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Record W2104063010 · doi:10.1002/cssc.201500486

Direct Methylation of Amines with Carbon Dioxide and Molecular Hydrogen using Supported Gold Catalysts

2015· article· en· W2104063010 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemSusChem · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
FundersArmy Research OfficeNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsCatalysisCarbon dioxideChemistryHydrogen moleculeHydrogenCarbon fibersInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The N-methylation of amines with CO2 and H2 is an important step in the synthesis of bioactive compounds and chemical intermediates. The first heterogeneous Au catalyst is reported for this methylation reaction with good to excellent yields. The average turnover frequency (TOF) based on surface Au atoms is 45 h(-1) , which is the highest TOF value ever reported for the methylation of aniline with CO2 and H2 . Furthermore, the catalyst is tolerant toward a variety of amines, which includes aromatic, aliphatic, secondary, and primary amines. Preliminary mechanistic studies suggest that the N-alkyl formamide might be an intermediate in the N-methylation of amine process. Moreover, through a one-pot process, it is possible to convert primary amines, aldehydes, and CO2 into unsymmetrical tertiary amines with H2 as a reductant in the presence of the Au catalyst.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.241
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