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Record W1985826163 · doi:10.1002/adsc.201200449

One‐Pot Oxidative Passerini Reaction of Alcohols Using a Magnetically Recyclable TEMPO under Metal‐ and Halogen‐Free Conditions

2013· article· en· W1985826163 on OpenAlexfundno aff
Babak Karimi, Elham Farhangi

Bibliographic record

VenueAdvanced Synthesis & Catalysis · 2013
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsnot available
FundersInstitute for Advanced Studies in Basic SciencesIran National Science FoundationMcGill University
KeywordsChemistryHalogenCatalysisAlcohol oxidationOxidative phosphorylationPrimary (astronomy)DominoOrganic chemistryMetalCombinatorial chemistry

Abstract

fetched live from OpenAlex

Abstract A novel domino oxidative Passerini three‐component reaction (OP‐3CR) has been successfully developed with either primary or secondary alcohols instead of their corresponding aldehydes or ketones by using a recyclable magnetic core‐shell nanoparticle supported TEMPO (2,2,6,6‐tetramethyl‐piperidin‐1‐oxyl) (MNST, 1). It has been shown that MNST is a highly effective and recyclable catalyst system in the metal‐ and halogen‐free aerobic, oxidative, three‐component Passerini reaction. To the best of our knowledge this method can be considered as the first example of a OP‐3CR of alcohols under metal‐ and halogen‐free reaction conditions. It should be also noted that there has been no precedent example of oxidative OP‐3CR of secondary alcohols to their corresponding α‐acyloxy carboxamides. The catalyst can be magnetically recycled and successfully reused in 14 subsequent reaction cycles with only slight decreases of its catalytic activity.

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 score0.001
Threshold uncertainty score0.002

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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

Citations56
Published2013
Admission routes1
Has abstractyes

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