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Record W4205986035 · doi:10.1002/anie.202115030

Metal‐Catalyzed Organic Reactions by Resonant Acoustic Mixing**

2022· article· en· W4205986035 on OpenAlexafffund
Lori Gonnet, Cameron B. Lennox, Jean‐Louis Do, Ivani Malvestiti, Stefan G. Koenig, Karthik Nagapudi, Tomislav Friščić

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaGenentechGovernment of Canada
KeywordsCatalysisMixing (physics)MetalMaterials scienceAcousticsChemistryPhysicsOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

We demonstrate catalytic organic synthesis by Resonant Acoustic Mixing (RAM): a mechanochemical methodology that does not require bulk solvent or milling media. Using as model reactions ruthenium-catalyzed ring-closing metathesis and copper-catalyzed sulfonamide-isocyanate coupling, RAM mechanosynthesis is shown to be faster, operationally simpler than conventional ball-milling, while also providing the first example of a mechanochemical strategy for ruthenium-catalyzed ene-yne metathesis. Reactions by RAM are readily and directly scaled-up without any significant changes in reaction conditions, as shown by the straightforward 200-fold scaling-up of the synthesis of the antidiabetic drug Tolbutamide, from hundreds of milligrams directly to 30 grams.

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 score0.007

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations57
Published2022
Admission routes2
Has abstractyes

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