MétaCan
Menu
Back to cohort
Record W4412855844 · doi:10.1021/acs.organomet.5c00094

Selective Carbonyl Reduction in Unsaturated Esters and Aldehydes by Transfer Hydrogenation

2025· article· en· W4412855844 on OpenAlexaff
Victor Martínez‐Agramunt, Lucas H. R. Passos, Dmitry G. Gusev, Eduardo Peris, Eduardo N. dos Santos

Bibliographic record

VenueOrganometallics · 2025
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsWilfrid Laurier University
FundersNextGenerationEUFundação de Amparo à Pesquisa do Estado de Minas GeraisUniversitat Jaume IConselho Nacional de Desenvolvimento Científico e TecnológicoMinisterio de Ciencia, Innovación y Universidades
KeywordsChemistryTransfer hydrogenationRutheniumCatalysisIsomerizationOsmiumSelectivitySelective reductionOrganic chemistryIsoindolineDouble bondHydrideNoyori asymmetric hydrogenationCinnamaldehydeSolventHydrogen

Abstract

fetched live from OpenAlex

Catalytic transfer hydrogenation (TH) is an alternative to the industrially relevant hydrogenation of carbonyl compounds, dismissing the use of pressurized reactors. Herein, we compare ruthenium and osmium pincer complexes as catalysts for the selective carbonyl reduction of the renewable methyl 10-undecenoate, myrtenal, and cinnamaldehyde. Their selective carbonyl reduction is challenging because they also have a C-C double bond susceptible to reduction or isomerization. The osmium complexes, used for the first time in TH, showed considerably better activity and selectivity than the ruthenium ones. The reactions were carried out at temperatures as low as 35 °C at short reaction times, and a solvent screening demonstrated that anisole, which has a high sustainability score, is also the most efficient solvent for these reactions. Finally, renewable ethanol was employed as a sacrificial hydrogen source, circumventing the use of the usually high-carbon-footprint dihydrogen.

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueOrganometallicsSame topicAsymmetric Hydrogenation and CatalysisFrench-language works237,207