustrated Lewis pairChemistryCatalysisAsymmetric hydrogenationMetalLewis acids and basesScope (computer science)HydrogenCombinatorial chemistryCatalytic hydrogenationOrganic chemistryEnantioselective synthesis

Abstract

fetched live from OpenAlex

In 2006, our group reported the first metal-free systems that reversibly activate hydrogen. This finding was extended to the discovery of "frustrated Lewis pair" (FLP) catalysts for hydrogenation. It is this catalysis that is the focal point of this article. The development and applications of such FLP hydrogenation catalysts are reviewed, and some previously unpublished data are reported. The scope of the substrates is expanded. Optimal conditions and functional group tolerance are considered and applied to targets of potential commercial significance. Recent developments in asymmetric FLP hydrogenations are also reviewed. The future of FLP hydrogenation catalysts is considered.

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 score0.002
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.201
Teacher spread0.187 · 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

Citations309
Published2011
Admission routes1
Has abstractyes

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