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Reactions of Alkenes: The RajanBabu Synthesis of Pseudopterosin G-J Aglycone Dimethyl Ether

2015· book-chapter· en· W3104481492 on OpenAlexaboutno aff
Douglass F. Taber

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook-chapter
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsDihydroxylationChemistryAlkeneEtherKetoneOsmiumMedicinal chemistryCatalysisOrganic chemistryRuthenium

Abstract

fetched live from OpenAlex

Xiangge Zhou of Sichuan University showed (Tetrahedron Lett. 2011, 52, 318) that even the monosubstituted alkene 1 was smoothly converted to the methyl ether 2 by catalytic FeCl3. Brian C. Goess of Furman University protected (J. Org. Chem. 2011, 76, 4132) the more reactive alkene of 3 as the 9-BBN adduct, allowing selective reduction of the less reactive alkene to give, after reoxidation, the monoreduced 4. Nobukazu Taniguchi of the Fukushima Medical University added (Synlett 2011, 1308) Na p-toluenesulfinate oxidatively to 1 to give the sulfone 5. Krishnacharya G. Akamanchi of the Indian Institute of Chemical Technology, Mumbai oxidized (Synlett 2011, 81) 1 directly to the bromo ketone 6. Osmium is used catalytically both to effect dihydroxylation, to prepare 8, and to mediate oxidative cleavage, as in the conversion of 7 to the dialdehyde 9. Ken-ichi Fujita of AIST Tsukuba devised (Tetrahedron Lett. 2011, 52, 3137) magnetically retrievable osmium nanoparticles that can be reused repeatedly for the dihydroxylation. B. Moon Kim of Seoul National University established (Tetrahedron Lett. 2011, 52, 1363) an extraction scheme that allowed the catalytic Os to be reused repeatedly for the oxidative cleavage. Maurizio Taddei of the Università di Siena showed (Synlett 2011, 199) that aqueous formaldehyde could be used in place of Co/H2 (syngas) for the formylation of 1 to 10. Hirohisa Ohmiya and Masaya Sawamura of Hokkaido University prepared (Org. Lett. 2011, 13, 1086) carboxylic acids (not illustrated) from alkenes using CO2. Joseph M. Ready of the University of Texas Southwestern Medical Center selectively arylated (Angew. Chem. Int. Ed. 2011, 50, 2111) the homoallylic alcohol 11 to give 12. Many reactions of alkenes are initiated by hydroboration, then conversion of the resulting alkyl borane. Hiroyuki Kusama of the Tokyo Institute of Technology photolyzed (J. Am. Chem. Soc. 2011, 133, 3716) 14 with 13 to give the ketone 15. William G. Ogilvie of the University of Ottawa added (Synlett 2011, 1113) the 9-BBN adduct from 1 to 16 to give 17. Professors Ohmiya and Sawamura effected (Org. Lett. 2011, 13, 482) a similar conjugate addition, not illustrated, of 9-BBN adducts to α,β-unsaturated acyl imidazoles.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.040
GPT teacher head0.224
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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