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Record W4232629971 · doi:10.26434/chemrxiv.7119887

Catalytic Enantioselective Benzylation Directly from Aryl Acetic Acids

2018· preprint· en· W4232629971 on OpenAlexaff
Patrick J. Moon, Zhongyu Wie, Rylan J. Lundgren

Bibliographic record

VenueChemRxiv · 2018
Typepreprint
Languageen
FieldChemistry
TopicAsymmetric Synthesis and Catalysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnantioselective synthesisDecarboxylationElectrophileChemistryNucleophileCarbanionArylAllylic rearrangementCombinatorial chemistryCatalysisReactive intermediateOrganic chemistry

Abstract

fetched live from OpenAlex

The stability and wide availability of carboxylic acids make them valuable reagents in chemical synthesis. Most transition metal catalyzed processes using carboxylic acid substrates are initiated by a decarboxylation event that generates reactive carbanion or radical intermediates. Developing enantioselective methodologies relying on these principles can be challenging, as highly reactive species tend to react indiscriminately without selectivity. Furthermore, anionic or radical intermediates generated from decarboxylation can be incompatible with protic and electrophilic functionality, or groups that undergo trapping with radicals. We demonstrate that metal-catalyzed enantioselective benzylation reactions of allylic electrophiles can occur directly from aryl acetic acids. The reaction proceeds via a pathway in which decarboxylation is the terminal event, occurring after stereoselective carbon–carbon bond formation. The mechanistic features of the process enable enantioselective benzylation without the generation of a highly basic nucleophile. Thus, the process has broad functional group compatibility that would not be possible employing established protocols.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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